{ "cta_get_started": "Start free trial", "cta_github": "View on GitHub", "cta_agent": "Build with your agent", "cta_agent_copied": "Prompt copied", "cta_agent_hint": "Copy a setup prompt for Claude Code, Codex or Cursor", "nav_features": "Features", "nav_use_cases": "Use Cases", "nav_integrations": "Integrations", "nav_pricing": "Pricing", "nav_dashboard": "Dashboard", "nav_get_started": "Start free trial", "nav_distributed_tracing": "Distributed Tracing", "nav_browser_sessions": "Browser Sessions", "nav_metrics_dashboards": "Metrics & Dashboards", "nav_log_management": "Log Management", "nav_service_catalog": "Service Catalog & Map", "nav_error_tracking": "Error Tracking", "nav_ai_mcp": "AI & MCP Integration", "nav_kubernetes": "Kubernetes Monitoring", "nav_alerts": "Alerting", "nav_ecommerce": "E-Commerce Observability", "nav_microservices": "Microservices Debugging", "nav_api_performance": "API Performance", "nav_vs_datadog": "Maple vs Datadog", "nav_vs_grafana": "Maple vs Grafana", "nav_vs_new_relic": "Maple vs New Relic", "nav_vs_dash0": "Maple vs Dash0", "nav_vs_signoz": "Maple vs SigNoz", "nav_vs_axiom": "Maple vs Axiom", "nav_vs_better_stack": "Maple vs Better Stack", "nav_vs_honeycomb": "Maple vs Honeycomb", "nav_nextjs": "Next.js", "nav_python": "Python", "nav_nodejs": "Node.js", "nav_product": "Product", "nav_compare": "Compare", "nav_desc_distributed_tracing": "Follow every request across services", "nav_desc_browser_sessions": "Replay sessions, jump to the trace", "nav_desc_metrics_dashboards": "Browse every metric, build dashboards on them", "nav_desc_log_management": "Search structured logs, open the trace behind any line", "nav_desc_service_catalog": "Latency, errors and throughput per service, plus a live map", "nav_desc_error_tracking": "Exceptions grouped into issues, linked to the span that threw them", "nav_desc_ai_mcp": "Let Claude or Cursor query your traces, logs and metrics", "nav_desc_kubernetes": "Pods, nodes, and workloads alongside spans", "nav_desc_alerts": "Alert on any signal, routed where you work", "nav_desc_ecommerce": "Trace a failed checkout by its order id", "nav_desc_microservices": "Trace latency across service boundaries", "nav_desc_api_performance": "Find and fix slow endpoints", "nav_desc_nextjs": "Auto-instrument routes, RSC, and middleware", "nav_all_integrations": "All integrations", "nav_desc_all_integrations": "Languages, agents, clouds and alerts", "nav_desc_python": "Auto-instrument Flask, FastAPI and Django", "nav_desc_nodejs": "Auto-instrument Express, Fastify and database clients", "nav_desc_vs_datadog": "Open-source, no per-host billing", "nav_desc_vs_grafana": "One platform, nothing to assemble", "nav_desc_vs_new_relic": "OTel-native, no per-user fees", "nav_desc_vs_dash0": "Open source and self-hostable", "nav_desc_vs_signoz": "Issues, session replay, one local binary", "nav_desc_vs_axiom": "Issues, session replay, self-hosting", "nav_desc_vs_better_stack": "Source code, self-hosting, local mode", "nav_desc_vs_honeycomb": "Session replay, self-hosting, price per GB", "nav_product_footer": "Everything in one platform", "nav_product_footer_cta": "Explore all features \u2192", "hero_title": "Open-source observability.", "hero_title_sub": "Built on OpenTelemetry.", "hero_title_accent": "Your agent reads it too.", "hero_subtitle": "Query your traces, logs, metrics, and session replay in under a second. $39/month includes 100 GB of each signal. No per-host or per-seat fees.", "customers_eyebrow": "Trusted by teams at", "customers_story_cta": "Read the story", "ingest_stats_label": "traces ingested / month", "stat_github": "on GitHub", "sov_eyebrow": "05 · Sovereignty", "sov_title": "Your data, your instrumentation, your bill.", "sov_lede": "OpenTelemetry in, no proprietary agent, and the source on GitHub the whole time. You can leave with everything you brought.", "sov_pillar_1_label": "Open source", "sov_pillar_1_body": "Source on GitHub under FSL-1.1. Each release becomes Apache 2.0 after two years. Read every line, fork it, or self-host on your own boxes if your security review needs that.", "sov_pillar_2_label": "OpenTelemetry native", "sov_pillar_2_body": "OTLP straight in. No proprietary agent. The instrumentation you write today moves to anywhere OTel-compatible tomorrow.", "sov_pillar_3_label": "Honest pricing", "sov_pillar_3_body": "Pay $39/month for 100 GB per signal, then a flat $0.30/GB. No per-host fees. No per-seat fees. The pricing page and your invoice show the same numbers.", "sov_link_source": "Read the source", "sov_link_otel": "Why OpenTelemetry", "sov_link_pricing": "See the calculator", "mcp_eyebrow": "09 \u00b7 Agents", "mcp_title": "The same incident, run by an agent.", "mcp_lede": "Point Claude, Cursor, or any MCP client at Maple's server. The transcript is the exact sequence of tool calls it makes, from a service list to a proposed patch.", "mcp_point_1_title": "Reads the code behind the trace", "mcp_point_1_body": "The agent pulls the source file behind a span, so the fix it proposes cites your actual code.", "mcp_point_2_title": "Writes back", "mcp_point_2_body": "Claim an issue, set its severity, attach a fix. The agent changes state in Maple as well as reading it.", "mcp_point_3_title": "OAuth, no plugin", "mcp_point_3_body": "Point any MCP client at the endpoint and sign in. Open protocol, nothing to install.", "k8s_eyebrow": "06 \u00b7 Kubernetes", "k8s_title": "The pod behind the slow span", "k8s_lede": "Install a Helm chart and cluster metrics start flowing. Opt a namespace in, and a slow request takes you straight to the pod that served it.", "k8s_link_feature": "Kubernetes monitoring \u2192", "k8s_link_helm": "Read the Helm chart", "k8s_col_namespace": "Namespace", "k8s_col_workload": "Workload", "k8s_col_kind": "Kind", "k8s_col_ready": "Ready", "k8s_col_cpu": "CPU", "k8s_col_mem": "Memory", "k8s_col_node": "Node", "k8s_summary_nodes": "Nodes", "k8s_summary_workloads": "Workloads", "k8s_summary_cluster_cpu": "Cluster CPU", "k8s_summary_cluster_mem": "Cluster memory", "analytics_eyebrow": "07 · Web analytics", "analytics_title": "Web analytics from the sessions you already record.", "analytics_lede": "Visitors, page views, referrers, countries, devices. No second script, no second vendor.", "analytics_point_1_title": "One SDK", "analytics_point_1_body": "MapleBrowser.init records the session. Analytics, replay, and the trace all read from it.", "analytics_point_2_title": "Bots excluded by default", "analytics_point_2_body": "Crawlers are filtered out and the excluded count is shown.", "analytics_point_3_title": "Every row is a filter", "analytics_point_3_body": "Click a referrer or a country and the page narrows to it. Every session links to its trace.", "analytics_link_feature": "Session replay →", "analytics_link_docs": "Browser SDK docs", "pe_eyebrow": "08 · Product events", "pe_title": "Funnels on the telemetry you already send.", "pe_lede": "Count signups, checkouts and upgrades from the browser, your backend or a span you already emit. See the step where people stop, on the same dashboards as your latency.", "pe_point_1_title": "Events from your traces", "pe_point_1_body": "Set maple.product_event.name on a span and it becomes an event, in any language. The event keeps a link to the trace that ran it.", "pe_point_2_title": "One person, start to finish", "pe_point_2_body": "An anonymous visit to your site joins the user it signs up as. Funnels count people, not cookies.", "pe_point_3_title": "1M events free every month", "pe_point_3_body": "Then $5 per million. Automatic page views are never counted, and every event is kept for 365 days.", "pe_link_feature": "Product events →", "pe_link_docs": "Product events docs", "nav_product_events": "Product Events", "nav_desc_product_events": "Funnels, drop-off and paths on your events", "feat_pe_seo_title": "Product Analytics and Funnels | Maple", "feat_pe_seo_desc": "Track product events from the browser, your backend or any OpenTelemetry span. Build funnels, drop-off and paths next to your traces and metrics. 1M events a month included, then $5 per million.", "feat_pe_hero_title": "Product analytics, next to the trace", "feat_pe_hero_lede": "Page views arrive on their own. Track the rest from the browser, your backend or a span you already emit, and see where people stop on the same dashboards as your latency.", "feat_pe_artifact_title": "Find the step where people stop", "feat_pe_artifact_lede": "Each step counts the people who reached it inside the conversion window. Switch to drop-off to see who left, how long converting took, and where they went instead.", "feat_pe_cap_title": "From one event to a conversion rate", "feat_pe_cap_1_title": "Page views without code", "feat_pe_cap_1_body": "The browser SDK records a page view on every navigation. Your first funnel works before you write a single track call.", "feat_pe_cap_2_title": "Events from spans", "feat_pe_cap_2_body": "Tag a span you already emit and it becomes an event, with the span's attributes as properties. Works with any OpenTelemetry SDK, and the event links back to its trace.", "feat_pe_cap_3_title": "Anonymous to signed in", "feat_pe_cap_3_body": "Pass your user id to identify() and earlier anonymous visits join it, across your subdomains. A server event with the same user id lands on the same person.", "feat_pe_cap_4_title": "Funnels, drop-off and paths", "feat_pe_cap_4_body": "Up to 10 steps, a conversion window, and a breakdown by referrer, UTM, country or any property. Paths show what people did before or after any event.", "feat_pe_cap_5_title": "Charts and alerts", "feat_pe_cap_5_body": "Product events are a query source like traces, logs and metrics. Chart signups next to error rate, or alert when they drop.", "feat_pe_cap_6_title": "Ask your agent", "feat_pe_cap_6_body": "The MCP server lists your events and runs funnels, so an agent can tell you where signups stall without leaving the editor.", "pe_src_eyebrow": "Three ways in", "pe_src_title": "Send events from wherever they happen", "pe_src_lede": "All three land in one place with the same ids, so a funnel can start in the browser and finish in a webhook handler.", "pe_src_browser_title": "From the browser", "pe_src_browser_body": "One call next to the click. The event shows up in the session replay beside the requests around it.", "pe_src_span_title": "From a span", "pe_src_span_body": "Two attributes on a span you already emit. No second SDK on the server.", "pe_src_server_title": "From any backend", "pe_src_server_body": "NDJSON over HTTP with your ingest key. For mobile apps, cron jobs and webhook handlers.", "pe_src_docs_link": "Read the docs →", "bill_eyebrow": "10 \u00b7 The bill", "bill_title": "The bill, line by line.", "bill_lede": "Each vendor's list price next to Maple's, for the same usage.", "bill_col_metric": "Line item", "bill_row_per_host": "Per-host fee", "bill_row_per_seat": "Per-seat fee", "bill_row_ingest": "Ingest pricing", "bill_row_retention": "Default retention", "bill_row_otel": "OpenTelemetry support", "bill_row_oss": "License", "bill_row_selfhost": "Self-host option", "bill_row_mcp": "MCP / agent surface", "bill_v_none": "None", "bill_v_usage": "$0.30 / GB per signal", "bill_v_native": "Native", "bill_v_yes": "Yes", "bill_v_apache": "FSL-1.1 \u2192 Apache 2.0", "bill_v_proprietary": "Proprietary", "bill_v_supported": "Supported", "bill_v_no": "No", "bill_v_oss_only": "OSS components", "bill_footnote": "Competitor cells summarize public list prices, checked September 2026. Check each vendor's pricing page for current numbers. Maple's rate is the published per-GB rate, billed per signal. Use the calculator to project your monthly cost.", "bill_link_calculator": "Pricing calculator \u2192", "bill_link_vs_dd": "Maple vs Datadog", "bill_link_vs_gf": "Maple vs Grafana Cloud", "bill_link_vs_nr": "Maple vs New Relic", "bill_link_vs_dash0": "Maple vs Dash0", "bill_link_vs_signoz": "Maple vs SigNoz", "bill_link_vs_axiom": "Maple vs Axiom", "bill_link_vs_better_stack": "Maple vs Better Stack", "bill_link_vs_honeycomb": "Maple vs Honeycomb", "cta_bookend_title": "Try it on your own traces.", "cta_bookend_lede": "14-day free trial, card required, no charge until it ends. Or self-host, or run {cmd} locally with no account at all.", "cta_bookend_primary": "Start free trial", "cta_bookend_secondary": "Read the source", "footer_manifesto": "No per-seat fees. No proprietary agents. No black boxes.", "trustbar_text": "Works with every language OpenTelemetry supports", "how_heading": "Three steps to observability", "how_step1_title": "Instrument", "how_step1_desc": "Add the OpenTelemetry SDK to your app. Auto-instrumentation handles the rest.", "how_step2_title": "Send", "how_step2_desc": "Point the OTLP exporter at Maple. Traces, logs, and metrics flow automatically.", "how_step3_title": "Visualize", "how_step3_desc": "Open the dashboard and start exploring. Everything is queryable, filterable, and fast.", "bottomcta_heading": "See your first trace today.", "bottomcta_subtitle": "Add the SDK, point your OTLP exporter at Maple, and traces arrive.", "bottomcta_tagline": "maple.dev: observability on OpenTelemetry", "footer_tagline": "Open-source observability platform built on OpenTelemetry.", "footer_product": "Product", "footer_features": "Features", "footer_how_it_works": "How it works", "footer_pricing": "Pricing", "footer_opentelemetry": "OpenTelemetry", "footer_roadmap": "Roadmap", "footer_compare": "Compare", "footer_vs_datadog": "vs Datadog", "footer_vs_grafana": "vs Grafana", "footer_vs_new_relic": "vs New Relic", "footer_vs_dash0": "vs Dash0", "footer_vs_signoz": "vs SigNoz", "footer_vs_axiom": "vs Axiom", "footer_vs_better_stack": "vs Better Stack", "footer_vs_honeycomb": "vs Honeycomb", "footer_integrations": "Integrations", "footer_community": "Community", "footer_github": "GitHub", "footer_twitter": "Twitter / X", "footer_discord": "Discord", "footer_copyright": "\u00a9 2026 Makisuo, Inc.", "footer_privacy": "Privacy Policy", "footer_terms": "Terms of Service", "footer_brand": "Brand", "footer_about": "About", "footer_contact": "Contact", "pricing_30day_retention": "30 days of traces and logs, 90 of metrics", "pricing_unlimited_dashboards": "Unlimited dashboards", "pricing_advanced_alerting": "Advanced alerting", "pricing_full_api": "Full API access", "pricing_mcp_server": "MCP server", "pricing_ai_triage": "AI error triaging", "pricing_private_channel": "Private support channel", "pricing_trial_badge": "{duration}-day trial", "pricing_enterprise": "Enterprise", "pricing_enterprise_price": "Custom", "pricing_talk_to_founder": "Talk to the founder", "pricing_logs": "Logs", "pricing_traces": "Traces", "pricing_metrics": "Metrics", "pricing_product_events": "Product Events", "pricing_ai_usage": "AI usage", "pricing_rate_ai_usage": "then billed per model, by tokens used", "pricing_ai_usage_included": "included / month", "pricing_start_trial": "Start {duration}-day free trial", "pricing_everything_included": "Everything is included.", "pricing_zero_host": "per host", "pricing_zero_seat": "per seat", "pricing_zero_query": "per query", "pricing_included_monthly": "Included every month", "pricing_rate_gb": "then {rate} / GB", "pricing_rate_session": "then {rate} / session", "pricing_rate_events": "then {rate} / {block} events", "pricing_unlimited": "Unlimited", "pricing_free_beta": "free during beta", "pricing_trial_reassure": "Free for {duration} days · Cancel anytime · Card required to start", "pricing_enterprise_rail": "Higher volume, custom retention, priority support.", "pricing_estimate_link": "Estimate your bill →", "pricing_calc_label": "Compare", "pricing_calc_heading": "The same volume, priced by another vendor", "pricing_calc_sub": "Slide the vendor's own sliders to a matching volume and compare their published list price with what Maple bills for the same data.", "faq_badge": "FAQ", "faq_heading": "Common questions", "faq_limits_q": "What happens if I exceed my plan limits?", "faq_limits_a": "Usage alerts tell you as you approach your included volume. On the Startup plan, additional usage is billed at $0.30 per GB, per signal, the rate shown on this page. If you would rather not go over, set an overage cap per signal and ingest for that signal is rejected at the cap.", "faq_retention_q": "How long is data retained?", "faq_retention_a": "On the Startup plan, traces, logs and session replays are kept 30 days, metrics 90 days, and service charts (request rate, errors, latency and the service map) 12 months. Enterprise plans have custom retention periods.", "faq_otel_q": "Is Maple compatible with OpenTelemetry?", "faq_otel_a": "Yes. Maple ingests OpenTelemetry directly. Any application instrumented with an OTel SDK or the OTel Collector can send data to Maple without code changes. No proprietary agent is required.", "faq_selfhost_q": "Can I self-host Maple?", "faq_selfhost_a": "Yes. Run Maple on your own infrastructure against a ClickHouse you operate, using the Docker Compose setup in the repository. Maple Cloud