# FriendliAI Docs

> Let your team focus on building great AI products. FriendliAI will make sure your AI runs fast, affordable, and reliable at scale. Leave the hassle of AI inference to FriendliAI.

## Docs

- [Latest Updates](https://friendli.ai/docs/changelog.md): Track the latest FriendliAI product updates: new models, pricing changes, new features, and more.
- [Configuration](https://friendli.ai/docs/guides/container/configuration.md): Configuration reference for Friendli Container: how to pass launch options, serve across multiple GPUs, enable quantization, and run MoE models.
- [CUDA Compatibility](https://friendli.ai/docs/guides/container/cuda-compatibility.md): The Friendli Engine supports CUDA-enabled NVIDIA GPUs, which means it relies on a specific version of CUDA and necessitates proper CUDA compute compatibilities.
- [Deploy Friendli Container as Amazon EKS Add-On](https://friendli.ai/docs/guides/container/eks-quickstart.md): Deploy Friendli Container on Amazon EKS using the official AWS EKS Add-On. Set up GPU nodes, install the add-on, and run model inference.
- [Inference with gRPC](https://friendli.ai/docs/guides/container/inference-with-grpc.md): Run a gRPC inference server with Friendli Container and send requests using the Friendli Python SDK. Includes setup, configuration, and code examples.
- [Introducing Friendli Container](https://friendli.ai/docs/guides/container/introduction.md): Deploy generative AI models on your own infrastructure with Friendli Container. Full control over GPU resources, networking, and scaling.
- [Observability for Friendli Container](https://friendli.ai/docs/guides/container/monitoring.md): Observability is an integral part of DevOps. To support this, Friendli Container exports internal metrics in a Prometheus text format.
- [Optimize with Policy Search](https://friendli.ai/docs/guides/container/optimizing-inference-with-policy-search.md): Boost inference throughput by up to 2x for MoE and quantized models by running execution policy search in Friendli Container for production.
- [Quantization](https://friendli.ai/docs/guides/container/quantization.md): Learn how to serve pre-quantized models or perform online quantization with Friendli Container to reduce memory and speed up inference.
- [QuickStart: Friendli Container Trial](https://friendli.ai/docs/guides/container/quickstart.md): Get started with Friendli Container trial. Access the registry, configure your secret, launch the container, and monitor with Grafana.
- [Running Friendli Container on SageMaker](https://friendli.ai/docs/guides/container/sagemaker-integration.md): Create a real-time inference endpoint in Amazon SageMaker with Friendli Container. Leverage Friendli Engine for faster, cost-efficient serving.
- [Serving MoE Models](https://friendli.ai/docs/guides/container/serving-moe-models.md): Serve Mixture of Experts (MoE) models like Mixtral 8x7B with Friendli Container. Covers policy search setup and multi-GPU Docker configuration.
- [Serving Multi-LoRA Models](https://friendli.ai/docs/guides/container/serving-multi-lora-models.md): Serve multiple LoRA-adapted LLMs simultaneously with Friendli Container without additional GPU resources. No retraining needed for task-specific models.
- [Data Privacy and Security](https://friendli.ai/docs/guides/data-handling.md): Learn how FriendliAI handles your data. Inference requests and responses are never used for training or shared with third parties.
- [Autoscaling](https://friendli.ai/docs/guides/dedicated-endpoints/autoscaling.md): Configure autoscaling for Friendli Dedicated Endpoints to automatically adjust GPU replicas based on traffic and latency thresholds.
- [Dataset Specifications and Upload Guide](https://friendli.ai/docs/guides/dedicated-endpoints/dataset.md): Upload and manage datasets for Friendli Dedicated Endpoints. Covers supported formats, size limits, splits, versioning, and the upload process.
- [Endpoints](https://friendli.ai/docs/guides/dedicated-endpoints/endpoints.md): Configure, monitor, and manage Friendli Dedicated Endpoints. Learn about endpoint configuration options, statuses, and the metrics you can monitor.
- [Dedicated Endpoints FAQ and Troubleshooting](https://friendli.ai/docs/guides/dedicated-endpoints/faq.md): Answers to common questions about Friendli Dedicated Endpoints, including model compatibility, GPU requirements, billing, and troubleshooting tips.
