aid: vector name: Vector description: >- Vector is an open source high-performance observability data pipeline from Datadog for collecting, transforming, and routing logs, metrics, and traces. Built in Rust for performance and reliability, Vector supports 50+ sources, 20+ transforms, and 80+ sinks. It provides a built-in API for health monitoring and component inspection, plus Vector Remap Language (VRL) for powerful data transformation. type: Index image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg tags: - Data Pipeline - Logs - Metrics - Observability - Open Source - Rust - Traces url: >- https://raw.githubusercontent.com/api-evangelist/vector/refs/heads/main/apis.yml created: '2026-03-25' modified: '2026-05-03' specificationVersion: '0.19' apis: - aid: vector:vector-observability-api name: Vector Observability API description: >- The Vector Observability API provides HTTP endpoints for health monitoring of running Vector instances and gRPC endpoints for component inspection and event streaming. Enable via api.enabled: true in Vector configuration. Binds to 127.0.0.1:8686 by default. Note: the API does not support authentication and should only be used in isolated environments. humanURL: https://vector.dev/docs/reference/api/ baseURL: http://127.0.0.1:8686 tags: - Health Monitoring - Observability - Pipeline Management properties: - type: Documentation url: https://vector.dev/docs/reference/api/ - type: OpenAPI url: openapi/vector-observability-api-openapi.yml - type: JSONSchema url: json-schema/vector-observability-api-health-response-schema.json title: Health Response Schema - type: JSONStructure url: json-structure/vector-observability-api-health-response-structure.json title: Health Response Structure - type: Example url: examples/vector-observability-api-health-response-example.json title: Health Response Example - type: JSON-LD url: json-ld/vector-observability-api-context.jsonld - aid: vector:vector-vrl name: Vector Remap Language (VRL) description: >- Vector Remap Language (VRL) is a purpose-built expression language for transforming observability data in Vector. Provides 100+ built-in functions for parsing, filtering, enriching, and transforming logs, metrics, and traces without leaving the Vector pipeline. humanURL: https://vector.dev/docs/reference/vrl/ tags: - Data Transformation - Expression Language - VRL properties: - type: Documentation url: https://vector.dev/docs/reference/vrl/ - type: GitHub Repository url: https://github.com/vectordotdev/vrl - aid: vector:vector-helm name: Vector Helm Charts description: >- Official Helm charts for deploying Vector on Kubernetes as a DaemonSet (agent mode) or Deployment (aggregator mode). humanURL: https://vector.dev/docs/setup/installation/package-managers/helm/ tags: - Helm - Kubernetes - Deployment properties: - type: Documentation url: https://vector.dev/docs/setup/installation/package-managers/helm/ - type: GitHub Repository url: https://github.com/vectordotdev/helm-charts common: - type: Website url: https://vector.dev - type: Documentation url: https://vector.dev/docs/ - type: GitHubOrganization url: https://github.com/vectordotdev - type: GitHubRepository url: https://github.com/vectordotdev/vector - type: ReleaseNotes url: https://vector.dev/releases/ - type: Blog url: https://vector.dev/blog/ - type: Forum url: https://discord.com/invite/n2yjjZR - type: StackOverflow url: https://stackoverflow.com/questions/tagged/vector-dev - type: SpectralRules url: rules/vector-spectral-rules.yml - type: NaftikoCapability url: capabilities/pipeline-monitoring.yaml title: Pipeline Monitoring - type: Vocabulary url: vocabulary/vector-vocabulary.yaml - type: Features data: - name: High-Performance Pipeline description: Built in Rust with benchmarks showing 86+ MiB/s throughput for log pipeline workloads. - name: Unified Data Plane description: Single binary handles logs, metrics, and traces from collection through routing. - name: 50+ Sources description: Native integrations for files, Kafka, Kubernetes, AWS S3/CloudWatch, Splunk, and more. - name: 80+ Sinks description: Route data to Elasticsearch, Datadog, S3, BigQuery, Splunk, Loki, and many more destinations. - name: Vector Remap Language (VRL) description: Purpose-built expression language with 100+ functions for transforming observability data. - name: Observability API description: Built-in HTTP/gRPC API for health checks and component inspection (must be explicitly enabled). - name: Kubernetes Native description: Deploy as DaemonSet (agent) or Deployment (aggregator) with official Helm charts. - name: Agent and Aggregator Modes description: Run as a lightweight agent on each node or as a centralized aggregator for fan-in routing. - type: UseCases data: - name: Log Pipeline Unification description: Replace multiple log shippers with a single Vector pipeline for all log collection and routing. - name: Observability Cost Reduction description: Filter, sample, and transform data before sending to expensive SaaS observability platforms. - name: Vendor Switching description: Route observability data to multiple backends simultaneously to facilitate migration. - name: Kubernetes Log Collection description: Deploy Vector as a DaemonSet to collect container logs from all Kubernetes nodes. - name: Log Enrichment description: Parse, enrich, and normalize log events using VRL before routing to downstream systems. - name: Metrics Collection description: Collect host and service metrics using Vector's built-in sources and forward to Prometheus or DataDog. - name: Splunk Cost Reduction description: Use Vector to filter and route Splunk data to reduce indexing volume and licensing costs. - type: Integrations data: - name: Datadog description: Native Datadog logs and metrics sink; Vector was created and is maintained by Datadog. - name: Elasticsearch description: Elasticsearch sink for forwarding logs and metrics to Elasticsearch clusters. - name: Splunk HEC description: Splunk HTTP Event Collector sink for sending data to Splunk Enterprise and Cloud. - name: Kafka description: Kafka source and sink for consuming and producing observability data streams. - name: AWS S3 description: S3 sink for archiving logs and metrics to Amazon S3 for long-term storage. - name: Grafana Loki description: Loki sink for forwarding logs to Grafana's log aggregation system. - name: Prometheus description: Prometheus remote write sink and scrape source for metrics pipelines. - name: Kubernetes description: Kubernetes source for collecting container logs, pod metadata, and events. maintainers: - FN: Kin Lane email: kin@apievangelist.com