is there for teams that would rather not operate it.", "faq_trial_q": "Is there a free trial?", "faq_trial_a": "The Startup plan starts with a 14-day free trial. A credit card is required, and you are not charged until the trial ends. Enterprise plans start with a conversation.", "page_home_title": "Open Source Observability for Traces, Logs & Metrics | Maple", "page_home_desc": "Open-source observability built on OpenTelemetry. Traces, logs, metrics and session replay in one place, with an MCP server your AI agent can query.", "page_pricing_title": "Pricing | Maple", "page_pricing_desc": "One plan: $39/month with 100 GB of logs, traces and metrics each, then a flat $0.30/GB. No per-seat, per-host or per-query fees. Estimate your bill in seconds.", "page_pricing_label": "Pricing", "page_otel_title": "OpenTelemetry observability platform | Maple", "page_otel_desc": "Maple is built on OpenTelemetry. Send traces, logs and metrics over OTLP from any OpenTelemetry SDK or the Collector. No proprietary agent.", "page_otel_label": "OpenTelemetry", "page_otel_heading": "Maple runs on OpenTelemetry", "page_otel_subtitle": "Traces, logs and metrics all arrive over OTLP. There is no Maple agent to install and no Maple SDK to depend on.", "page_otel_standard": "The standard", "page_otel_what_is": "What is OpenTelemetry?", "page_otel_what_is_p1": "OpenTelemetry is the CNCF's open standard for collecting telemetry. It defines the APIs, SDKs and wire protocol (OTLP) that applications use to produce traces, logs and metrics, independent of any vendor.", "page_otel_what_is_p2": "It has SDKs for every major language and contributions from hundreds of companies. Most new instrumentation is written against it.", "page_otel_approach": "Our approach", "page_otel_why_heading": "Why we build on OpenTelemetry", "page_otel_no_vendor": "Switch backends without code changes", "page_otel_no_vendor_desc": "Your instrumentation doesn't depend on Maple. Sending it somewhere else is a config change.", "page_otel_one_standard": "One standard, all signals", "page_otel_one_standard_desc": "Traces, logs and metrics share one data model and carry the same trace context, so they link up without glue code.", "page_otel_community": "Run by the community", "page_otel_community_desc": "Backed by the CNCF and hundreds of contributors. The spec is governed by the community, and no single vendor sets its roadmap.", "page_otel_language": "Broad language support", "page_otel_language_desc": "Official SDKs for Go, Java, Python, JavaScript, .NET, Rust, and more. Auto-instrumentation is available for most frameworks.", "page_otel_future": "Instrument once", "page_otel_future_desc": "Instrumentation you write today keeps working with whatever backend you use next.", "page_otel_ecosystem_title": "Collector pipelines", "page_otel_ecosystem_desc": "Receivers, processors and exporters let you filter, sample and route telemetry before it reaches any backend.", "page_otel_spec": "Spec compliance", "page_otel_spec_heading": "How Maple follows the spec", "page_otel_spec_otlp": "Native OTLP ingest", "page_otel_spec_otlp_desc": "Send telemetry over OTLP/HTTP as protobuf or JSON. There is no Maple-specific format.", "page_otel_spec_semantic": "Semantic conventions", "page_otel_spec_semantic_desc": "Maple reads the OTel semantic conventions for HTTP, database, RPC, and messaging spans and builds its views from them.", "page_otel_spec_signals": "All three signals", "page_otel_spec_signals_desc": "Traces, logs, and metrics are each stored, queried, and correlated with the others.", "page_otel_spec_collector": "Collector compatible", "page_otel_spec_collector_desc": "Use the OpenTelemetry Collector to route, filter, and batch telemetry before sending it to Maple.", "page_otel_spec_sdk": "Any OTel SDK", "page_otel_spec_sdk_desc": "Works with every official OTel SDK. If it sends OTLP, Maple accepts it.", "page_otel_spec_context": "Context propagation", "page_otel_spec_context_desc": "W3C Trace Context is supported. Spans and logs from different services correlate by trace ID.", "page_otel_ecosystem": "Ecosystem", "page_otel_ecosystem_heading": "Works with your existing OTel setup", "page_otel_ecosystem_p1": "Already using the OpenTelemetry Collector, auto-instrumentation agents, or OTel SDKs? Point your OTLP exporter at Maple with an ingest key. Nothing else in your pipeline changes.", "page_otel_ecosystem_p2": "Maple is the backend at the end of that pipeline. It stores and queries what your pipeline already produces.", "page_roadmap_title": "Roadmap | Maple", "page_roadmap_desc": "What Maple has shipped, what is in progress, and what comes next.", "page_roadmap_label": "Roadmap", "page_roadmap_heading": "What we're building", "page_roadmap_subtitle": "What's shipped, what's in progress, and what comes next.", "page_k8s_title": "Kubernetes Monitoring | Maple", "page_k8s_desc": "Monitor Kubernetes clusters with OpenTelemetry. A Helm chart collects kubelet, host, and cluster-state metrics. Spans from instrumented pods arrive with pod, node, and namespace attached.", "page_k8s_heading": "Your cluster and your traces, together", "page_k8s_subtitle": "Install one Helm chart. Cluster metrics flow in, and spans from pods in opted-in namespaces arrive tagged with their pod, node and namespace.", "page_k8s_helm": "One Helm chart", "page_k8s_helm_desc": "A small chart you can read in full. Collects pod, node and cluster metrics, and receives OTLP from your apps.", "page_k8s_correlation": "Pods linked to spans", "page_k8s_correlation_desc": "Go from a slow trace to the pod and node that ran it.", "page_k8s_kube_state": "Cluster-state metrics", "page_k8s_kube_state_desc": "Deployments, replicas and pod status, collected by the chart. No separate kube-state-metrics needed.", "page_k8s_views": "Workloads, pods, nodes and services", "page_k8s_views_desc": "Four views that share the same filters, from a whole deployment down to one pod.", "page_k8s_operator": "OpenTelemetry Operator", "page_k8s_operator_desc": "Pods in an annotated namespace are configured automatically. Apps with an OTel SDK need no code changes.", "page_k8s_multi": "Multi-cluster ingest", "page_k8s_multi_desc": "One endpoint for prod, staging and local clusters. Tell them apart by cluster name.", "page_k8s_console_eyebrow": "Cluster console", "page_k8s_console_title": "Workloads, pods and nodes, one filter panel", "page_k8s_console_lede": "Filter down to one deployment, namespace or node, or watch a whole cluster live.", "page_k8s_breadcrumb": "infra › kubernetes › workloads", "page_k8s_filter_heading": "Filters", "page_k8s_filter_deployment": "Deployment", "page_k8s_filter_namespace": "Namespace", "page_k8s_filter_cluster": "Cluster", "page_k8s_filter_compute": "Compute Type", "page_k8s_tab_deployment": "Deployment", "page_k8s_tab_statefulset": "StatefulSet", "page_k8s_tab_daemonset": "DaemonSet", "page_k8s_time_last_12h": "Last 12 hours", "page_k8s_reload": "Reload", "page_k8s_live": "Live", "page_k8s_workloads_h3": "Workloads", "page_k8s_workloads_subtitle": "Aggregated pod metrics by Deployment, StatefulSet, and DaemonSet.", "page_k8s_views_eyebrow": "Pods, nodes, workloads", "page_k8s_views_title": "Pods, nodes, and workloads", "page_k8s_views_lede": "Same filters everywhere. Click from a workload to its pods to the node running them.", "page_k8s_views_pods_route": "/infra/kubernetes/pods", "page_k8s_views_nodes_route": "/infra/kubernetes/nodes", "page_k8s_views_workloads_route": "/infra/kubernetes/workloads", "page_k8s_views_pods_caption": "Pods", "page_k8s_views_pods_lede": "Per-pod CPU and memory against requests and limits.", "page_k8s_views_nodes_caption": "Nodes", "page_k8s_views_nodes_lede": "Kubelet stats per node, with uptime and lifecycle.", "page_k8s_views_workloads_caption": "Workloads", "page_k8s_views_workloads_lede": "Aggregated by Deployment, StatefulSet, DaemonSet.", "page_k8s_correlation_eyebrow": "Span → pod", "page_k8s_correlation_title": "From a slow span to the exact pod", "page_k8s_correlation_lede": "In opted-in namespaces, every span is tagged with its pod, node and namespace, so a slow trace already knows where it ran.", "page_k8s_correlation_trace_label": "Trace: 7af1c204", "page_k8s_correlation_waterfall": "Waterfall", "page_k8s_correlation_pod_label": "Pod attribution", "page_k8s_correlation_arrow_caption": "k8s.pod.name injected at admission", "page_k8s_correlation_pod_status": "Active", "page_k8s_correlation_attr_pod": "k8s.pod.name", "page_k8s_correlation_attr_node": "k8s.node.name", "page_k8s_correlation_attr_ns": "k8s.namespace.name", "page_k8s_install_eyebrow": "One Helm chart", "page_k8s_install_title": "Install in three commands", "page_k8s_install_lede": "Create the secret, install the chart, and metrics start arriving.", "page_k8s_install_window": "helm install maple-k8s-infra", "page_k8s_install_step_1": "Create the ingest-key secret", "page_k8s_install_step_2": "Install the chart", "page_k8s_install_step_3": "Watch the rollout", "page_k8s_install_copy": "Copy", "page_nextjs_title": "Next.js Observability with OpenTelemetry | Maple", "page_nextjs_desc": "Add distributed tracing and logging to a Next.js app with OpenTelemetry and the built-in instrumentation hook, then send it to Maple over OTLP.", "page_nextjs_label": "Next.js", "page_nextjs_heading": "Next.js observability with OpenTelemetry", "page_nextjs_subtitle": "Next.js has an instrumentation hook built in. Register OpenTelemetry there, and server components, API routes and middleware produce spans with no further code.", "page_nextjs_what_you_get": "What you get", "page_nextjs_auto_heading": "Automatic instrumentation for Next.js", "page_nextjs_trace_preview": "Live trace preview", "page_nextjs_trace_heading": "See your Next.js traces", "breadcrumb_home": "Home", "breadcrumb_features": "Features", "breadcrumb_compare": "Compare", "breadcrumb_integrations": "Integrations", "breadcrumb_use_cases": "Use Cases", "language_en": "English", "language_ja": "\u65e5\u672c\u8a9e", "language_ko": "\ud55c\uad6d\uc5b4", "faq_home_what_q": "What is Maple?", "faq_home_what_a": "Maple is an observability platform for traces, logs, metrics and session replay, built on OpenTelemetry and backed by ClickHouse. It lets you collect, visualize, and query telemetry from distributed systems in real time, or let an AI agent do it for you over MCP.", "faq_home_oss_q": "Is Maple open source?", "faq_home_oss_a": "Maple's source is on GitHub under the Functional Source License (FSL-1.1). You can read every line, fork it, and self-host. Each release becomes Apache 2.0 two years after publication. You can run Maple yourself or use the hosted version.", "faq_home_otel_q": "Is Maple OpenTelemetry-native?", "faq_home_otel_a": "Yes. Maple is built on OpenTelemetry, the open, vendor-neutral standard for telemetry, so there are no proprietary agents. If you already emit OpenTelemetry data, you can point it at Maple without re-instrumenting your code.", "faq_home_price_q": "How is Maple priced?", "faq_home_price_a": "The Startup plan is $39/month with 100 GB of logs, traces, and metrics each included, then a flat $0.30 per GB. No per-host or per-seat fees. Enterprise plans add custom volume and retention.", "faq_home_agents_q": "Does Maple work with AI agents?", "faq_home_agents_a": "Yes. Maple runs an MCP (Model Context Protocol) server, so compatible AI agents can list services, search traces, find errors, and propose fixes directly against your telemetry.", "hero_announce_badge": "New", "hero_announce_text": "Maple Local: one binary, no account.", "signals_eyebrow": "01 · Signals", "signals_trace_title": "Read the trace.", "signals_trace_sub": "Every request as a span tree, attributes intact. Click a span and see what it carried.", "signals_logs_title": "Search the logs.", "signals_logs_sub": "Structured logs straight from OTLP. Filter by severity, service, or any attribute, then jump to the trace.", "signals_session_title": "Replay the session.", "signals_session_sub": "Every click, route, console line, and failed request. Replay and spans share one session id.", "incident_eyebrow": "02 · Incident", "incident_title": "From a 3 AM page to the line that caused it.", "incident_lede": "Four surfaces, one trace id. Every step below opens from the one before it.", "incident_step_1_title": "The alert arrives with context.", "incident_step_1_body": "Error rate on api-gateway crosses 5%. The alert carries the service, the threshold it broke and the spans that failed. Route it to Slack, Microsoft Teams, Google Chat, Discord, Telegram, PagerDuty, incident.io, email, or any webhook.", "incident_step_2_title": "The trace opens on the failing span.", "incident_step_2_body": "The failing request is a span tree with every attribute intact. Three Stripe retries sit at the bottom, red, each timing out at exactly 1.75 seconds.", "incident_step_3_title": "The logs are already correlated.", "incident_step_3_body": "Jump from the failing span to its logs on the trace id. Retry exhaustion, connection pool at 20/20. The confirmation is on the same screen.", "incident_step_4_title": "Or hand it to an agent.", "incident_step_4_body": "Point Claude, Cursor, or any MCP client at Maple. It lists services, searches traces, reads the source, and proposes the fix.", "platform_eyebrow": "04 · Platform", "platform_title": "Everything else you'd have bought separately.", "platform_lede": "Metrics, service maps, error grouping, alerting, and an MCP server, all on the instrumentation you already send.", "platform_metrics_name": "Metrics & dashboards", "platform_metrics_desc": "Request rate, error rate, latency percentiles. Build the board yourself, or ask the agent to build it.", "platform_map_desc": "Live request flow across your services. The cascade you'd otherwise reconstruct from a postmortem, in the foreground.", "platform_errors_name": "Error tracking", "platform_errors_desc": "Errors grouped by type across every service, with trends over time, affected services, and sample traces attached.", "platform_alerts_desc": "Alert on error rate, latency, Apdex, throughput, any query-builder chart or raw SQL. Incidents open and resolve on their own. Routed to Slack, Microsoft Teams, Google Chat, Discord, Telegram, PagerDuty, incident.io, email, or any webhook.", "platform_mcp_name": "AI & MCP", "platform_mcp_desc": "Any MCP-compatible client can list services, search traces, read the source, and propose a fix. Open protocol, no plugin.", "platform_cloud_name": "Cloud & Kubernetes", "platform_cloud_desc": "Kubernetes ships as a Helm chart. PlanetScale, Cloudflare, GitHub, and the rest connect with one OAuth grant.", "sov_pillar_4_label": "Your perimeter", "sov_pillar_4_body": "Run Maple on your own infrastructure, against a ClickHouse you operate and scale yourself. Your telemetry never has to leave your network, and retention and residency are your call.", "local_eyebrow": "03 · Local", "local_title": "Maple on your laptop, as one binary.", "local_body": "One maple executable runs OTLP ingest, an embedded database, the query API and the dashboard on 127.0.0.1. No account and no cloud.", "local_install_alt": "or", "local_link_docs": "Read the docs \u2192", "pricing_home_eyebrow": "11 \u00b7 Pricing", "pricing_home_title": "One plan, billed by the gigabyte.", "pricing_home_lede": "One plan. 100 GB per signal included, then a flat rate. Nothing metered by host, seat, or query.", "nav_login": "Log in", "nav_docs": "Docs", "nav_local": "Maple Local", "nav_guides": "Guides", "nav_blog": "Blog", "nav_customers": "Customers", "nav_changelog": "Changelog", "footer_learn": "Learn", "footer_what_is_apm": "What is APM?", "footer_what_is_observability": "What is observability?", "footer_best_tools": "Best open-source tools", "hero_shot_overview": "Overview", "hero_shot_traces": "Traces", "hero_shots_aria": "Product screenshots", "hero_shot_overview_line": "Health, latency and errors across your system.", "hero_shot_traces_line": "Follow one request end to end.", "page_panel_eyebrow": "The surface", "page_cap_eyebrow": "What it does", "page_artifact_eyebrow": "In motion", "page_gallery_eyebrow": "More screens", "page_gallery_title": "{feature}, screen by screen", "page_story_eyebrow": "The path", "page_outcome_label": "outcome", "page_cross_eyebrow": "Keep going", "page_cross_features_title": "Related features", "page_cross_usecases_title": "The same data, other jobs", "page_cta_title": "Point OTLP at Maple.", "page_cta_lede": "One endpoint, one key. Traces, logs, metrics and sessions, linked from the first request.", "feat_tracing_seo_title": "Distributed Tracing | Maple", "feat_tracing_seo_desc": "Open one trace and read where the time went: waterfall, flamegraph and service flow over the same spans, with every attribute the SDK sent.", "feat_tracing_hero_title": "See where the time went", "feat_tracing_hero_lede": "A trace shows one request from start to finish. Maple keeps every span with its attributes and timing, so you can see exactly what made it slow.", "feat_tracing_panel_title": "One