- [Introducing Friendli Dedicated Endpoints](https://friendli.ai/docs/guides/dedicated-endpoints/introduction.md): Run custom or open-source generative AI models on dedicated GPU hardware with Friendli Dedicated Endpoints. No shared resources or infra management.
- [Multi-LoRA Serving](https://friendli.ai/docs/guides/dedicated-endpoints/lora-serving.md): Learn how to deploy LoRA models from Hugging Face Hub to Friendli Dedicated Endpoints for efficient inference, including a quick guide for FLUX LoRA models.
- [Models](https://friendli.ai/docs/guides/dedicated-endpoints/models.md): Manage models for Friendli Dedicated Endpoints. Upload directly, or load from Hugging Face repositories.
- [Online Quantization](https://friendli.ai/docs/guides/dedicated-endpoints/online-quantization.md): Automatically quantize models to 4-bit or 8-bit precision at deploy time on Friendli Dedicated Endpoints. No pre-quantized checkpoint needed.
- [GPUs and Pricing](https://friendli.ai/docs/guides/dedicated-endpoints/pricing.md): View Friendli Dedicated Endpoints pricing by GPU type. Covers supported instance types, per-second billing, and how autoscaling affects costs.
- [QuickStart: Friendli Dedicated Endpoints](https://friendli.ai/docs/guides/dedicated-endpoints/quickstart.md): Get started with Friendli Dedicated Endpoints. Create a project, pick a model, deploy an endpoint, and generate your first inference response.
- [Request Queueing](https://friendli.ai/docs/guides/dedicated-endpoints/request-queueing.md): Learn how to configure request queueing to keep performance predictable when a Friendli Dedicated Endpoint receives more traffic than it can serve.
- [Speculative Decoding](https://friendli.ai/docs/guides/dedicated-endpoints/speculative-decoding.md): Speed up LLM inference on Friendli Dedicated Endpoints with speculative decoding using proprietary draft models and N-gram token prediction.
- [Versioning](https://friendli.ai/docs/guides/dedicated-endpoints/versioning.md): Use endpoint versioning on Friendli Dedicated Endpoints to track deployment history, roll back to previous configurations, and update without downtime.
- [Get Started with FriendliAI](https://friendli.ai/docs/guides/introduction/get-started.md): Get started with FriendliAI. Create an API key, send your first request with Model APIs, and set up your favorite coding agent or SDK.
- [Build with FriendliAI](https://friendli.ai/docs/guides/introduction/overview.md): Build with FriendliAI for fast, affordable, and reliable AI inference. Start with Model APIs, then deploy any model on dedicated GPUs with Dedicated Endpoints.
- [Introducing Friendli Model APIs](https://friendli.ai/docs/guides/model-apis/introduction.md): Get started with Friendli Model APIs to access popular AI models via API. No infrastructure setup or GPU management required.
- [Models and Pricing](https://friendli.ai/docs/guides/model-apis/pricing.md): View Friendli Model APIs pricing per model. Compare token-based and audio-based rates across text and audio models.
- [QuickStart: Friendli Model APIs](https://friendli.ai/docs/guides/model-apis/quickstart.md): Get started with Friendli Model APIs in minutes. Explore popular AI models, experiment in a chat-style playground, and make your first API call with no setup required.
- [Rate Limits](https://friendli.ai/docs/guides/model-apis/rate-limits.md): Understand Friendli Model APIs rate limits and usage tiers. Tiers are based on lifetime spend and increase automatically as your usage grows.
- [Multi-Modality](https://friendli.ai/docs/guides/multi-modality.md): Process text, images, audio, and video with Friendli multimodal APIs. Includes vision, transcription, and image generation endpoint guides.
- [OpenAI Compatibility](https://friendli.ai/docs/guides/openai-compatibility.md): Use official OpenAI Python and Node.js SDKs with Friendli endpoints. Migrate existing OpenAI applications by changing the base URL and API key.
- [Reasoning](https://friendli.ai/docs/guides/reasoning.md): Enable model-agnostic reasoning on Friendli endpoints. Extract chain-of-thought traces from any supported model without writing custom parsers.
- [Structured Outputs](https://friendli.ai/docs/guides/structured-outputs.md): Generate JSON outputs conforming to a schema using Friendli Structured Outputs. Works on all chat-capable models with response_format support.