request, 18 spans, one screen", "feat_tracing_panel_lede": "Click a span to see all its attributes, like the SQL it ran and the status it returned.", "feat_tracing_cap_title": "Four views of the same trace", "feat_tracing_cap_1_title": "Waterfall", "feat_tracing_cap_1_body": "Spans nested by parent and scaled to the request's length. You see the slow part before you read a number.", "feat_tracing_cap_2_title": "Flamegraph", "feat_tracing_cap_2_body": "Spans stacked by depth. Fifty db.query calls in a row show up as a wall, so N+1 queries are easy to spot.", "feat_tracing_cap_3_title": "Service flow", "feat_tracing_cap_3_body": "The trace reduced to the services it touched. Use it to find which service the time was spent in.", "feat_tracing_cap_4_title": "Attributes", "feat_tracing_cap_4_body": "Every attribute the SDK sent, including your custom ones, is stored and searchable.", "feat_tracing_cap_5_title": "Jump to logs", "feat_tracing_cap_5_body": "Open any span's logs in one click. They share the same trace id.", "feat_tracing_artifact_title": "The flamegraph from the product", "feat_tracing_artifact_lede": "The same component the product uses. Hover a span to see its service and duration.", "feat_sessions_seo_title": "Browser Sessions | Maple", "feat_sessions_seo_desc": "Watch the session the user actually had, then open the trace behind any moment in it. Replay, console, network and spans share one session id.", "feat_sessions_hero_title": "Watch it happen, then open the trace", "feat_sessions_hero_lede": "The replay shows what the user did. The trace shows why it broke. One click takes you from one to the other.", "feat_sessions_panel_title": "The replay next to its console and network log", "feat_sessions_panel_lede": "Console logs, network requests and errors play back on the same timeline as the user's clicks.", "feat_sessions_cap_title": "What a session includes", "feat_sessions_cap_1_title": "Every event, in order", "feat_sessions_cap_1_body": "Clicks, inputs, console logs, network calls and errors in one timeline, each linked to its trace.", "feat_sessions_cap_2_title": "DOM replay", "feat_sessions_cap_2_body": "The SDK records DOM changes instead of video. Scrolling, hovers and typing play back exactly, at any screen size.", "feat_sessions_cap_3_title": "From a moment to a span", "feat_sessions_cap_3_body": "Pause on a failed click and open the request it sent.", "feat_sessions_cap_4_title": "Console and network, inline", "feat_sessions_cap_4_body": "Every request with its method, status and duration. A failed checkout shows up the moment it happens.", "feat_sessions_cap_5_title": "Masked before it leaves", "feat_sessions_cap_5_body": "Inputs and text are masked in the browser before anything is sent. You control sampling and masking in the SDK.", "feat_sessions_artifact_title": "A session, playing", "feat_sessions_artifact_lede": "The user clicks Pay now, and each event appears alongside with its trace id.", "feat_logs_seo_title": "Log Management | Maple", "feat_logs_seo_desc": "Search structured logs across every service, filter by severity and resource attribute, and open the trace that produced any line.", "feat_logs_hero_title": "Search logs, jump to the trace", "feat_logs_hero_lede": "Logs arrive structured and linked to their trace. Any result opens the request that wrote it.", "feat_logs_panel_title": "Filter by severity, service and attribute", "feat_logs_panel_lede": "Filter counts are computed for the time range you picked.", "feat_logs_cap_title": "What you can do with a log line", "feat_logs_cap_1_title": "Search text and attributes", "feat_logs_cap_1_body": "Filter on service.name, environment or your own attribute keys, the same way you search message text.", "feat_logs_cap_2_title": "Severity at a glance", "feat_logs_cap_2_body": "Each severity level has its own color and filter. WARN and ERROR are one click apart.", "feat_logs_cap_3_title": "Open the trace", "feat_logs_cap_3_body": "Jump from any log line to the span that was running when it was written.", "feat_logs_cap_4_title": "Spot spikes first", "feat_logs_cap_4_body": "A chart above the results, colored by severity, shows an error burst before you read a line.", "feat_logs_cap_5_title": "Standard OpenTelemetry", "feat_logs_cap_5_body": "Logs come in over OTLP with your traces and metrics. No extra agent or pipeline.", "feat_logs_artifact_title": "Live tail", "feat_logs_artifact_lede": "New lines stream in, tagged with service and severity. Same data as search.", "feat_metrics_seo_title": "Metrics & Dashboards | Maple", "feat_metrics_seo_desc": "Browse every metric your services emit, then build dashboards on the same data the trace list reads from, so a chart and a trace can never disagree.", "feat_metrics_hero_title": "Charts and traces that always agree", "feat_metrics_hero_lede": "Dashboards read the same data as your traces. If a chart shows p99 at 380 ms, the traces behind it are one click away.", "feat_metrics_panel_title": "Every metric your services send", "feat_metrics_panel_lede": "See each metric's type, the service that sends it, and when it was last seen.", "feat_metrics_cap_title": "Build a dashboard", "feat_metrics_cap_1_title": "Time series", "feat_metrics_cap_1_body": "p50, p95 and p99 on one chart, in the same colors everywhere in the product.", "feat_metrics_cap_2_title": "Stat and threshold", "feat_metrics_cap_2_body": "One number and the line it should stay under, like error rate against its budget.", "feat_metrics_cap_3_title": "Variables", "feat_metrics_cap_3_body": "Add $service or $env once and every chart follows it. One dashboard instead of twelve.", "feat_metrics_cap_4_title": "Attribute autocomplete", "feat_metrics_cap_4_body": "Group-by suggests only attributes the metric really has, so charts never come up empty.", "feat_metrics_cap_5_title": "Chart to trace", "feat_metrics_cap_5_body": "Select a time range on any latency chart to see the traces inside it.", "feat_metrics_artifact_title": "Log volume by severity", "feat_metrics_artifact_lede": "Five hours in five-minute buckets. A single number would hide the warning spike and the errors after it.", "feat_catalog_seo_title": "Service Catalog & Map | Maple", "feat_catalog_seo_desc": "Every service with its latency percentiles, error rate and throughput, and a live map of who calls whom, both built from spans.", "feat_catalog_hero_title": "A service map that builds itself", "feat_catalog_hero_lede": "Maple builds the map from your traces. There is nothing to configure, and a new service appears the first time something calls it.", "feat_catalog_panel_title": "Every service, one table", "feat_catalog_panel_lede": "Latency, error rate and throughput per service. Sort by p99 to find the one that needs attention.", "feat_catalog_cap_title": "Reading the map", "feat_catalog_cap_1_title": "Built from real calls", "feat_catalog_cap_1_body": "Every line is traffic that actually happened, with its own request and error rate.", "feat_catalog_cap_2_title": "Databases and caches too", "feat_catalog_cap_2_body": "Postgres, Redis and third-party APIs appear on the map next to your services.", "feat_catalog_cap_3_title": "Focus on one service", "feat_catalog_cap_3_body": "Pick a service to see only what it talks to. On a map of forty services, that is usually a handful.", "feat_catalog_cap_4_title": "One color per service", "feat_catalog_cap_4_body": "Each service keeps its color on the map, in traces and in charts.", "feat_catalog_cap_5_title": "Deploys on the chart", "feat_catalog_cap_5_body": "Releases are marked on each service's latency chart, so you can see which version caused a change.", "feat_catalog_artifact_title": "Live traffic", "feat_catalog_artifact_lede": "Each service shows its request rate, error rate and latency. Lines show traffic between them.", "feat_errors_seo_title": "Error Tracking | Maple", "feat_errors_seo_desc": "Exceptions grouped into issues by type and normalized message, each one linked to the span that threw it and the release that introduced it.", "feat_errors_hero_title": "142 errors, one issue", "feat_errors_hero_lede": "Maple groups the same error into one issue and links every occurrence to the span that threw it.", "feat_errors_panel_title": "Errors grouped into issues", "feat_errors_panel_lede": "Each issue shows its type, affected services, count and when it was last seen.", "feat_errors_cap_title": "From issue to fix", "feat_errors_cap_1_title": "Grouping", "feat_errors_cap_1_body": "Errors with the same type, message and stack are one issue. Ids, hosts and emails are ignored when matching.", "feat_errors_cap_2_title": "The span that threw it", "feat_errors_cap_2_body": "Every issue opens the trace it came from, with the query and status code at the time.", "feat_errors_cap_3_title": "New or back again", "feat_errors_cap_3_body": "See when an issue first and last appeared. A resolved issue that comes back reopens.", "feat_errors_cap_4_title": "Owners and notes", "feat_errors_cap_4_body": "Claim an issue, set severity and leave notes, so the next person on call knows what you tried.", "feat_errors_cap_5_title": "Triaged automatically", "feat_errors_cap_5_body": "New errors are grouped every minute and AI suggests a severity. Your choice always overrides it.", "feat_alerts_seo_title": "Alerting | Maple", "feat_alerts_seo_desc": "Threshold alerts on traces, logs and metrics, previewed against your own history before you save, evaluated every minute, and delivered to Slack, Microsoft Teams, Google Chat, Discord, Telegram, PagerDuty, incident.io, email, or any webhook.", "feat_alerts_hero_title": "Know why an alert fired, or why it didn't", "feat_alerts_hero_lede": "Test a rule against your own history before you save it. Once it is live, every evaluation is logged with the value it saw.", "feat_alerts_panel_title": "Preview before you save", "feat_alerts_panel_lede": "The preview uses the same logic as the real alert, so it shows exactly when it would have fired.", "feat_alerts_cap_title": "From threshold to resolution", "feat_alerts_cap_1_title": "Alert on anything", "feat_alerts_cap_1_body": "Error rate, latency, Apdex and throughput built in. Or alert on any query over traces, logs and metrics, or raw SQL.", "feat_alerts_cap_2_title": "Same logic as the live rule", "feat_alerts_cap_2_body": "If the preview says it would have fired twice last Tuesday, it would have.", "feat_alerts_cap_3_title": "One rule, many incidents", "feat_alerts_cap_3_body": "Group by service or any attribute, and each group alerts on its own. One bad service can't hide in an average.", "feat_alerts_cap_4_title": "Every check logged", "feat_alerts_cap_4_body": "Each check records its value and result, including failures. If a rule stays quiet, you can see why.", "feat_alerts_cap_5_title": "Delivery with retries", "feat_alerts_cap_5_body": "Automatic retries, custom templates and reminders while an incident is open. Slack, Microsoft Teams, Google Chat, Discord, Telegram, PagerDuty, incident.io, Linear, email, Hazel or any webhook.", "feat_alerts_artifact_title": "From breach to page in a minute", "feat_alerts_artifact_lede": "Rules run every minute. Two bad checks in a row open an incident and notify you. Two good ones close it.", "feat_mcp_seo_title": "AI & MCP Integration | Maple", "feat_mcp_seo_desc": "Maple ships an MCP server, so Claude Code and any other MCP client can read your traces, logs and metrics and work an incident with the same tools you do.", "feat_mcp_hero_title": "Give your AI agent the same tools you use", "feat_mcp_hero_lede": "Maple's MCP server lets agents query your traces, logs and metrics, using the same queries as the dashboard.", "feat_mcp_panel_title": "One endpoint, any MCP client", "feat_mcp_panel_lede": "Connect Claude Code or any MCP client. It sees the same data you do.", "feat_mcp_cap_title": "What an agent can do", "feat_mcp_cap_1_title": "Check a service", "feat_mcp_cap_1_body": "diagnose_service reports latency, error rate and throughput, and what changed.", "feat_mcp_cap_2_title": "Read the traces", "feat_mcp_cap_2_body": "find_slow_traces and inspect_trace return real spans, so the agent sees the actual queries.", "feat_mcp_cap_3_title": "Read your code", "feat_mcp_cap_3_body": "Link a repository and the agent can go from a stack trace to the exact line.", "feat_mcp_cap_4_title": "Changes need approval", "feat_mcp_cap_4_body": "Actions that change things are separate tools. In Maple's chat, they wait for your approval.", "feat_mcp_cap_5_title": "Limited to your org", "feat_mcp_cap_5_body": "Every query is locked to the org on the key. An agent can't see another team's data.", "feat_mcp_artifact_title": "An agent working an incident", "feat_mcp_artifact_lede": "Five steps from list_services to propose_fix, each one a query you could run yourself.", "feat_k8s_cap_title": "Cluster and application, one pipeline", "uc_api_seo_title": "API Performance Monitoring | Maple", "uc_api_seo_desc": "An SLO breach, traced to the release that caused it. Follow checkout p99 from the alert through the dashboard to the commit that took it from 120 ms to 380 ms.", "uc_api_hero_title": "The p99 moved. Find out which release moved it.", "uc_api_hero_lede": "When an SLO breaches, you want the release that caused it. Here that takes three screens, from the alert to the commit, all on the same time range.", "uc_api_story_title": "Threshold to commit, in three minutes", "uc_api_story_lede": "The SLO is checkout p99 at or under 200 ms. Each screen below is filtered to the window where it was breached.", "uc_api_step_1_title": "The alert states the breach", "uc_api_step_1_body": "The rule is written against the SLO, so the notification carries the threshold, the observed value and the evaluation window. 380 ms against 200 ms, sustained across three windows.", "uc_api_step_2_title": "The dashboard narrows it to one endpoint", "uc_api_step_2_body": "p50 has not moved. p99 has. If the whole service were slow, p50 would move too, so the problem is one route. The route breakdown shows which: POST /checkout.", "uc_api_step_3_title": "Two releases, side by side", "uc_api_step_3_body": "Split the same endpoint by service.version: p99 is 120 ms on abc123 and 380 ms on def456. Traces under def456 have 50 db.query spans where the earlier ones have 3.", "uc_api_outcome_line": "Reverted def456 at T+9:20. p99 back under the threshold on the next evaluation window.", "uc_api_metric_1_label": "alert to revert", "uc_api_metric_2_label": "p99, before and after", "uc_api_metric_3_label": "db spans per request", "uc_api_cap_title": "Why it took three minutes", "uc_api_cap_1_title": "The alert knows its own SLO", "uc_api_cap_1_body": "Rules are written against a percentile and a threshold, so the notification already says how far over the line you are.", "uc_api_cap_2_title": "Version is an attribute you can filter on", "uc_api_cap_2_body": "service.version rides on every span the SDK sends. Comparing two releases is a group-by.", "uc_api_cap_3_title": "The percentile keeps its traces", "uc_api_cap_3_body": "p99 is computed from spans that are still stored. Select the spike on the chart and you get the requests inside it.", "uc_ecom_seo_title": "E-Commerce Observability | Maple", "uc_ecom_seo_desc": "Checkout failures during a flash sale, traced from the alert to the Stripe timeout and the retry-exhaustion logs behind it, one order id the whole way.", "uc_ecom_hero_title": "Checkout is failing. Which orders, and why.", "uc_ecom_hero_lede": "During a sale you need to know which customers lost their basket and why. Filter by one order id and the alert, the trace and the logs all show the same failure.", "uc_ecom_story_title": "One order, from the alert to the timeout", "uc_ecom_story_lede": "order_8421 is one failed order. Each screen below is filtered to it, so you never line up timestamps across two tools.", "uc_ecom_step_1_title": "The alert fires on the checkout route only", "uc_ecom_step_1_body": "Error rate on POST /checkout crosses its threshold while the rest of the site is healthy. The sale traffic is fine. The payment path is not.", "uc_ecom_step_2_title": "The trace ends at Stripe", "uc_ecom_step_2_body": "Open one failed order. Three retries sit at the bottom of the waterfall in red, each timing out at exactly 1.75 s, the client's own deadline.", "uc_ecom_step_3_title": "The logs say the pool was empty", "uc_ecom_step_3_body": "The log lines from those spans share the same trace id. They show the retry budget running out and the connection pool at capacity. The requests timed out waiting for a connection.", "uc_ecom_outcome_line": "Pool raised and the retry budget cut to two. Checkout error rate back to baseline within one evaluation window.", "uc_ecom_metric_1_label": "alert to root cause", "uc_ecom_metric_2_label": "retries per failed order", "uc_ecom_metric_3_label": "timeout, every time", "uc_ecom_cap_title": "Why one order id was enough", "uc_ecom_cap_1_title": "Business ids are just attributes", "uc_ecom_cap_1_body": "Set order.id on the span once and it is filterable everywhere spans are: the trace list, the error issue, the log search. No separate index to maintain.", "uc_ecom_cap_2_title": "Outbound calls are spans too", "uc_ecom_cap_2_body": "The Stripe call is a client span with its own duration and status. You see the third-party timeout directly instead of guessing at it from your own error rate.", "uc_ecom_cap_3_title": "Logs are joined to the span", "uc_ecom_cap_3_body": "You open a span's logs from the span. There is no text query to narrow by hand to the same minute, which is usually the slowest part of an investigation.", "uc_micro_seo_title": "Microservices Debugging | Maple", "uc_micro_seo_desc": "Latency doubled with no deploy in sight. Follow one release across the service map, a trace and its logs to the connection pool that ran dry two hops away.", "uc_micro_hero_title": "Nobody deployed. Latency doubled anyway.", "uc_micro_hero_lede": "In a distributed system, the service that got slow is rarely the one that broke. The map shows which edge degraded first, and the trace shows how far the wait spread.", "uc_micro_story_title": "Two hops from the symptom to the cause", "uc_micro_story_lede": "payment-svc@1.4.2 shipped four hours before latency went up. The map, the trace and the logs below are all filtered to it.", "uc_micro_step_1_title": "The map shows a slow edge", "uc_micro_step_1_body": "api-gateway is the service being paged about, but its own spans are fine. The degraded edge is two hops in: payment-svc to user-db, p95 up from 12 ms to 210 ms.", "uc_micro_step_2_title": "The trace shows the wait", "uc_micro_step_2_body": "Open a slow request and the db span is almost all of it. The query itself takes 4 ms, but the span takes 210 ms. The other 206 ms was spent waiting before the query ran.", "uc_micro_step_3_title": "The logs name the release", "uc_micro_step_3_body": "Lines from that span read pool wait 206 ms, acquired after 3 attempts. Filter by service.version and they start at 1.4.2, which raised concurrency without raising the pool.", "uc_micro_outcome_line": "Pool size raised to match the new concurrency. The p95 edge latency returned to 14 ms without a rollback.", "uc_micro_metric_1_label": "hops from the page", "uc_micro_metric_2_label": "p95 edge latency, before and after", "uc_micro_metric_3_label": "of the span was waiting", "uc_micro_cap_title": "Why the cause was findable", "uc_micro_cap_1_title": "Edges carry their own latency", "uc_micro_cap_1_body": "The map measures each caller-callee pair separately, so a dependency that degraded for one caller and not another is visible as one edge.", "uc_micro_cap_2_title": "A db span is wait plus work", "uc_micro_cap_2_body": "The client span covers getting a connection as well as running the query. When the pool runs dry, span duration and query duration drift apart, and you can see the gap.", "uc_micro_cap_3_title": "Old releases are filterable too", "uc_micro_cap_3_body": "service.version is on every span, so you can ask which release a span came from. A release from four hours ago is as easy to find as one from four minutes ago.", "hero_shot_services": "Services", "hero_shot_services_line": "Rank every service by latency and errors.", "platform_map_name": "Service map", "platform_alerts_name": "Alerting", "pricing_hero_title": "Predictable, usage-based pricing.", "pricing_hero_lede": "One plan at {price}/month with {included} GB of logs, traces and metrics each, then a flat {rate}/GB. Nothing is metered by seat, host or query, and this page and your invoice show the same numbers.", "pricing_no_seat": "No per-seat pricing", "pricing_no_host": "No per-host pricing", "pricing_no_series": "No per-series pricing for metrics", "pricing_hero_estimate": "Estimate your monthly bill", "pricing_rates_eyebrow": "The rates", "pricing_rate_included": "{amount} included", "pricing_rates_retention": "{days}-day retention on traces, logs and replays, 90 days on metrics, and 12 months on service charts. Metrics are metered by volume like everything else, so a high-cardinality series costs by the bytes it sends.", "pricing_paths_eyebrow": "Ways to run Maple", "pricing_paths_heading": "Pick where it runs. The product is the same.", "pricing_paths_lede": "Every option has the same UI, query engine and MCP server. Alerting and team accounts are on Cloud only for now. What changes is who operates it and where the data lives.", "pricing_paths_when": "Best when", "pricing_path_local_name": "Local", "pricing_path_local_price": "Free", "pricing_path_local_body": "One binary on your machine: OTLP ingest, an embedded database and the dashboard on 127.0.0.1. No account, no cloud, no rate limits.", "pricing_path_local_when": "You're developing or debugging on a laptop or in CI.", "pricing_path_local_cta": "Read the local docs →", "pricing_path_selfhost_name": "Self-hosted", "pricing_path_selfhost_price": "Free", "pricing_path_selfhost_body": "One container for one team under FSL-1.1, with a shared access token and no alerting. Retention and residency are your call.", "pricing_path_selfhost_when": "Telemetry can't leave your network.", "pricing_path_selfhost_cta": "Read the source →", "pricing_path_cloud_name": "Cloud", "pricing_path_cloud_price": "{price}/month", "pricing_path_cloud_body": "Managed by us, metered per signal, {retention} days of traces and logs and 90 of metrics, every feature on. Starts with a {trial}-day free trial.", "pricing_path_cloud_when": "You'd rather not operate observability.", "pricing_path_cloud_cta": "Start free trial →", "pricing_path_enterprise_name": "Enterprise", "pricing_path_enterprise_body": "Higher volume, custom retention, priority support, and the option to run in your own cloud account.", "pricing_path_enterprise_when": "There's a compliance review or a volume that needs a quote.", "pricing_path_enterprise_cta": "Talk to the founder →", "faq_measure_q": "How is volume measured?", "faq_measure_a": "Per signal, on the decoded OpenTelemetry payload: the bytes your logs, spans and metric points occupy once the ingest endpoint unpacks them, before compression. Each signal has its own {included} GB allowance and they are not pooled. Sending 250 GB of logs and 10 GB of traces bills 150 GB of log overage, not zero.", "faq_metrics_q": "Do metrics cost extra?", "faq_metrics_a": "No. Metrics are metered by volume like logs and traces: {included} GB included, then {rate} per GB. There is no per-series, per-sample or custom-metric surcharge.", "faq_sessions_q": "How are browser sessions and product events billed?", "faq_sessions_a": "A browser session is one visitor session recorded by the browser SDK, replay included. {included} are included each month, then {rate} per session.", "faq_events_priced": "Product events are your track() calls, /v1/events posts and annotated spans: {included} are included each month, then {rate} per million. Automatic page views are not counted.", "faq_events_beta": "Product events are unlimited and free while they are in beta.", "faq_spend_q": "Can I cap what I spend?", "faq_spend_a": "Yes. Set an overage cap per signal in the billing settings, and usage alerts at the thresholds you choose. When a signal reaches its cap, ingest for that signal is rejected until the billing cycle resets. Caps and alerts run on the same usage your invoice is built from.", "stack_eyebrow": "What it works with", "stack_heading": "Every language and signal you already run.", "stack_lede": "Maple takes OTLP straight in, so anything OpenTelemetry can instrument is covered. Official SDKs, integrations and alert channels on top.", "stack_apm_title": "APM & tracing", "stack_apm_body": "Auto-instrument with an official Maple SDK, or point any OpenTelemetry SDK at the ingest endpoint.", "stack_apm_link": "All SDKs and guides →", "stack_frontend_title": "Frontend & mobile", "stack_frontend_body": "Session replay, web analytics and product events from the browser SDK; traces and crashes from the Swift SDK.", "stack_frontend_link": "Browser sessions →", "stack_infra_title": "Logs & infrastructure", "stack_infra_body": "Logs ride the same OTLP pipeline as traces. Kubernetes ships as a Helm chart; containers, Prometheus endpoints and Cloudflare come with their own collectors.", "stack_infra_link": "Kubernetes monitoring →", "stack_cloud_title": "Cloud & data sources", "stack_cloud_body": "Connect a provider with one OAuth grant and its metrics, logs and deploys land next to your spans.", "stack_cloud_link": "Integration docs →", "stack_alerts_title": "Alert destinations", "stack_alerts_body": "Every rule routes to where you already are, with queued delivery and every provider response kept on the record.", "stack_alerts_link": "Alerting →", "stack_agents_title": "AI agents", "stack_agents_body": "One MCP endpoint over Streamable HTTP, with API keys or OAuth. Any client that speaks MCP can list services, search traces and propose fixes.", "stack_agents_link": "AI & MCP →", "stack_otel_title": "OpenTelemetry native", "stack_otel_body": "Maple accepts OTLP from any language or framework OpenTelemetry supports. If it can emit OTLP, Maple can store it, query it and alert on it.", "stack_otel_link": "Read the docs →", "stack_chip_browser": "Browser", "stack_chip_ios": "iOS", "stack_chip_collector": "OTel Collector", "stack_chip_email": "Email", "stack_chip_webhooks": "Webhooks", "stack_chip_claude_code": "Claude Code", "stack_chip_cursor": "Cursor", "stack_chip_any_mcp": "Any MCP client", "cmp_eyebrow": "Compare", "cmp_hub_seo_title": "Maple vs Datadog, Grafana, New Relic, Dash0, SigNoz, Axiom, Better Stack and Honeycomb: observability compared", "cmp_hub_seo_desc": "Side-by-side comparisons of Maple with Datadog, Grafana Cloud, New Relic, Dash0, SigNoz, Axiom, Better Stack and Honeycomb: where each differs, where each is ahead, and what a month costs at list price.", "cmp_hub_title": "Maple, compared.", "cmp_hub_lede": "One page per vendor. Each lists where the two differ and where the other tool is ahead, and prices the same month on both at list price. Every vendor claim links to its source and carries the date it was checked.", "cmp_hub_matrix_eyebrow": "Summary", "cmp_hub_matrix_title": "The questions finance asks first", "cmp_hub_pages_eyebrow": "Comparisons", "cmp_hub_pages_title": "Pick a vendor", "cmp_hub_card_tally": "{total} differences · {name} ahead on {ahead}", "cmp_hub_card_cta": "Read the comparison", "cmp_nav_differences": "Differences", "cmp_nav_price": "Price", "cmp_nav_migration": "Migration", "cmp_nav_faq": "FAQ", "cmp_nav_surfaces": "Surfaces", "cmp_diff_eyebrow": "Differences", "cmp_diff_title": "Where Maple and {name} differ", "cmp_diff_lede": "The marker beside each row shows which tool is ahead on it. Rows where both do the same thing are grouped into one line at the end.", "cmp_diff_col_topic": "Topic", "cmp_diff_col_edge": "Edge", "cmp_diff_group_differ": "Where they differ", "cmp_diff_group_ahead": "Where {name} is ahead", "cmp_diff_edge_even": "Even", "cmp_diff_roadmap": "On the roadmap", "cmp_parity_label": "Both have", "cmp_price_eyebrow": "Price", "cmp_price_title": "The same month, billed twice", "cmp_price_lede": "A reference workload priced on both at list price. Change the sliders underneath for your own numbers.", "cmp_price_workload": "Reference workload", "cmp_price_per_month": "per month", "cmp_price_less": "{amount} less per month on Maple", "cmp_price_more": "{amount} more per month on Maple", "cmp_price_yearly": "{amount} a year", "cmp_price_adjust": "Adjust for your usage", "cmp_price_free": "Free", "cmp_price_verified": "List prices checked {date}. Sources at the foot of the page.", "cmp_migration_eyebrow": "Migration", "cmp_migration_title": "Moving from {name}", "cmp_migration_lede": "Telemetry moves with one exporter change. Dashboards and alert rules don't cross vendors, so plan those as the real work.", "cmp_migration_diff_label": "The change", "cmp_migration_diff_caption": "Add Maple as a second exporter, run both, then drop the first.", "cmp_surfaces_eyebrow": "Surfaces", "cmp_surfaces_title": "What {name} leaves you to build", "cmp_surfaces_instead_label": "On {name}:", "cmp_surfaces_link": "See the surface", "cmp_surface_map_name": "Service map", "cmp_surface_map_body": "Every service, queue and database, drawn from the spans themselves. There is no topology file to maintain. Edges carry throughput, error rate and latency, and clicking one opens the traces behind it.", "cmp_surface_sessions_name": "Browser sessions and replay", "cmp_surface_sessions_body": "Watch the session back with its DOM, console, network and errors, and open the backend trace behind any click. Masking happens in the browser, before anything leaves the page.", "cmp_surface_analytics_name": "Web analytics", "cmp_surface_analytics_body": "Visitors, page views, referrers and countries, from the same browser SDK that is already recording sessions. One script, and every number opens into the sessions behind it.", "cmp_surface_analytics_shot": "The Web Analytics page: unique visitors, sessions, page views and bounce rate over the last 7 days, with the live visitor badge and the visitors and page views chart", "cmp_surface_k8s_name": "Kubernetes console", "cmp_surface_k8s_body": "Workloads and restarts with live CPU and memory per pod, joined to the traces and logs of the service running inside them.", "cmp_sources_eyebrow": "Sources", "cmp_sources_title": "Where the {name} claims come from", "cmp_sources_lede": "Vendor facts on this page link to the vendor's own pages and carry the month they were last checked. If one is stale, tell us.", "cmp_sources_checked": "checked {date}", "cmp_related_eyebrow": "Compare", "cmp_related_title": "Other comparisons", "cmp_cta_title": "See the difference on your own traces.", "cmp_cta_lede": "Point one exporter at Maple and run both for a week. The free trial needs a card, with no charge until it ends.", "intg_seo_title": "Integrations: languages, AI agents, clouds and alerts | Maple", "intg_seo_desc": "Every language, agent framework, cloud, database and alert destination Maple works with, each with a setup guide. If it speaks OpenTelemetry, it sends to Maple.", "intg_eyebrow": "Integrations", "intg_title": "Connect the stack you already run.", "intg_lede": "Instrument any language with OpenTelemetry, connect your cloud and databases in a few clicks, and send alerts to the tools your team already watches.", "intg_search_label": "Search integrations", "intg_search_placeholder": "Search", "intg_rail_label": "Integration categories", "intg_search_empty": "No integration matches that search. Anything that speaks OpenTelemetry still works.", "intg_cat_languages_title": "Languages and frameworks", "intg_cat_languages_lede": "OpenTelemetry SDKs for every major runtime. Traces, logs and metrics arrive linked by one trace id.", "intg_cat_agents_title": "AI agent frameworks", "intg_cat_agents_lede": "Trace every model call, tool call and handoff your agents make, next to the services they call.", "intg_cat_cloud_title": "Cloud and infrastructure", "intg_cat_cloud_lede": "Connect a cloud account or run one collector. Platform metrics and logs land next to the services they run.", "intg_cat_data_title": "Databases and streaming", "intg_cat_data_lede": "Database and stream health beside the queries your services send them.", "intg_cat_alerts_title": "Alerts and chat", "intg_cat_alerts_lede": "Send alerts where your team already is. In Slack and Discord, ask Maple about your telemetry in the channel.", "intg_cat_code_title": "Code and AI tools", "intg_cat_code_lede": "Tie telemetry to the commits that shipped it, and give your coding agent the same view you have.", "intg_hint_workers": "Effect SDK or native OTLP export", "intg_hint_browser": "Session replay, Web Vitals and errors", "intg_hint_aws": "CloudWatch metrics through a read-only role", "intg_hint_gcp": "Cloud Logging and Cloud Monitoring", "intg_hint_cloudflare": "Zone, Workers, DNS and Queues analytics", "intg_hint_railway": "CPU, memory, network and disk per service", "intg_hint_kubernetes": "One Helm chart for host, kubelet and cluster metrics", "intg_hint_docker": "One container for per-container stats and logs", "intg_hint_hosts": "CPU, memory, disk and load per host", "intg_hint_prometheus": "Scrape any metrics endpoint, no collector", "intg_hint_planetscale": "Branch metrics, webhooks and query insights", "intg_hint_supabase": "Project metrics, logs and slowest queries", "intg_hint_warpstream": "Consumer lag and object-store health", "intg_hint_slack": "Alerts and questions in your channels", "intg_hint_discord": "Alerts and questions in your server", "intg_hint_pagerduty": "Incidents that open and resolve with the alert", "intg_hint_telegram": "Alerts to a chat, group or channel", "intg_hint_email": "Alerts to members of your organization", "intg_hint_webhooks": "Signed JSON to any HTTP endpoint", "intg_hint_hazel": "Alerts into a Hazel workspace channel", "intg_hint_github": "Commits, Actions runs and source