- [Account Suspension](https://friendli.ai/docs/guides/suite/account-suspension.md): Find out why your Friendli Suite account was suspended, what access is restricted, and the steps to resolve billing or policy-related suspensions.
- [Billing and Payments](https://friendli.ai/docs/guides/suite/billing-payments.md): Understand Friendli Suite prepaid billing, manage payment methods, view invoices, and learn how credits are applied to usage.
- [Credits](https://friendli.ai/docs/guides/suite/credits.md): Learn about Friendli Suite credit types, including promotional and purchased credits, their consumption order, expiration rules, and how to redeem promo codes.
- [Enterprise Plan](https://friendli.ai/docs/guides/suite/enterprise-plan.md): FriendliAI's Enterprise plan with reserved GPUs, higher API rate limits, private deployments, and enterprise-grade security and support.
- [Personal API Keys](https://friendli.ai/docs/guides/suite/personal-api-keys.md): Create and manage Personal API keys in Friendli Suite for API authentication. Covers key generation steps and usage in API requests.
- [Tool Calling](https://friendli.ai/docs/guides/tool-calling.md): Use OpenAI-compatible tool calling on Friendli endpoints. Broad model support, strict schema enforcement, and parallel tool call examples.
- [Build an Agent with Gradio](https://friendli.ai/docs/guides/tutorials/build-an-agent-with-gradio.md): Build and deploy an AI agent with Friendli Model APIs and Gradio in under 50 lines of Python. Includes chat UI setup.
- [Build an Agent with LangChain](https://friendli.ai/docs/guides/tutorials/build-an-agent-with-langchain.md): Create an AI agent using LangChain and Friendli Model APIs with tool calling. Step-by-step tutorial with code examples in Python.
- [Chat Docs with LangChain](https://friendli.ai/docs/guides/tutorials/chat-docs-with-langchain.md): Build a document chatbot with LangChain and Friendli using retrieval-augmented generation. Full RAG pipeline tutorial with embeddings and vector search.
- [Chat Docs with MongoDB](https://friendli.ai/docs/guides/tutorials/chat-docs-with-mongodb.md): Build a RAG chatbot with Friendli, MongoDB Atlas, and LangChain. Store document embeddings in a vector database and generate context-aware answers.
- [Getting Started with EXAONE 4.0](https://friendli.ai/docs/guides/tutorials/getting-started-with-exaone-4.0.md): Deploy and run LG AI Research's EXAONE 4.0 models on Friendli Dedicated Endpoints. Covers authentication, inference, reasoning, and optimization.
- [Getting Started with Nemotron 3](https://friendli.ai/docs/guides/tutorials/getting-started-with-nemotron-3.md): Deploy and run NVIDIA Nemotron 3 on Friendli Dedicated Endpoints. Includes setup, API usage, chat completions, and performance benchmarks.
- [Go Playground with Next.js](https://friendli.ai/docs/guides/tutorials/go-playground-with-nextjs.md): Build a Go game AI playground using Next.js and the Vercel AI SDK with Friendli Model APIs. Full-stack tutorial with streaming responses.
- [RAG App with LlamaIndex](https://friendli.ai/docs/guides/tutorials/rag-app-with-llamaindex.md): Build a retrieval-augmented generation app with LlamaIndex and Friendli Engine. Index documents, query with semantic search, and generate answers.
- [Tool Calling with Model APIs](https://friendli.ai/docs/guides/tutorials/tool-calling-with-model-apis.md): Implement tool calling with Friendli Model APIs. Tutorial covers defining tools, handling function calls, and building multi-turn agent loops.
- [FriendliAI Docs](https://friendli.ai/docs/index.md): FriendliAI provides fast, affordable, and reliable AI inference at scale. Explore guides, API reference, examples, and product quickstarts.