access", "intg_hint_mcp_client": "Ask about your telemetry over MCP", "intg_hint_mcp": "Any client that speaks MCP", "intg_hint_alchemy": "Telemetry, alerts and dashboards as code", "intg_otel_title": "Anything that speaks OpenTelemetry", "intg_otel_body": "No list covers every stack. Point any OTLP exporter or collector at Maple and it works, with no proprietary agent.", "intg_otel_link": "Read the quickstart", "intg_request_link": "Request an integration", "intg_cta_title": "Send your first trace today.", "intg_cta_lede": "Point one exporter at Maple and connect your cloud when you are ready. The free trial needs a card, with no charge until it ends.", "cmp_parity_tracing": "Distributed tracing", "cmp_parity_logs": "Log management", "cmp_parity_metrics": "Metrics and dashboards", "cmp_parity_alerting": "Alerting", "cmp_parity_errors": "Error tracking", "cmp_parity_k8s": "Kubernetes monitoring", "cmp_parity_api": "Public API", "cmp_parity_mcp": "MCP server for agents", "cmp_parity_otel": "OpenTelemetry ingest", "cmp_parity_replay": "Session replay", "cmp_topic_pricing": "Pricing model", "cmp_topic_hosts": "Per-host fees", "cmp_topic_seats": "Per-seat fees", "cmp_topic_agents": "Agents", "cmp_topic_source": "Source code", "cmp_topic_selfhost": "Self-hosting", "cmp_topic_retention": "Retention", "cmp_topic_setup": "Time to first trace", "cmp_topic_ai": "AI and agents", "cmp_topic_backends": "Backends to run", "cmp_topic_query": "Query languages", "cmp_topic_otel": "OpenTelemetry", "cmp_topic_synthetics": "Synthetic monitoring", "cmp_topic_products": "Security and network products", "cmp_topic_integrations": "Integration catalog", "cmp_topic_rum": "Frontend and mobile", "cmp_topic_plugins": "Plugins", "cmp_topic_promql": "PromQL compatibility", "cmp_topic_datasources": "Non-telemetry data sources", "cmp_topic_oncall": "On-call", "cmp_topic_security": "Application security", "cmp_topic_k8s_tooling": "Kubernetes tooling", "cmp_topic_compliance": "Compliance attestations", "cmp_topic_errors": "Error workflow", "cmp_topic_local": "Local development", "cmp_topic_cost_controls": "Cost controls", "cmp_topic_volume_pricing": "Price at high volume", "cmp_topic_channels": "Notification channels", "cmp_topic_status_pages": "Status pages", "cmp_topic_slo": "SLOs", "cmp_topic_outliers": "Outlier analysis", "cmp_topic_free": "Free plan", "cmp_dd_seo_title": "Maple vs Datadog: an open-source Datadog alternative, compared", "cmp_dd_seo_desc": "Maple vs Datadog for observability: where they differ, where Datadog is ahead, and what one month costs on each at list price. Open source, OpenTelemetry-native, no per-host fees.", "cmp_dd_hero_title": "Maple vs Datadog", "cmp_dd_hero_lede": "Datadog is the broadest observability suite on the market, and its bill is shaped by hosts, agents and add-on products. Maple is an open-source observability platform for traces, logs and metrics that ingests OpenTelemetry directly and bills on data volume.", "cmp_dd_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB. Browser sessions are the only other meter: 5,000 a month included, then $0.002 each.", "cmp_dd_pricing_them": "Infrastructure from $15 per host, APM from $31 per host, logs $0.10 per GB ingested plus $1.70 per million indexed events at 15-day retention. Custom metrics, RUM and each add-on product are priced separately.", "cmp_dd_hosts_maple": "None. A hundred containers on one host or one process on a hundred hosts cost the same at equal volume.", "cmp_dd_hosts_them": "Infrastructure and APM are both metered per host, and containers draw down a per-host allotment.", "cmp_dd_agents_maple": "None. Any OpenTelemetry SDK or the OpenTelemetry Collector sends OTLP straight to Maple.", "cmp_dd_agents_them": "The Datadog Agent and Datadog tracing libraries. OTLP is accepted through the Agent, the Collector's Datadog exporter or direct OTLP intake, and some features need the Agent.", "cmp_dd_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release.", "cmp_dd_source_them": "Proprietary. The Agent is open source; the platform is not.", "cmp_dd_selfhost_maple": "Yes. One container on your own infrastructure for one team. Alerting and team accounts are on Cloud only for now.", "cmp_dd_selfhost_them": "No. SaaS only.", "cmp_dd_retention_maple": "30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_dd_retention_them": "Indexed logs are priced at the retention you pick, 3 to 30 days; the $1.70 list rate is the 15-day option. Each other product carries its own retention.", "cmp_dd_setup_maple": "Point an OTLP exporter at Maple and traces start arriving.", "cmp_dd_setup_them": "Roll out the Agent per host or as a DaemonSet, then enable and configure each product.", "cmp_dd_ai_maple": "Investigations that diagnose an alert or issue and write up what broke, plus a hosted MCP server, so Claude, Cursor or any agent can query your telemetry.", "cmp_dd_ai_them": "Bits AI across the platform and an MCP server.", "cmp_dd_synthetics_maple": "HTTP, TCP and DNS checks as often as every 30 seconds, plus cron heartbeats. A failed HTTP check opens as a trace. No scripted browser tests.", "cmp_dd_synthetics_them": "API and browser tests from managed locations, priced per ten thousand runs.", "cmp_dd_products_maple": "Not available.", "cmp_dd_products_them": "Network Performance Monitoring, Cloud SIEM, Cloud Security and CI Visibility: a product for each layer, each on its own meter.", "cmp_dd_integrations_maple": "OpenTelemetry receivers plus connected providers: AWS, GCP, Cloudflare, Railway, Supabase, PlanetScale, Prometheus, WarpStream, Hazel and GitHub. Kubernetes and Docker are agent installs.", "cmp_dd_integrations_them": "More than 800 vendor-maintained integrations, many for systems that never emit OpenTelemetry.", "cmp_dd_rum_maple": "Session replay correlated with backend traces, web analytics from the browser SDK, and a Swift SDK.", "cmp_dd_rum_them": "Full RUM with funnels, heatmaps and error tracking, plus mobile RUM for iOS and Android.", "cmp_dd_compliance_maple": "SOC 2 and ISO 27001 certification in progress. Maple Cloud runs in the US or the EU (Frankfurt), and self-hosting keeps the data in your own audited environment.", "cmp_dd_compliance_them": "SOC 2, ISO 27001, HIPAA and FedRAMP attestations with regional sites.", "cmp_dd_mig_1_title": "Keep the OpenTelemetry you have", "cmp_dd_mig_1_body": "Services already on OpenTelemetry SDKs need no code change. Add Maple as a second OTLP exporter and ship to both backends while you compare.", "cmp_dd_mig_2_title": "Replace the Agent with the Collector", "cmp_dd_mig_2_body": "Where the Datadog Agent is the only source, run the OpenTelemetry Collector with the same receivers, host metrics, Kubernetes and file logs, and export OTLP to Maple.", "cmp_dd_mig_3_title": "Rebuild dashboards and monitors", "cmp_dd_mig_3_body": "Dashboards and monitors don't cross vendors. Maple's builder and alert rules cover the common set, and the MCP server can draft both from a description.", "cmp_dd_faq_1_q": "Is Maple a drop-in replacement for Datadog?", "cmp_dd_faq_1_a": "For traces, logs, metrics, dashboards, alerting and error tracking, yes. Maple has uptime checks but no scripted browser tests, Network Performance Monitoring or Cloud SIEM, and its integration catalog is OpenTelemetry-first rather than 800 vendor connectors. Teams that live in APM, logs and infrastructure dashboards move cleanly; teams that bought Datadog for its security or network products should keep those.", "cmp_dd_faq_2_q": "Does Maple support the Datadog Agent?", "cmp_dd_faq_2_a": "No. Maple ingests OpenTelemetry, so the Datadog Agent is replaced by the OpenTelemetry Collector, which has receivers for the same sources: host metrics, Kubernetes, Docker, file logs, Prometheus endpoints. Datadog's own tracing libraries are replaced by OpenTelemetry SDKs, which cover the same languages and frameworks.", "cmp_dd_faq_3_q": "Can I migrate without downtime?", "cmp_dd_faq_3_a": "Yes. Add Maple as a second exporter in the Collector and send telemetry to both backends. Verify traces, logs and alerts in Maple, then remove the Datadog exporter. Nothing about the switch requires a deploy of your services if they already speak OpenTelemetry.", "cmp_dd_faq_4_q": "How does the pricing actually compare?", "cmp_dd_faq_4_a": "Maple charges $39 a month with 100 GB each of logs, traces and metrics included, then $0.30 per GB. Datadog charges per host for infrastructure and APM, per GB ingested and per million indexed events for logs, and separately for custom metrics and each add-on product. The receipts above price one reference month on both; the calculator under them prices yours.", "cmp_dd_faq_5_q": "Can I self-host Maple?", "cmp_dd_faq_5_a": "Yes. The source is on GitHub under FSL-1.1 and converts to Apache 2.0 two years after each release. Run it on your own infrastructure for data residency, keep retention as long as you like, and pay for compute and storage instead of per-GB fees. The hosted cloud is there when you'd rather not operate it.", "cmp_gf_seo_title": "Maple vs Grafana: one platform instead of Loki, Tempo and Mimir", "cmp_gf_seo_desc": "Maple vs Grafana Cloud and the LGTM stack: where they differ, where Grafana is ahead, and what one month costs on each at list price. One store for traces, logs and metrics, no PromQL, LogQL or TraceQL.", "cmp_gf_hero_title": "Maple vs Grafana", "cmp_gf_hero_lede": "Grafana is the dashboard most engineers already know, and behind it sits a stack: Loki for logs, Tempo for traces, Mimir or Prometheus for metrics, each with its own query language. Maple is an open-source observability platform that stores all three signals in one place behind one query surface.", "cmp_gf_backends_maple": "One system. Traces, logs and metrics land in one store, keyed by the same trace id.", "cmp_gf_backends_them": "Loki, Tempo and Mimir or Prometheus, each deployed, scaled and upgraded on its own, with Grafana in front.", "cmp_gf_query_maple": "One visual builder across every signal, SQL through the API and the MCP server when you want it.", "cmp_gf_query_them": "PromQL for metrics, LogQL for logs, TraceQL for traces.", "cmp_gf_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB. No per-user fee.", "cmp_gf_pricing_them": "Free tier, then $19 a month plus $8 per active user, $6.50 per thousand active metric series, and $0.55 per GB of logs and traces: $0.05 to process, $0.40 to write and $0.10 to retain for the 30 days.", "cmp_gf_seats_maple": "Unlimited. Every engineer, on-call responder and manager gets a login.", "cmp_gf_seats_them": "$8 per active user beyond the three included on Pro.", "cmp_gf_setup_maple": "Point an OTLP exporter at Maple and traces start arriving.", "cmp_gf_setup_them": "Provision each backend, connect each as a data source, then build the first dashboard.", "cmp_gf_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release. One repository for the whole platform.", "cmp_gf_source_them": "AGPL-3.0 for Grafana, Loki, Tempo and Mimir. Grafana Cloud itself is proprietary.", "cmp_gf_selfhost_maple": "One deployment: the platform and its database.", "cmp_gf_selfhost_them": "Self-hostable with mature Helm charts, at the cost of operating four services.", "cmp_gf_otel_maple": "OTLP in. Nothing else to translate.", "cmp_gf_otel_them": "OTLP through Alloy or the Collector, fanned out to Tempo, Loki and Mimir.", "cmp_gf_plugins_maple": "No plugin system. Dashboards are built from Maple's own panels.", "cmp_gf_plugins_them": "Hundreds of panel and data-source plugins, dashboards-as-code, and a community library of dashboards for almost anything.", "cmp_gf_promql_maple": "No PromQL. Existing Grafana dashboards and Prometheus alert rules don't import.", "cmp_gf_promql_them": "PromQL is the standard for metrics queries. Existing dashboards and recording rules carry over unchanged.", "cmp_gf_datasources_maple": "Telemetry only.", "cmp_gf_datasources_them": "Query Postgres, Elasticsearch, CloudWatch and dozens more from one panel.", "cmp_gf_oncall_maple": "On-call schedules with overrides, escalation chains that page until someone acknowledges, and maintenance windows, with no per-responder fee. Pages go out by email and Slack or Discord message, not phone or SMS.", "cmp_gf_oncall_them": "Grafana IRM with on-call schedules, escalation chains and incident timelines.", "cmp_gf_mig_1_title": "Re-point the Collector", "cmp_gf_mig_1_body": "If Alloy or the OpenTelemetry Collector already feeds Tempo, Loki and Mimir, add an OTLP exporter to Maple and run both until the alerts you care about fire from Maple.", "cmp_gf_mig_2_title": "Rebuild the dashboards that matter", "cmp_gf_mig_2_body": "Grafana JSON doesn't import. Most teams find a handful of dashboards carry the load; rebuild those in Maple's builder and let the long tail lapse.", "cmp_gf_mig_3_title": "Decommission the stack", "cmp_gf_mig_3_body": "Turn off Loki, Tempo, Mimir and Grafana once Maple is the source of truth. That is four services fewer to upgrade, scale and page on.", "cmp_gf_faq_1_q": "Does Maple replace the whole Grafana stack?", "cmp_gf_faq_1_a": "Yes. Maple replaces Grafana for dashboards and alerting, Loki for logs, Tempo for traces and Mimir or Prometheus for metrics with one platform. What it doesn't replace is Grafana as a general-purpose charting tool for non-telemetry data sources.", "cmp_gf_faq_2_q": "How is Maple different from Grafana Cloud?", "cmp_gf_faq_2_a": "Grafana Cloud operates Loki, Tempo and Mimir for you, but you still query three stores in three languages and pay per active user. Maple stores every signal in one place, correlates them by trace id, and has no per-user line.", "cmp_gf_faq_3_q": "Do I need to learn a new query language?", "cmp_gf_faq_3_a": "No. Maple's query builder works the same way for traces, logs and metrics. SQL is available through the API and the MCP server for the questions a builder can't express. There is no PromQL, LogQL or TraceQL to learn, and also no PromQL compatibility for dashboards you already have.", "cmp_gf_faq_4_q": "Can I still self-host?", "cmp_gf_faq_4_a": "Yes. Maple is on GitHub under FSL-1.1, converting to Apache 2.0. The difference from the LGTM stack is that you deploy one platform and one database instead of four services with their own scaling models.", "cmp_gf_faq_5_q": "What about Grafana's plugins?", "cmp_gf_faq_5_a": "Maple has no plugin ecosystem. If your dashboards lean on community panels or on querying business databases alongside telemetry, Grafana is ahead here. Most observability dashboards use a dozen panel types, and Maple's builder covers those.", "cmp_nr_seo_title": "Maple vs New Relic: full-stack observability without per-user pricing", "cmp_nr_seo_desc": "Maple vs New Relic: where they differ, where New Relic is ahead, and what one month costs on each at list price. OpenTelemetry-native, no per-user fees, open source and self-hostable.", "cmp_nr_hero_title": "Maple vs New Relic", "cmp_nr_hero_lede": "New Relic is a full-stack platform priced per full-platform user plus data. Maple is an open-source observability platform for traces, logs and metrics that ingests OpenTelemetry directly and charges for data volume.", "cmp_nr_seats_maple": "Unlimited. Every engineer, on-call responder and manager gets a login.", "cmp_nr_seats_them": "Standard: $10 for the first full-platform user, then $99 each, capped at five. Pro: $349 per user per month on an annual commitment.", "cmp_nr_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB.", "cmp_nr_pricing_them": "100 GB free, then $0.40 per GB on Original Data or $0.60 on Data Plus, on top of user fees.", "cmp_nr_agents_maple": "None. OpenTelemetry SDKs and the OpenTelemetry Collector.", "cmp_nr_agents_them": "New Relic agents per language. OTLP is accepted, with some features available only through the agents.", "cmp_nr_query_maple": "Visual builder across every signal, SQL through the API and the MCP server.", "cmp_nr_query_them": "NRQL, a SQL-like language across all data types, with a mature query builder.", "cmp_nr_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release.", "cmp_nr_source_them": "Proprietary. The agents are open source; the platform is not.", "cmp_nr_selfhost_maple": "Yes. Run it on your own infrastructure.", "cmp_nr_selfhost_them": "No. SaaS only.", "cmp_nr_retention_maple": "30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_nr_retention_them": "Eight days for traces and 30 for logs by default. Data Plus adds up to 90 days on most data types.", "cmp_nr_ai_maple": "Investigations that diagnose an alert or issue and write up what broke, plus a hosted MCP server, so Claude, Cursor or any agent can query your telemetry.", "cmp_nr_ai_them": "New Relic AI and an MCP server.", "cmp_nr_synthetics_maple": "HTTP, TCP and DNS checks as often as every 30 seconds, plus cron heartbeats. A failed HTTP check opens as a trace. No scripted browser tests.", "cmp_nr_synthetics_them": "Scripted browser and API checks from managed locations, included in the platform.", "cmp_nr_rum_maple": "Session replay tied to backend traces, web analytics from the browser SDK, and a Swift SDK.", "cmp_nr_rum_them": "Browser RUM with funnels and session traces, plus mobile agents for iOS and Android.", "cmp_nr_integrations_maple": "OpenTelemetry receivers plus connected providers: AWS, GCP, Cloudflare, Railway, Supabase, PlanetScale, Prometheus, WarpStream, Hazel and GitHub. Kubernetes and Docker are agent installs.", "cmp_nr_integrations_them": "Hundreds of quickstarts and agents, including legacy runtimes without OpenTelemetry support.", "cmp_nr_security_maple": "Not available.", "cmp_nr_security_them": "Vulnerability management and IAST bundled into the platform.", "cmp_nr_mig_1_title": "Swap agents for OpenTelemetry", "cmp_nr_mig_1_body": "Replace New Relic's language agents with OpenTelemetry SDKs. Auto-instrumentation covers the same frameworks, and the result is portable to any backend.", "cmp_nr_mig_2_title": "Export to both", "cmp_nr_mig_2_body": "Point the Collector or the SDK exporter at Maple alongside New Relic's OTLP endpoint. Verify traces, logs and alerts in Maple before cutting over.", "cmp_nr_mig_3_title": "Recreate dashboards and alert policies", "cmp_nr_mig_3_body": "NRQL dashboards don't import. Rebuild the ones that matter in Maple's builder and map alert policies to alert rules; the MCP server can draft both.", "cmp_nr_faq_1_q": "Is Maple a full replacement for New Relic?", "cmp_nr_faq_1_a": "For APM, logs, metrics, dashboards, alerting and error tracking, yes. For browser visibility Maple offers session replay correlated with backend traces rather than full RUM, and it has uptime checks but no scripted browser tests or vulnerability management. If those products are why you bought New Relic, keep it for them.", "cmp_nr_faq_2_q": "How much does the per-user pricing matter?", "cmp_nr_faq_2_a": "It is the largest line on the receipts above. New Relic charges per full-platform user, capped at five on Standard and $349 each on Pro. Maple has no per-user line, so the gap grows with every engineer who needs access. The receipts show it on a ten-person team.", "cmp_nr_faq_3_q": "Does Maple have a query language like NRQL?", "cmp_nr_faq_3_a": "Maple ships a visual query builder that works the same way across traces, logs and metrics, and exposes SQL through the API and the MCP server for questions the builder can't express. There is no proprietary grammar to learn.", "cmp_nr_faq_4_q": "Can I run both during migration?", "cmp_nr_faq_4_a": "Yes. Configure the OpenTelemetry Collector to export to both New Relic and Maple. Verify in Maple, then remove the New Relic exporter.", "cmp_nr_faq_5_q": "Is Maple open source? Can I self-host it?", "cmp_nr_faq_5_a": "Yes to both. The source is on GitHub under FSL-1.1, converting to Apache 2.0 two years after each release. Self-host for data residency and unlimited retention, or use the hosted cloud at app.maple.dev.", "cmp_d0_seo_title": "Maple vs Dash0: the open-source, self-hostable Dash0 alternative", "cmp_d0_seo_desc": "Maple is an open-source Dash0 alternative: OpenTelemetry-native, usage-based, and self-hostable. Where they differ, where Dash0 is ahead, and what a month costs on each.", "cmp_d0_hero_title": "Maple vs Dash0", "cmp_d0_hero_lede": "Dash0 and Maple start from the same place: OpenTelemetry in, usage-based pricing, no per-seat fees for observability, an MCP server for agents. The difference is ownership: Maple is open source and you can self-host it, while Dash0 is a closed SaaS.", "cmp_d0_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release.", "cmp_d0_source_them": "Closed. Dash0 open-sources its OpenTelemetry distribution and Kubernetes operator, not the platform.", "cmp_d0_selfhost_maple": "Yes. One container on your own infrastructure for one team. Alerting and team accounts are on Cloud only for now.", "cmp_d0_selfhost_them": "No. SaaS only, in Dash0-operated regions.", "cmp_d0_retention_maple": "30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_d0_retention_them": "30 days for spans, logs and web events and 13 months for metrics, included in the storage rate.", "cmp_d0_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB.", "cmp_d0_pricing_them": "Per data point: $0.60 per million spans or log records ($0.06 ingestion plus $0.54 storage at 30-day retention) and $0.20 per million metric data points ($0.02 plus $0.18 at 13-month retention).", "cmp_d0_ai_maple": "Investigations that diagnose an alert or issue and write up what broke, plus a hosted MCP server that can also create dashboards, alerts and SLOs.", "cmp_d0_ai_them": "An MCP server and AI-assisted views.", "cmp_d0_promql_maple": "No PromQL. Visual builder and SQL.", "cmp_d0_promql_them": "PromQL for metrics and Perses-based dashboards-as-code.", "cmp_d0_k8s_tooling_maple": "Kubernetes monitoring through the OpenTelemetry Collector. No operator of Maple's own.", "cmp_d0_k8s_tooling_them": "A Kubernetes operator and an OpenTelemetry distribution, both maintained in the open.", "cmp_d0_mig_1_title": "Keep your OpenTelemetry pipeline", "cmp_d0_mig_1_body": "Both platforms ingest standard OTLP, so SDKs, auto-instrumentation and semantic conventions carry over unchanged.", "cmp_d0_mig_2_title": "Add a second exporter", "cmp_d0_mig_2_body": "Add Maple next to the Dash0 exporter in the Collector and ship to both while you verify.", "cmp_d0_mig_3_title": "Recreate dashboards and alerts", "cmp_d0_mig_3_body": "Dashboards, saved views and alert rules don't travel over OTLP. Rebuild the ones that matter in Maple; the MCP server can draft them.", "cmp_d0_faq_1_q": "How is Maple different from Dash0?", "cmp_d0_faq_1_a": "The fundamentals match: both are OpenTelemetry-native, both price on usage with no per-seat fee for observability, both expose an MCP server. The difference is ownership. Maple is open source under FSL-1.1 and self-hostable on your own infrastructure with retention you control. Dash0 is a closed, SaaS-only backend.", "cmp_d0_faq_2_q": "Is there an open-source alternative to Dash0?", "cmp_d0_faq_2_a": "Yes. Maple is open source on GitHub under FSL-1.1, converting to Apache 2.0, and you can self-host it as one container for one team. Dash0 open-sources client-side tooling, its OpenTelemetry distribution and Kubernetes operator, but not the platform, and offers no self-hosting.", "cmp_d0_faq_3_q": "How hard is the migration?", "cmp_d0_faq_3_a": "The pipeline is one exporter change: both platforms ingest standard OTLP. Dashboards, saved views and alert rules don't transfer, so plan to recreate those.", "cmp_d0_faq_4_q": "How does the pricing compare?", "cmp_d0_faq_4_a": "Both are usage-based with no per-seat fee for observability. Dash0 bills per data point; Maple bills per GB of decoded OTLP. Which is cheaper depends on how large your spans and log records are. The receipts above convert at one kilobyte per span or log record and 0.1 KB per metric data point; the calculator lets you change the counts.", "cmp_d0_faq_5_q": "Does Dash0 have anything Maple doesn't?", "cmp_d0_faq_5_a": "PromQL for metrics, Perses dashboards-as-code, synthetic API checks, and a vendor-maintained Kubernetes operator and OpenTelemetry distribution. Those rows are marked above.", "cmp_sn_seo_title": "Maple vs SigNoz: two OpenTelemetry-native, ClickHouse-backed platforms compared", "cmp_sn_seo_desc": "Maple vs SigNoz: both OpenTelemetry-native, ClickHouse-backed, one app for traces, logs and metrics. Where they differ (license, self-hosting, the error workflow, session replay, retention pricing), where SigNoz is ahead, and what one month costs on each.", "cmp_sn_hero_title": "Maple vs SigNoz", "cmp_sn_hero_lede": "SigNoz and Maple are the closest pair on this site: OpenTelemetry in, ClickHouse underneath, one application for traces, logs and metrics, an MCP server, usage pricing with no seats or hosts. The differences are in what happens after the telemetry lands and in how each one is licensed and run.", "cmp_sn_retention_maple": "At the flat rate: 30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_sn_retention_them": "15 days for logs and traces and one month for metrics by default. Longer retention raises the per-GB and per-sample rates; the calculator on SigNoz's pricing page quotes them.", "cmp_sn_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB. Metrics are metered by volume, not by sample.", "cmp_sn_pricing_them": "$49 a month including $49 of usage, then $0.30 per GB of logs and traces and $0.10 per million metric samples. Enterprise from $4,000 a month.", "cmp_sn_errors_maple": "Exceptions become issues with an owner, a state, a severity and comments. A resolved issue that fires again reopens.", "cmp_sn_errors_them": "An exceptions list grouped by service, type and message, each linked to its trace. No state, owner or severity on a group.", "cmp_sn_rum_maple": "Browser session replay with the trace behind every click. 5,000 sessions a month included.", "cmp_sn_rum_them": "Browser traces, logs and Web Vitals over OpenTelemetry. No session replay.", "cmp_sn_local_maple": "One binary with an embedded ClickHouse. `maple start` on a laptop, no containers, no account.", "cmp_sn_local_them": "The Linux install still needs ClickHouse and Postgres beside it; Docker wants 4 GB of memory.", "cmp_sn_source_maple": "FSL-1.1, converting to Apache 2.0 two years after each release. One license, no enterprise directory.", "cmp_sn_source_them": "MIT core, plus an `ee/` directory under a commercial license that holds SAML, OIDC and fine-grained RBAC.", "cmp_sn_ai_maple": "A hosted MCP server exposing the product's own queries, scoped to the org, with write tools for alert rules, dashboards and issues.", "cmp_sn_ai_them": "A hosted MCP server with fifty-plus tools, agent skills for Claude, Codex and Cursor, and Noz, an AI teammate that is Cloud-only.", "cmp_sn_selfhost_maple": "One container with a built-in store, an ingest key and an access token. One team, one replica, no alerting or SSO yet.", "cmp_sn_selfhost_them": "Docker, Swarm, Helm and Foundry installs with per-platform guides. Multi-user with roles and Google OAuth in the Community edition.", "cmp_sn_promql_maple": "No PromQL. Visual builder and SQL, one grammar across signals.", "cmp_sn_promql_them": "Query Builder v5, ClickHouse SQL and PromQL in dashboards. Prometheus rules carry over.", "cmp_sn_cost_controls_maple": "Ingest rules drop, sample or redact data before it is stored or billed. Per-signal overage caps stop a signal at a set spend until the billing cycle resets, with usage alerts before that.", "cmp_sn_cost_controls_them": "Ingest Guard: daily caps per signal and per team in Cloud that drop data past the limit.", "cmp_sn_channels_maple": "Slack, Microsoft Teams, Google Chat, PagerDuty, incident.io, Discord, Telegram, Linear, email and webhooks.", "cmp_sn_channels_them": "Nine channel types, including Opsgenie, Microsoft Teams, Incident.io, Rootly and Zenduty.", "cmp_sn_mig_1_title": "Keep the Collector, add an exporter", "cmp_sn_mig_1_body": "Both platforms ingest standard OTLP, so SDKs, auto-instrumentation and semantic conventions carry over unchanged. Add Maple next to the SigNoz exporter and ship to both until the alerts you care about fire from Maple.", "cmp_sn_mig_2_title": "Move the dashboards and alerts with one prompt", "cmp_sn_mig_2_body": "Dashboards and alert rules don't travel over OTLP, but both MCP servers expose them: SigNoz's reads them, Maple's creates them. Connect an agent to both and ask it to port everything. One prompt is usually enough, PromQL panels included.", "cmp_sn_faq_1_q": "Is SigNoz open source?", "cmp_sn_faq_1_a": "The core is MIT. The `ee/` directory is under the SigNoz Enterprise License, which forbids production use without a paid license, and currently holds SAML and OIDC single sign-on and fine-grained RBAC. Maple is under FSL-1.1, source-available for two years per release and Apache 2.0 after, with no enterprise-only directory.", "cmp_sn_faq_2_q": "How is Maple different from SigNoz?", "cmp_sn_faq_2_a": "The basics match: OpenTelemetry in, ClickHouse underneath, one app, usage pricing, an MCP server. Maple adds error issues with an owner and a state, browser session replay, agent sessions for AI workloads, and a single-binary local mode. SigNoz is ahead on self-hosting maturity and PromQL.", "cmp_sn_faq_3_q": "Which is cheaper, SigNoz or Maple?", "cmp_sn_faq_3_a": "At small volumes they are within ten dollars of each other: $49 against $39. Above that, Maple includes 100 GB per signal and keeps 30 days at the same rate, while SigNoz's rate rises with retention. SigNoz's per-sample metric pricing can win if your metrics are many and small. The receipts above run both models on the same month.", "cmp_sn_faq_4_q": "Can I self-host both?", "cmp_sn_faq_4_a": "Yes. SigNoz has the more polished path today: Docker Compose, Helm, Foundry and platform guides, multi-user with roles in the Community edition. Maple self-hosts from the repository with a compose file, your ClickHouse and a root password, single-org, and runs as one binary with an embedded ClickHouse for local development.", "cmp_sn_faq_5_q": "Does SigNoz have session replay?", "cmp_sn_faq_5_a": "No. SigNoz ingests browser traces, logs and Web Vitals over OpenTelemetry. Maple records and replays browser sessions with the trace behind each event, masked in the browser before anything leaves the page.", "cmp_ax_seo_title": "Maple vs Axiom: an event store with APL, or a platform you can self-host", "cmp_ax_seo_desc": "Maple vs Axiom: both ingest OpenTelemetry and bill on usage with no per-seat fee. Where they differ on price, the error workflow, session replay, self-hosting and APL. Where Axiom is ahead, and what one month costs on each.", "cmp_ax_hero_title": "Maple vs Axiom", "cmp_ax_hero_lede": "Axiom and Maple both take OpenTelemetry in, bill on usage, and charge nothing per seat. They differ in what you get on top of the data. Axiom gives you APL and a dashboard builder. Maple ships the views already built, including the service map, session replay and web analytics, and you can self-host all of it.", "cmp_ax_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB. Browser sessions are the only other meter. Queries and storage are not separate line items.", "cmp_ax_pricing_them": "$25 a month, then three meters: data loading from $0.12 per GB, query compute from $0.20 per GB-hour, storage $0.03 per GB. The free allowance is 1 TB, 100 GB-hours and 100 GB. Single sign-on costs $100 a month, RBAC and the audit log $50 each.", "cmp_ax_volume_pricing_maple": "$0.30 per GB at every volume. Lower rates exist on Enterprise but are not published.", "cmp_ax_volume_pricing_them": "The ingest rate falls as volume grows, with no negotiation: $0.12 per GB, $0.085 above 500 TB, $0.06 above 5 PB. Pre-purchased credits on a 12- to 36-month term take a further discount.", "cmp_ax_errors_maple": "Exceptions become issues with an owner, a state, a severity and comments. A resolved issue that fires again reopens.", "cmp_ax_errors_them": "No issue object. Exceptions stay events: you query them in APL, group by message, and have a monitor notify per group.", "cmp_ax_rum_maple": "Browser session replay with the trace behind every click, web analytics from the browser SDK, and a Swift SDK for iOS.", "cmp_ax_rum_them": "Web Vitals from the browser and a zero-config app for Vercel. No session replay, no mobile SDK.", "cmp_ax_local_maple": "One binary with an embedded ClickHouse. `maple start` on a laptop, no containers, no account.", "cmp_ax_local_them": "Cloud only. Local work still means a dataset in a real organization and a token in your environment.", "cmp_ax_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release.", "cmp_ax_source_them": "Closed. The SDKs, Collector configurations and the Vercel app are public. The platform is not.", "cmp_ax_selfhost_maple": "Yes. One container on your own infrastructure, retention you set. One team, no alerting yet.", "cmp_ax_selfhost_them": "No. Axiom runs the edge deployments, in US East and EU Central. Enterprise can point storage at its own S3 bucket.", "cmp_ax_retention_maple": "At the flat rate: 30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_ax_retention_them": "Configurable per dataset and billed as storage: $0.03 per compressed GB a month for the first 60 days, $0.0163 to 120 days and $0.0052 after that, with 100 GB free.", "cmp_ax_query_maple": "One visual builder across every signal. When you want the query itself it is ClickHouse SQL, through the API or the MCP server.", "cmp_ax_query_them": "APL for events, MPL for metrics. Over a hundred functions, plus a skill that translates Splunk's SPL into APL.", "cmp_ax_ai_maple": "A hosted MCP server over the product's own queries, scoped to the org. Investigations run the diagnosis and write up what broke.", "cmp_ax_ai_them": "An MCP server, six agent skills (SRE, metrics, dashboards, alerting, cost control, SPL translation) and Spotlight, which diffs two sets of events. Query cost limits per token stop an agent running up the