- [Use Claude Code with FriendliAI](https://friendli.ai/docs/integrate/agents/claude-code.md): Configure Claude Code to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use Cline with FriendliAI](https://friendli.ai/docs/integrate/agents/cline.md): Configure Cline to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use Cursor with FriendliAI](https://friendli.ai/docs/integrate/agents/cursor.md): Configure Cursor to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use Hermes Agent with FriendliAI](https://friendli.ai/docs/integrate/agents/hermes-agent.md): Configure Hermes Agent to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use Kilo Code with FriendliAI](https://friendli.ai/docs/integrate/agents/kilo-code.md): Configure Kilo Code to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use OpenClaw with FriendliAI](https://friendli.ai/docs/integrate/agents/openclaw.md): Configure OpenClaw to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use OpenCode with FriendliAI](https://friendli.ai/docs/integrate/agents/opencode.md): Configure OpenCode to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use Your Agent with FriendliAI](https://friendli.ai/docs/integrate/agents/overview.md): Set up your favorite coding agent with FriendliAI to run fast, cost-efficient, and reliable open-source models.
- [Use Pi with FriendliAI](https://friendli.ai/docs/integrate/agents/pi.md): Configure Pi to use FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [Use Your Agent or SDK with FriendliAI](https://friendli.ai/docs/integrate/overview.md): Connect your favorite coding agents and SDKs to FriendliAI for fast, cost-efficient, and reliable open-source model inference.
- [LangChain Node.js SDK](https://friendli.ai/docs/integrate/sdks/langchain/nodejs.md): Utilize the LangChain Node.js SDK with FriendliAI for seamless integration and enhanced tool calling capabilities in your applications.
- [LangChain Python SDK](https://friendli.ai/docs/integrate/sdks/langchain/python.md): Integrate FriendliAI with LangChain Python SDK. Use ChatOpenAI with tool calling and connect to Model APIs or Dedicated Endpoints.
- [Linkup](https://friendli.ai/docs/integrate/sdks/linkup.md): Find and access high-quality web content using the Linkup API, integrated with Friendli Model APIs for seamless interaction.
- [LiteLLM](https://friendli.ai/docs/integrate/sdks/litellm.md): Use LiteLLM with FriendliAI to call Model APIs, Dedicated Endpoints, and fine-tuned endpoints. Includes setup, model selection, and streaming examples.
- [LlamaIndex](https://friendli.ai/docs/integrate/sdks/llamaindex.md): Integrate FriendliAI with LlamaIndex. Use the Friendli LLM class for chat completions and text completions with sync, async, and streaming support.
- [OpenAI Node.js SDK](https://friendli.ai/docs/integrate/sdks/openai/nodejs.md): Use the OpenAI Node.js SDK with FriendliAI endpoints. Migrate existing Node.js apps by changing the base URL. Covers chat, streaming, and tool calls.
- [OpenAI Python SDK](https://friendli.ai/docs/integrate/sdks/openai/python.md): Use the OpenAI Python SDK with FriendliAI endpoints. Migrate existing Python apps by changing the base URL. Covers chat, streaming, and tool calling.
- [Use Your SDK with FriendliAI](https://friendli.ai/docs/integrate/sdks/overview.md): Browse FriendliAI SDK integrations for OpenAI, LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, and Weaviate. Pick the SDK that fits your stack.
- [Vercel AI SDK](https://friendli.ai/docs/integrate/sdks/vercel-ai.md): Use the Vercel AI SDK with FriendliAI for streaming chat UIs in Next.js and React. Connect to Model APIs or Dedicated Endpoints with minimal setup.
- [FriendliAI + Weaviate (Node.js)](https://friendli.ai/docs/integrate/sdks/weaviate/nodejs.md): Build RAG apps with FriendliAI and Weaviate in Node.js. Combine vector search with Friendli Engine inference to reduce hallucinations in responses.
- [FriendliAI + Weaviate (Python)](https://friendli.ai/docs/integrate/sdks/weaviate/python.md): Build RAG apps with FriendliAI and Weaviate in Python. Combine vector search with Friendli Engine inference for context-aware, grounded responses.
- [Cost](https://friendli.ai/docs/openapi/administration/cost.md): Get cost details for the team.
- [Container Audio Transcriptions](https://friendli.ai/docs/openapi/container/audio-transcriptions.md): Transcribe an audio file into text.
- [Container Audio Transcriptions Chunk Object](https://friendli.ai/docs/openapi/container/audio-transcriptions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Container when streaming the audio transcriptions API.
- [Container Chat Completions](https://friendli.ai/docs/openapi/container/chat-completions.md): Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
- [Container Chat Completions Chunk Object](https://friendli.ai/docs/openapi/container/chat-completions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Container when streaming the chat completions API.
- [Container Completions](https://friendli.ai/docs/openapi/container/completions.md): Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
- [Container Completions Chunk Object](https://friendli.ai/docs/openapi/container/completions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Container when streaming the completions API.
- [Container Detokenization](https://friendli.ai/docs/openapi/container/detokenization.md): Convert a list of token IDs back into text.
- [Container Image Edits](https://friendli.ai/docs/openapi/container/image-edits.md): Edit an image based on a text prompt.
- [Container Image Generations](https://friendli.ai/docs/openapi/container/image-generations.md): Generate an image from a text prompt.
- [Container Messages](https://friendli.ai/docs/openapi/container/messages.md): Generate a model response from a conversation using the Anthropic Messages API format. Supports streaming, function tool calls, and extended thinking.
- [Container Messages Chunk Object](https://friendli.ai/docs/openapi/container/messages-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Container when streaming the messages API.
- [Container Overview](https://friendli.ai/docs/openapi/container/overview.md): API reference for Friendli Container. Browse self-hosted inference endpoints for chat, responses, completions, messages, tokenization, images, and audio transcription.
- [Container Responses](https://friendli.ai/docs/openapi/container/responses.md): Generate a model response from text or image inputs. Follows the OpenAI Responses API format, with support for streaming, function and custom tool calls, structured outputs, and reasoning controls.
- [Container Responses Chunk Object](https://friendli.ai/docs/openapi/container/responses-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Container when streaming the responses API.
- [Container Text Classification](https://friendli.ai/docs/openapi/container/text-classification.md): Classify text input into categories with per-class probabilities.
- [Container Tokenization](https://friendli.ai/docs/openapi/container/tokenization.md): Convert text input into token IDs.
- [Add Samples](https://friendli.ai/docs/openapi/dataset/add-samples.md): Add new data samples to an existing Friendli dataset split via the API. Append training or evaluation data entries to your dataset programmatically.
- [Create a New Dataset](https://friendli.ai/docs/openapi/dataset/create-a-new-dataset.md): Create a new dataset in Friendli Suite via the API. Initialize a dataset resource to organize training and evaluation data with splits and versions.
- [Create a New Split](https://friendli.ai/docs/openapi/dataset/create-a-split.md): Create a new split within a Friendli dataset via the API. Use splits to organize data into training, validation, and test partitions.
- [Create a New Version](https://friendli.ai/docs/openapi/dataset/create-a-version.md): Create a new version of a Friendli dataset via the API. Snapshot your current dataset state to track changes and enable reproducible training runs.
- [Delete a Version](https://friendli.ai/docs/openapi/dataset/delete-a-version.md): Delete a specific version of a Friendli dataset by ID via the API. Permanently removes the version snapshot and its associated metadata.
- [Delete a Dataset](https://friendli.ai/docs/openapi/dataset/delete-dataset.md): Permanently delete a Friendli dataset by ID via the API. Removes the dataset along with all its splits, versions, and sample data.
- [Delete Samples](https://friendli.ai/docs/openapi/dataset/delete-samples.md): Delete specific data samples from a Friendli dataset split via the API. Remove individual entries by sample ID from your training or evaluation data.
- [Delete a Split](https://friendli.ai/docs/openapi/dataset/delete-split.md): Delete a split from a Friendli dataset by ID via the API. Permanently removes the split and all sample data it contains from the dataset.
- [Get Dataset Info](https://friendli.ai/docs/openapi/dataset/get-dataset-info.md): Retrieve metadata for a Friendli dataset by ID via the API. Returns the dataset name, modality, and creation and update timestamps.
- [Get Split Info](https://friendli.ai/docs/openapi/dataset/get-split-info.md): Retrieve metadata for a specific split within a Friendli dataset via the API. Returns the split name, sample count, and creation timestamp.
- [Get Version Info](https://friendli.ai/docs/openapi/dataset/get-version-info.md): Retrieve metadata for a specific version of a Friendli dataset via the API. Returns the version tag, creation date, and snapshot details.
- [List Datasets](https://friendli.ai/docs/openapi/dataset/list-datasets.md): List all datasets in your Friendli Suite project via the API. Returns dataset IDs, names, split counts, and creation timestamps for each entry.