bill.", "cmp_ax_integrations_maple": "OpenTelemetry receivers plus connected providers: AWS, GCP, Cloudflare, Railway, Supabase, PlanetScale, Prometheus, WarpStream, Hazel and GitHub. Kubernetes and Docker are agent installs.", "cmp_ax_integrations_them": "Over forty apps, including Vector, CloudWatch, Lambda, Heroku and CI/CD. A Splunk app federates search across both tools.", "cmp_ax_cost_controls_maple": "Ingest rules drop, sample or redact data before it is stored or billed. Per-signal overage caps stop a signal at a set spend until the billing cycle resets, with usage alerts before that.", "cmp_ax_cost_controls_them": "Spend alerts and hard caps in the console. Past the limit, ingestion pauses instead of billing as overage.", "cmp_ax_compliance_maple": "SOC 2 and ISO 27001 certification in progress. Maple Cloud runs in the US or the EU (Frankfurt) at the same price, and self-hosting keeps the data in your own audited environment.", "cmp_ax_compliance_them": "SOC 2 Type II, ISO 27001, GDPR, a self-serve HIPAA BAA, and a choice of US or EU residency.", "cmp_ax_mig_1_title": "Keep the Collector, add an exporter", "cmp_ax_mig_1_body": "Both platforms ingest standard OTLP, so SDKs, auto-instrumentation and semantic conventions carry over unchanged. Add Maple next to the Axiom exporter and ship to both until the alerts you care about fire from Maple.", "cmp_ax_mig_2_title": "Move the dashboards and monitors with one prompt", "cmp_ax_mig_2_body": "Dashboards and monitors do not travel over OTLP, and neither does APL. Both platforms expose an MCP server: Axiom's reads what you have, Maple's creates it. Connect an agent to both and ask it to port everything, translating the queries as it goes.", "cmp_ax_faq_1_q": "Is Axiom open source? Can I self-host it?", "cmp_ax_faq_1_a": "No to both. Axiom publishes its SDKs, Collector configurations and the Vercel app, but the platform is closed and runs only in Axiom's own edge deployments, today US East and EU Central. Enterprise customers can keep object storage in their own S3 bucket, which is data residency rather than self-hosting. Maple's source is on GitHub under FSL-1.1, converts to Apache 2.0 two years after each release, and runs on your own infrastructure with your own ClickHouse.", "cmp_ax_faq_2_q": "How is Maple different from Axiom?", "cmp_ax_faq_2_a": "Axiom is an event store first. Everything lands in a dataset, you query it in APL, and a monitor runs that query on a schedule. Maple is the product on top of that storage. Exceptions become issues with an owner and a state, browser sessions replay with the trace behind each click, agent sessions are a signal of their own, and one binary runs the whole thing on a laptop. Axiom is ahead on price at volume, retention economics, integration breadth and compliance.", "cmp_ax_faq_3_q": "Which is cheaper, Axiom or Maple?", "cmp_ax_faq_3_a": "At the reference workload Maple is, because Axiom's platform fee and query-compute meter start charging before Maple's overage does. Above a terabyte a month Axiom's ingest rate is the lower one and keeps falling with volume, and its storage pricing makes long retention cheap. The receipts above run both published rate cards on the same month, and the calculator lets you change the volumes.", "cmp_ax_faq_4_q": "Does Axiom have error tracking or session replay?", "cmp_ax_faq_4_a": "Not as products. Exceptions are events you query in APL and group inside a monitor, so nothing carries an owner, a state or a reopen. There is no session replay. Maple has both: issues with a lifecycle, and browser replay masked in the page before anything leaves it.", "cmp_ax_faq_5_q": "Do I have to learn APL?", "cmp_ax_faq_5_a": "On Axiom, mostly yes. APL for events, MPL for metrics, and a skill that translates SPL if you are arriving from Splunk. Maple's visual builder covers every signal, and when you want the query itself it is ClickHouse SQL, through the API or the MCP server.", "cmp_ax_surfaces_lede": "Axiom stores events and gives you a dashboard builder, so every view is a panel you write in APL. Maple ships these four views already built.", "cmp_ax_surface_map_instead": "no dependency graph. You aggregate caller and callee pairs in APL and read the topology as a table.", "cmp_ax_surface_sessions_instead": "nothing to build on. Axiom stores browser events if you send them, but there is no capture and no replay.", "cmp_ax_surface_analytics_instead": "a dashboard you assemble from APL over your own events, with no path from a number to the session that produced it.", "cmp_ax_surface_k8s_instead": "a Kubernetes app that ships the data, and dashboards you build over it.", "cmp_bs_seo_title": "Maple vs Better Stack: telemetry you can self-host, or an incident platform with telemetry attached", "cmp_bs_seo_desc": "Maple vs Better Stack: both ingest OpenTelemetry, track errors, replay browser sessions and charge nothing for people who only read telemetry. Where they differ on source code, self-hosting and pricing, where Better Stack is ahead on status pages, phone paging and price per GB, and what one month costs on each.", "cmp_bs_hero_title": "Maple vs Better Stack", "cmp_bs_hero_lede": "Better Stack and Maple both take OpenTelemetry in, track errors, replay browser sessions and charge nothing for people who only read telemetry. Better Stack grew out of uptime monitoring into an incident platform: checks, on-call, status pages and a Slack incident bot sit beside the logs and traces. Maple starts from the telemetry and adds uptime checks and on-call, with the source on GitHub and a version you can run yourself.", "cmp_bs_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release.", "cmp_bs_source_them": "Closed. The platform's source is not published.", "cmp_bs_selfhost_maple": "Yes. One container on your own infrastructure, retention you set. One team, no alerting yet.", "cmp_bs_selfhost_them": "No. Enterprise plans offer a custom VPC deployment, and logs can live in your own S3 bucket for $250 a month.", "cmp_bs_local_maple": "One binary with an embedded ClickHouse. `maple start` on a laptop, no containers, no account.", "cmp_bs_local_them": "Cloud only. Local work still sends data to a source in a real team, with a token in your environment.", "cmp_bs_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB. The same rate in the US and the EU, with 30 days of traces and logs and 90 of metrics in the price.", "cmp_bs_pricing_them": "Ingest plus storage, priced by region. Logs and traces cost $0.10 per GB plus $0.05 per GB a month stored in Europe, $0.15 plus $0.08 in the US, $0.35 plus $0.18 in Singapore. Metrics are billed only on stored size, from $0.50 per GB a month. Bundles from $30 a month prepay 40 GB of each signal.", "cmp_bs_seats_maple": "No per-seat fees for anyone.", "cmp_bs_seats_them": "Members who only read telemetry are free. Anyone on call or working with uptime monitors needs a Responder license at $34 a month, $29 on a yearly plan.", "cmp_bs_errors_maple": "Exceptions from OpenTelemetry become issues with an owner, a state, a severity and comments, each linked to its trace. A resolved issue that fires again reopens.", "cmp_bs_errors_them": "Compatible with the Sentry SDKs, with 100,000 exceptions a month free and 90 days of retention. Resolve, snooze, release tracking, and a prompt with the error's context for Claude Code or Cursor.", "cmp_bs_ai_maple": "A hosted MCP server over the product's own queries, scoped to the org. Investigations run the diagnosis and write up what broke.", "cmp_bs_ai_them": "A remote MCP server that runs ClickHouse SQL over logs, spans, metrics, errors and replays, and an AI SRE agent in Slack billed at $5 per million tokens.", "cmp_bs_volume_pricing_maple": "$0.30 per GB at every volume past the allowance. Lower rates exist on Enterprise but are not published.", "cmp_bs_volume_pricing_them": "Lower than Maple's for logs and traces, storage included: $0.15 per GB held 30 days in Europe, $0.23 in the US. A terabyte of logs held 30 days is about $150 a month in Europe and $230 in the US, against about $309 on Maple.", "cmp_bs_retention_maple": "At the flat rate: 30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_bs_retention_them": "Billed as stored GB a month, so keeping data longer costs cents per GB rather than a plan change. Errors and replays keep 90 days, and archival to S3 is built in.", "cmp_bs_synthetics_them": "HTTP, TCP, DNS, SSL and ping checks every 30 seconds from multiple locations, Playwright transaction checks at $1 per 100 minutes, and cron heartbeats. Ten monitors are free.", "cmp_bs_oncall_them": "On-call schedules, escalations, unlimited phone and SMS alerts, and incident channels in Slack or Teams, included with each Responder license.", "cmp_bs_status_pages_maple": "None. Maple has no public status page.", "cmp_bs_status_pages_them": "Hosted status pages on your own domain, fed by the monitors. One is included, more are $15 a month each.", "cmp_bs_promql_them": "A drag-and-drop builder, SQL and PromQL. Prometheus queries carry over.", "cmp_bs_compliance_maple": "SOC 2 and ISO 27001 certification in progress. Maple Cloud runs in the US or the EU (Frankfurt) at the same price, and self-hosting keeps the data in your own audited environment.", "cmp_bs_compliance_them": "SOC 2 Type II with the report under NDA, GDPR, ISO 27001 data centers, and a choice of Europe, the US or Singapore. No HIPAA.", "cmp_bs_mig_1_title": "Keep the Collector, add an exporter", "cmp_bs_mig_1_body": "Both platforms ingest standard OTLP, so SDKs, auto-instrumentation and semantic conventions carry over unchanged. Add Maple next to the Better Stack exporter and ship to both until the alerts you care about fire from Maple.", "cmp_bs_mig_2_title": "Move monitors and rotations, or keep them", "cmp_bs_mig_2_body": "Maple has uptime checks, on-call schedules and escalation chains, but no status pages or phone paging yet. Recreate monitors and rotations in Maple, or point a Maple alert at a Better Stack incoming webhook so the rotation stays where it is.", "cmp_bs_mig_3_title": "Move the dashboards with one prompt", "cmp_bs_mig_3_body": "Dashboards and alert rules do not travel over OTLP. Both platforms expose an MCP server: Better Stack's reads what you have, Maple's creates it. Connect an agent to both and ask it to port everything, translating the SQL as it goes.", "cmp_bs_faq_1_q": "Is Better Stack open source? Can I self-host it?", "cmp_bs_faq_1_a": "No to both. Better Stack runs as a hosted service in Europe, the US and Singapore. Enterprise plans can get a custom VPC deployment, and logs can live in your own S3 bucket for $250 a month, which is data residency rather than self-hosting. Maple's source is on GitHub under FSL-1.1, converts to Apache 2.0 two years after each release, and runs on your own infrastructure as one container.", "cmp_bs_faq_2_q": "How is Maple different from Better Stack?", "cmp_bs_faq_2_a": "Better Stack is an incident platform with telemetry attached. Uptime checks, on-call schedules, status pages and a Slack incident bot are the core, and logs, traces, errors and replays feed into them. Maple starts from the telemetry: traces, logs, metrics, errors with a lifecycle and session replay, plus uptime checks and on-call, with the source published, a self-hosted version, and one binary that runs on a laptop. Better Stack is ahead on status pages, phone and SMS paging, price per GB and retention.", "cmp_bs_faq_3_q": "Which is cheaper, Better Stack or Maple?", "cmp_bs_faq_3_a": "It depends on the mix. At the reference workload Maple is, because Better Stack bills metrics at $0.50 per GB a month stored in Europe and $0.75 in the US. For logs and traces Better Stack's rate is the lower one: a terabyte of logs held 30 days is about $150 a month in Europe and $230 in the US, against about $309 on Maple. Anyone on call also needs a Responder license. The receipts above run both rate cards on the same month.", "cmp_bs_faq_4_q": "Can Maple replace Better Stack Uptime?", "cmp_bs_faq_4_a": "Partly. Maple runs HTTP, TCP and DNS checks and cron heartbeats, and its on-call schedules and escalation chains page people by email and Slack or Discord message. It has no scripted browser checks, no phone or SMS paging and no status pages yet. Teams that need those keep Better Stack for them, point a Maple webhook at a Better Stack incoming webhook, and send their telemetry to Maple.", "cmp_bs_faq_5_q": "Does Better Stack support OpenTelemetry?", "cmp_bs_faq_5_a": "Yes. It ingests OTLP from any Collector or SDK, and its own collector uses eBPF to trace services in Kubernetes or Docker without code changes. Because both sides speak OTLP, moving is an exporter change and the instrumentation stays as it is.", "cmp_hc_seo_title": "Maple vs Honeycomb: an open-source Honeycomb alternative, compared", "cmp_hc_seo_desc": "Maple vs Honeycomb: both ingest OpenTelemetry, charge nothing per seat and ship an MCP server. Where they differ on source code, self-hosting, session replay and error tracking, where Honeycomb is ahead on BubbleUp, retention and its free plan, and what one month costs on each.", "cmp_hc_hero_title": "Maple vs Honeycomb", "cmp_hc_hero_lede": "Honeycomb and Maple both take OpenTelemetry in, put traces at the center, charge nothing per seat and let an agent query your data over MCP. Honeycomb bills per event and is built for asking new questions of wide, high-cardinality spans. Maple bills per GB, adds session replay and an error inbox on every plan, and publishes its source with a version you can run yourself.", "cmp_hc_source_maple": "On GitHub under FSL-1.1, converting to Apache 2.0 two years after each release.", "cmp_hc_source_them": "Closed. Refinery, the sampling proxy, is Apache 2.0; the platform itself is not published.", "cmp_hc_selfhost_maple": "Yes, for free. One container on your own infrastructure, retention you set. One team, no alerting yet.", "cmp_hc_selfhost_them": "Enterprise only. Honeycomb Private Cloud runs single-tenant in your own AWS account, managed by Honeycomb or by you.", "cmp_hc_local_maple": "One binary with an embedded ClickHouse. `maple start` on a laptop, no containers, no account.", "cmp_hc_local_them": "Cloud only. Local work still sends events to a real environment, with an API key in your shell.", "cmp_hc_rum_maple": "Session replay tied to backend traces, web analytics from the browser SDK, and a Swift SDK. On every plan.", "cmp_hc_rum_them": "Frontend Observability for web, iOS and Android, sold as an Enterprise add-on. Core Web Vitals and traces from the browser, no session replay.", "cmp_hc_errors_maple": "Exceptions become issues with an owner, a state, a severity and comments, each linked to its trace. A resolved issue that fires again reopens.", "cmp_hc_errors_them": "Error analysis ships inside Frontend Observability. Backend exceptions are span events you query, group and alert on; there is no issue with an owner or a state.", "cmp_hc_pricing_maple": "$39 a month with 100 GB per signal included, then $0.30 per GB. Bytes are the meter, so a wide span costs more than a narrow one.", "cmp_hc_pricing_them": "Per event: every span, log record, span event and link counts as one, up to 1 MB and 2,000 attributes each. Pro is $3.00 per million events in fixed tiers from $150 for 50M to 750M, with metric data points included at five per event.", "cmp_hc_cost_controls_them": "No overage bill. Burst protection forgives days above twice the daily target; a second month over the tier gets ten days' notice, then events are throttled.", "cmp_hc_ai_maple": "A hosted MCP server over the product's own queries, scoped to the org. Investigations run the diagnosis and write up what broke.", "cmp_hc_ai_them": "Canvas, an AI copilot that runs queries in a notebook, the Honeycomb MCP server and Agent Timeline, included on every plan since July 2026.", "cmp_hc_outliers_maple": "No automatic outlier comparison. You filter and group by attributes yourself, or ask the agent to.", "cmp_hc_outliers_them": "BubbleUp: select the slow or failing requests on a heatmap and it ranks every attribute by how much it differs from the baseline. It is the reason teams choose Honeycomb.", "cmp_hc_slo_maple": "SLOs with error budgets and multiwindow burn-rate alerts, on every plan. Agents can create them over MCP.", "cmp_hc_slo_them": "SLOs with burn-rate alerts: 2 on Pro, 100 or more on Enterprise, with a reporting API.", "cmp_hc_retention_maple": "30 days for traces, logs and replays, 90 for metrics, 12 months for service charts. Longer on Enterprise, and your call when you self-host.", "cmp_hc_retention_them": "60 days for events and metrics on every plan, the free one included. Longer as an Enterprise add-on.", "cmp_hc_free_maple": "No free cloud plan: a 14-day trial, then $39 a month. Self-hosting and `maple start` locally are free.", "cmp_hc_free_them": "Free forever: 20M events and 100M metric data points a month, unlimited seats, two triggers.", "cmp_hc_compliance_maple": "SOC 2 and ISO 27001 certification in progress. Maple Cloud runs