- [List Samples](https://friendli.ai/docs/openapi/dataset/list-samples.md): List data samples in a Friendli dataset split via the API. Paginate through training or evaluation entries with sample IDs and content previews.
- [List Splits](https://friendli.ai/docs/openapi/dataset/list-splits.md): List all splits within a Friendli dataset via the API. Returns split IDs, names, and sample counts for training, validation, and test partitions.
- [List Versions](https://friendli.ai/docs/openapi/dataset/list-versions.md): List all versions of a Friendli dataset via the API. Returns version IDs, tags, and creation timestamps to track your dataset's revision history.
- [Dataset Overview](https://friendli.ai/docs/openapi/dataset/overview.md): API reference for Friendli Dataset API. Create, manage, and version datasets with splits and samples for model training and evaluation.
- [Update Samples](https://friendli.ai/docs/openapi/dataset/update-samples.md): Update existing data samples in a Friendli dataset split via the API. Modify sample content by ID without deleting and re-adding entries.
- [Dedicated Create Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/create.md): Create a Friendli Dedicated Endpoint deployment for a Hugging Face model via the API. Specify GPU type, replica count, and model configuration.
- [Dedicated Delete Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/delete.md): Permanently delete a Friendli Dedicated Endpoint deployment by ID. This stops the endpoint and releases all associated GPU resources immediately.
- [Dedicated Get Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/get-spec.md): Retrieve the full specification of a Friendli Dedicated Endpoint by ID, including model config, GPU type, replica count, and deployment settings.
- [Dedicated Get Endpoint Status](https://friendli.ai/docs/openapi/dedicated/endpoint/get-status.md): Check the current status of a Friendli Dedicated Endpoint by ID. Returns the lifecycle state such as running, sleeping, initializing, or terminated.
- [Dedicated Get Endpoint Version](https://friendli.ai/docs/openapi/dedicated/endpoint/get-version.md): Retrieve the version history of a Friendli Dedicated Endpoint by ID. View past configurations and rollback points for deployment tracking.
- [Dedicated List Endpoints](https://friendli.ai/docs/openapi/dedicated/endpoint/list.md): List all Friendli Dedicated Endpoint deployments in your project. Returns endpoint IDs, statuses, model names, and GPU configurations.
- [Dedicated Restart Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/restart.md): Restart a failed or terminated Friendli Dedicated Endpoint by ID. The endpoint re-initializes with the same model and GPU configuration.
- [Dedicated Sleep Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/sleep.md): Put a Friendli Dedicated Endpoint into sleep mode by ID. The endpoint stops serving but retains its configuration for quick wake-up later.
- [Dedicated Terminate Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/terminate.md): Terminate a running Friendli Dedicated Endpoint by ID. Stops all inference and releases GPU resources while preserving the endpoint configuration.
- [Dedicated Update Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/update.md): Update a Friendli Dedicated Endpoint with a new model, GPU type, or replica count. Changes are applied as a new version in the deployment history.
- [Dedicated Wake Endpoint](https://friendli.ai/docs/openapi/dedicated/endpoint/wake.md): Wake up a sleeping Friendli Dedicated Endpoint by ID. The endpoint resumes serving with its previous model and GPU configuration intact.
- [Dedicated Audio Transcriptions](https://friendli.ai/docs/openapi/dedicated/inference/audio-transcriptions.md): Transcribe an audio file into text.
- [Dedicated Audio Transcriptions Chunk Object](https://friendli.ai/docs/openapi/dedicated/inference/audio-transcriptions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Dedicated Endpoints when streaming the audio transcriptions API.
- [Dedicated Chat Completions](https://friendli.ai/docs/openapi/dedicated/inference/chat-completions.md): Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
- [Dedicated Chat Completions Chunk Object](https://friendli.ai/docs/openapi/dedicated/inference/chat-completions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Dedicated Endpoints when streaming the chat completions API.
- [Dedicated Chat Render](https://friendli.ai/docs/openapi/dedicated/inference/chat-render.md): Render a list of chat messages into the prompt text sent to the model.
- [Dedicated Completions](https://friendli.ai/docs/openapi/dedicated/inference/completions.md): Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
- [Dedicated Completions Chunk Object](https://friendli.ai/docs/openapi/dedicated/inference/completions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Dedicated Endpoints when streaming the completions API.