in the US or the EU (Frankfurt) at the same price, and self-hosting keeps the data in your own audited environment.", "cmp_hc_compliance_them": "SOC 2 Type II audited yearly, ISO 27001 certified hosting, a HIPAA BAA on Pro and Enterprise, and a US or EU region.", "cmp_hc_mig_1_title": "Keep the Collector, add an exporter", "cmp_hc_mig_1_body": "Both platforms ingest standard OTLP, so SDKs, auto-instrumentation and semantic conventions carry over unchanged. Add Maple next to the Honeycomb exporter and ship to both until the alerts you care about fire from Maple.", "cmp_hc_mig_2_title": "Sample before Maple, not inside it", "cmp_hc_mig_2_body": "If Refinery sits in front of Honeycomb today, point a second Collector pipeline at Maple before it. Maple bills bytes, not events, so the sample rate that kept you inside an event tier can usually come down.", "cmp_hc_mig_3_title": "Move boards and triggers with one prompt", "cmp_hc_mig_3_body": "Boards, triggers and SLOs do not travel over OTLP. Both platforms expose an MCP server: Honeycomb's reads what you have, Maple's creates dashboards and alert rules. Connect an agent to both and ask it to port everything.", "cmp_hc_faq_1_q": "Is Honeycomb open source? Can I self-host it?", "cmp_hc_faq_1_a": "The platform is closed source. Refinery, its tail-sampling proxy, is Apache 2.0. Enterprise customers can run Honeycomb Private Cloud, a single-tenant deployment in their own AWS account. Maple's source is on GitHub under FSL-1.1, converts to Apache 2.0 two years after each release, and self-hosts on any infrastructure, on any plan, with your own ClickHouse.", "cmp_hc_faq_2_q": "How is Maple different from Honeycomb?", "cmp_hc_faq_2_a": "Honeycomb is a query engine for wide events: BubbleUp, heatmaps and SLOs make it very good at finding why a slice of requests is slow. Maple covers the same traces, logs and metrics and adds what Honeycomb leaves out or sells as add-ons: session replay, an error inbox with owners and states, open source code, self-hosting and a local binary. Honeycomb is ahead on outlier analysis, retention and its free plan.", "cmp_hc_faq_3_q": "Which is cheaper, Honeycomb or Maple?", "cmp_hc_faq_3_a": "At the reference workload Maple is, because Honeycomb's 2026 Pro rate is $3.00 per million events and a span with 1 KB of attributes is a lot of events per GB. The gap narrows as spans get wider: Honeycomb charges the same for a 50 KB span as a 1 KB one, while Maple charges for the bytes. Honeycomb's free plan is also free, and Maple Cloud is not. The receipts above run both rate cards on the same month.", "cmp_hc_faq_4_q": "Does Maple have BubbleUp?", "cmp_hc_faq_4_a": "Not as a one-click feature. Maple lets you filter and group spans by any attribute, and its MCP server lets an agent do the comparison for you, but nothing ranks every attribute against a baseline automatically the way BubbleUp does. If that workflow is how your team debugs, weigh it.", "cmp_hc_faq_5_q": "Does Honeycomb support OpenTelemetry?", "cmp_hc_faq_5_a": "Yes. Honeycomb ingests OTLP over gRPC and HTTP from any Collector or SDK, in the US or the EU. Because both sides speak OTLP, moving is an exporter change and the instrumentation stays as it is.", "home_objections_aria": "Common questions", "home_objection_selfhost_claim": "Self-host the whole thing", "home_objection_selfhost_detail": "Every line of Maple's source is on GitHub under FSL-1.1, and each release becomes Apache 2.0 after two years. Run it on your own infrastructure with no license call.", "home_objection_selfhost_cta": "Read the source", "home_objection_otel_claim": "No proprietary agent", "home_objection_otel_detail": "Maple speaks plain OpenTelemetry. Point an OTLP exporter at it and keep the instrumentation you already wrote. If you leave, there is nothing to rip out.", "home_objection_otel_cta": "See instrumentation", "home_objection_trial_claim": "{price}/month, {days}-day free trial", "home_objection_trial_claim_no_trial": "{price}/month, everything included", "home_objection_trial_detail": "A card is required to start the trial and nothing is charged until it ends. No per-seat, per-host or per-query fees at any volume.", "home_objection_trial_cta": "See what it costs", "home_objection_region_claim": "Hosted in the US or the EU", "home_objection_region_detail": "Choose the EU when you sign up and your telemetry is stored and processed in Frankfurt. Same plan and price in both regions.", "home_objection_region_cta": "About EU hosting", "faq_region_q": "Where is my data stored?", "faq_region_a": "Maple Cloud runs in two regions: the United States and the European Union (Frankfurt, Germany). You choose one when you create your organization, and all of its telemetry is stored and processed there. The region cannot be changed later. Pricing is the same in both. To keep the data inside your own network, self-host Maple.", "pricing_regions_note": "Hosted in the US or the EU (Frankfurt) at the same price.", "pricing_regions_link": "EU hosting", "footer_eu_hosting": "EU hosting", "pricing_region_claim": "Hosted in the US or the EU", "pricing_region_eyebrow": "Data region", "pricing_region_us_name": "United States", "pricing_region_us_detail": "Dashboard at app.maple.dev", "pricing_region_eu_name": "European Union", "pricing_region_eu_detail": "Frankfurt, Germany. Dashboard at app.eu.maple.dev", "pricing_region_same": "Same plan, same rates", "pricing_region_same_body": "Pick a region when you create your organization. Its telemetry is stored and processed there.", "pricing_region_link": "About EU hosting", "compliance_title": "Compliance", "compliance_status": "Status", "compliance_available": "Available", "compliance_in_progress": "In progress", "compliance_dpa_request": "Request the DPA from {email}.", "compliance_dpa_held": "DPA available", "compliance_audits": "SOC 2 Type II and ISO 27001 audits are in progress.", "compliance_dpa_link": "Request the DPA", "bill_v_hosted": "Hosted server", "cmp_d0_synthetics_them": "Synthetic API checks at $0.20 per thousand runs, retained 13 months.", "nav_github": "GitHub, CI & Code Review", "nav_desc_github": "PR reviews and CI runs read against production", "feat_github_seo_title": "GitHub Code Review and CI Observability | Maple", "feat_github_seo_desc": "Maple reviews every pull request against the traces, errors and alerts behind the changed code, turns GitHub Actions runs into traces, and checks production after each deploy.", "feat_github_hero_title": "Review every pull request against production", "feat_github_hero_lede": "Maple reads your diff next to the traces, errors and CI runs it touches, comments before you merge, and checks back once it ships.", "feat_github_panel_title": "A reviewer that has seen production", "feat_github_panel_lede": "Every push gets one summary comment, an inline review and a check run. Findings cite the traffic, errors and alerts behind the lines you changed, and Code Review tracks what they caught across every repository.", "feat_github_artifact_title": "What lands on the pull request", "feat_github_artifact_lede": "A real review from Maple's own repository: a confidence score with its reason, the deploy steps found in the diff, findings with the file and line, and the production traffic of the changed code.", "feat_github_cap_title": "One GitHub App, the whole loop", "feat_github_cap_1_title": "One install, every repository", "feat_github_cap_1_body": "History backfills in the background after install. Pick one tracked branch per repository.", "feat_github_cap_2_title": "A review on every push", "feat_github_cap_2_body": "Summary comment, inline review and check run. Reply \u201cwon't fix\u201d to dismiss a finding, or mention the bot to ask about one.", "feat_github_cap_3_title": "Deploy steps found in the diff", "feat_github_cap_3_body": "Secrets, env vars, migrations and infra changes become a checklist. Merge with one unticked and Maple reminds you.", "feat_github_cap_4_title": "Broken telemetry fails the check", "feat_github_cap_4_body": "Removing a span, attribute or metric that an alert or dashboard reads is flagged before it ships.", "feat_github_cap_5_title": "CI runs as traces", "feat_github_cap_5_body": "Jobs and steps on one waterfall with runner queue time, fail rate and red streaks per workflow.", "feat_github_cap_6_title": "Your agent reads CI too", "feat_github_cap_6_body": "The MCP tool answers \u201cwhy is CI red\u201d, \u201cdid CI pass on this commit\u201d and \u201care builds getting slower\u201d.", "feat_github_cap_7_title": "Source at the deployed commit", "feat_github_cap_7_body": "Agents read the code that is running in production, not whatever is on main today.", "feat_github_cap_8_title": "Org defaults, repo overrides", "feat_github_cap_8_body": "Lenses, drafts, inline comments and daily limits per repository. Learns from the findings your team dismisses.", "gh_ci_eyebrow": "CI observability", "gh_ci_title": "Every workflow run is a trace", "gh_ci_lede": "Turn on CI traces and each GitHub Actions run lands as a job and step waterfall, runner queue time included. See why CI is red, which workflow keeps failing and whether builds are getting slower.", "gh_ci_trace_title": "The failing step, in the run's trace", "gh_ci_pr_title": "Reviews and CI runs on one pull request", "gh_after_eyebrow": "After merge", "gh_after_title": "The review doesn't end at merge", "gh_after_lede": "An hour after the deploy, Maple compares the hour before with the hour after and comments back on the pull request: shipped clean, or what stopped arriving and which errors are new.", "gh_after_rule_1_title": "Commit context on every trace", "gh_after_rule_1_body": "Commit SHAs on spans resolve to the message, author and a GitHub link.", "gh_after_rule_2_title": "Deploy markers", "gh_after_rule_2_body": "Metric shifts line up with the deploy that caused them, down to the commits that shipped.", "gh_after_rule_3_title": "Pull requests close issues", "gh_after_rule_3_body": "Link a PR to an error issue. Once it merges, Maple closes the issue if the error stops.", "home_github_eyebrow": "GitHub", "home_github_title": "Your pull requests, checked against production", "home_github_lede": "Install the GitHub App and Maple follows each change through its whole life: a review that has read the traces, CI runs as traces, and a check-in after the deploy.", "home_github_link": "GitHub, CI and code review", "home_github_step_1_title": "Before merge", "home_github_step_1_body": "A review on every push that cites the traffic, errors and alerts behind the lines you changed.", "home_github_step_2_title": "In CI", "home_github_step_2_body": "Every GitHub Actions run becomes a trace: jobs, steps and runner queue time on one waterfall.", "home_github_step_3_title": "After it ships", "home_github_step_3_body": "An hour after the deploy, Maple compares the hour before with the hour after and reports back on the pull request.", "nav_uptime": "Uptime Monitoring", "nav_desc_uptime": "Know the moment a site, port or cron job stops", "feat_uptime_seo_title": "Uptime Monitoring | Maple", "feat_uptime_seo_desc": "Uptime monitoring for websites, APIs, ports, DNS and cron jobs, checked as often as every 30 seconds. A failed HTTP check opens as a trace of the request inside your app, and alerts go to Slack, PagerDuty, email or a webhook.", "feat_uptime_hero_title": "Know when it's down, and why", "feat_uptime_hero_lede": "Maple checks your sites, APIs, ports and cron jobs around the clock. When one fails, you get the alert with the reason, and a failed HTTP check opens as a trace of the request inside your app.", "feat_uptime_artifact_title": "Your monitors, live", "feat_uptime_artifact_lede": "Every bar is one check. By default, two failures in a row open an incident and alert your team. It closes on its own once the check passes again.", "feat_uptime_cap_title": "What you can monitor", "feat_uptime_cap_1_title": "Websites and APIs", "feat_uptime_cap_1_body": "Check the status code, response time, body text, JSON fields or headers. Anything other than a 2xx counts as a failure unless you say otherwise.", "feat_uptime_cap_2_title": "Databases and other ports", "feat_uptime_cap_2_body": "Make sure Postgres, Redis, a message broker or a mail server still accepts connections.", "feat_uptime_cap_3_title": "DNS records", "feat_uptime_cap_3_body": "Catch a missing or changed A, AAAA, CNAME, MX, TXT or NS record before your users do.", "feat_uptime_cap_4_title": "Cron jobs and workers", "feat_uptime_cap_4_body": "Your job pings a URL when it finishes. If the ping doesn't arrive on schedule, Maple alerts you.", "feat_uptime_cap_5_title": "As often as every 30 seconds", "feat_uptime_cap_5_body": "Check every 30 seconds or once an hour, and decide how many failures it takes to alert, from 1 to 10.", "feat_uptime_cap_6_title": "Set up by your AI agent", "feat_uptime_cap_6_body": "Agents can create and update monitors through Maple's MCP server, for example right after shipping a new endpoint.", "uptime_trace_eyebrow": "Root cause", "uptime_trace_title": "See why it failed, not just that it failed", "uptime_trace_lede": "Most uptime tools stop at \"503\". Maple's HTTP checks carry a trace ID into your app, so if the app is instrumented, a failed check opens as the full request. Here, the database pool took five seconds to hand out a connection.", "uptime_trace_rule_1_title": "Nothing new to learn", "uptime_trace_rule_1_body": "HTTP, TCP and DNS checks are stored like any other request. Search them, chart them and alert on them with the tools you already use.", "uptime_trace_rule_2_title": "Tied to your services", "uptime_trace_rule_2_body": "Link a monitor to a service and its checks show up on that service's page, next to its errors and deploys.", "uptime_trace_rule_3_title": "Your service map stays clean", "uptime_trace_rule_3_body": "Checks never show up as a caller on your service map, so your real dependencies stay easy to read.", "uptime_eyebrow": "Uptime monitoring", "uptime_title": "The trace behind the failed check", "uptime_lede": "Maple checks your sites, APIs, ports and cron jobs around the clock. When one fails, your team gets the alert with the reason, and a failed HTTP check opens as a trace of the request inside your app.", "uptime_point_1_title": "HTTP failures come with a trace", "uptime_point_1_body": "Open a failed HTTP check and see what happened inside your instrumented app, down to the slow query.", "uptime_point_2_title": "Alerts where your team works", "uptime_point_2_body": "Slack, PagerDuty, email or a webhook, through the same alerting as the rest of Maple. Resolves itself when the check passes.", "uptime_point_3_title": "Sites, ports, DNS and cron jobs", "uptime_point_3_body": "Check status, speed, body or headers, or have a job ping Maple when it finishes.", "uptime_link_feature": "Explore uptime monitoring", "nav_anomalies": "Anomaly Detection", "nav_desc_anomalies": "Catch what no alert rule was written for", "feat_anomalies_seo_title": "Anomaly Detection | Maple", "feat_anomalies_seo_desc": "Maple compares every service with its own last 7 days and opens an anomaly when error rate, p95 latency, traffic, error logs or one error type moves far outside it. No rules to write.", "feat_anomalies_hero_title": "Catch the problem nobody wrote an alert for", "feat_anomalies_hero_lede": "Maple learns how each service behaves at each hour of the day. When error rate, latency, traffic or one error type moves far outside that, it opens an anomaly with the numbers behind it.", "feat_anomalies_artifact_title": "One service against its own week", "feat_anomalies_artifact_lede": "The band is checkout's p95 at this hour over the last 7 days. Two checks in a row far above it open an anomaly. At four times the baseline it is critical.", "feat_anomalies_cap_title": "What Maple watches", "feat_anomalies_cap_1_title": "Error rate", "feat_anomalies_cap_1_body": "The share of failed requests per service and environment, against the same hours last week. A service needs 50 requests in the hour before it is judged.", "feat_anomalies_cap_2_title": "p95 latency", "feat_anomalies_cap_2_body": "Slowdowns far outside the band and at least 50 ms slower. A nightly batch that is always slow is part of the baseline.", "feat_anomalies_cap_3_title": "Traffic loss", "feat_anomalies_cap_3_body": "A busy service that drops to almost nothing. A slow Sunday does not count. Losing 95% of normal traffic does.", "feat_anomalies_cap_4_title": "Error spikes", "feat_anomalies_cap_4_body": "One error type firing far more often than usual in 30 minutes. Spikes on the same service are grouped and linked to their issues.", "feat_anomalies_cap_5_title": "Error logs", "feat_anomalies_cap_5_body": "A jump in error-level logs per service, for failures that never show up as a failed span.", "feat_anomalies_cap_6_title": "Investigated for you", "feat_anomalies_cap_6_body": "Open an investigation from any anomaly, or turn on AI triage and an agent looks into each new one and attaches what it found." }