- [Dedicated Detokenization](https://friendli.ai/docs/openapi/dedicated/inference/detokenization.md): Convert a list of token IDs back into text.
- [Dedicated Embeddings](https://friendli.ai/docs/openapi/dedicated/inference/embeddings.md): Generate an embedding vector from input text or token sequence.
- [Dedicated Image Edits](https://friendli.ai/docs/openapi/dedicated/inference/image-edits.md): Edit an image based on a text prompt.
- [Dedicated Image Generations](https://friendli.ai/docs/openapi/dedicated/inference/image-generations.md): Generate an image from a text prompt.
- [Dedicated Messages](https://friendli.ai/docs/openapi/dedicated/inference/messages.md): Generate a model response from a conversation using the Anthropic Messages API format. Supports streaming, function tool calls, and extended thinking.
- [Dedicated Messages Chunk Object](https://friendli.ai/docs/openapi/dedicated/inference/messages-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Dedicated Endpoints when streaming the messages API.
- [Dedicated Responses](https://friendli.ai/docs/openapi/dedicated/inference/responses.md): Generate a model response from text or image inputs. Follows the OpenAI Responses API format, with support for streaming, function and custom tool calls, structured outputs, and reasoning controls.
- [Dedicated Responses Chunk Object](https://friendli.ai/docs/openapi/dedicated/inference/responses-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Dedicated Endpoints when streaming the responses API.
- [Dedicated Text Classification](https://friendli.ai/docs/openapi/dedicated/inference/text-classification.md): Classify text input into categories with per-class probabilities.
- [Dedicated Tokenization](https://friendli.ai/docs/openapi/dedicated/inference/tokenization.md): Convert text input into token IDs.
- [Dedicated Overview](https://friendli.ai/docs/openapi/dedicated/overview.md): API reference for Friendli Dedicated Endpoints. Browse inference, endpoint management, chat completions, responses, messages, completions, embeddings, and image generation endpoints.
- [Complete File Upload](https://friendli.ai/docs/openapi/file/complete-file-upload.md): Finalize a multipart file upload to Friendli Suite via the API. Call this endpoint after all file parts have been uploaded to mark the file as ready.
- [Get File Download URL](https://friendli.ai/docs/openapi/file/get-file-download-url.md): Get a pre-signed download URL for a file stored in Friendli Suite via the API. Use the returned URL to download the file with standard HTTP clients.
- [Get File Info](https://friendli.ai/docs/openapi/file/get-file-info.md): Retrieve metadata for a file stored in Friendli Suite by file ID via the API. Returns filename, size, upload status, and creation timestamp.
- [Initiate File Upload](https://friendli.ai/docs/openapi/file/init-file-upload.md): Start a new file upload to Friendli Suite via the API. Returns a file ID and pre-signed upload URL to begin transferring your file data.
- [File Overview](https://friendli.ai/docs/openapi/file/overview.md): API reference for Friendli File API. Upload, download, and manage files used for model training and dataset preparation on Friendli Suite.
- [API Reference](https://friendli.ai/docs/openapi/introduction.md): Complete API reference for Friendli Suite. Explore endpoints for Model APIs, Dedicated Endpoints, Container, File, and Dataset APIs with HTTP examples.
- [Model APIs Audio Transcriptions](https://friendli.ai/docs/openapi/model-apis/audio-transcriptions.md): Transcribe an audio file into text.
- [Model APIs Audio Transcriptions Chunk Object](https://friendli.ai/docs/openapi/model-apis/audio-transcriptions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Model APIs when streaming the audio transcriptions API.
- [Model APIs Chat Completions](https://friendli.ai/docs/openapi/model-apis/chat-completions.md): Generate a model response from a list of messages comprising a conversation. Compatible with the OpenAI Chat Completions API, with support for streaming, tool calls, and structured outputs.
- [Model APIs Chat Completions Chunk Object](https://friendli.ai/docs/openapi/model-apis/chat-completions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Model APIs when streaming the chat completions API.
- [Model APIs Chat Render](https://friendli.ai/docs/openapi/model-apis/chat-render.md): Render a list of chat messages into the prompt text sent to the model.
- [Model APIs Completions](https://friendli.ai/docs/openapi/model-apis/completions.md): Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
- [Model APIs Completions Chunk Object](https://friendli.ai/docs/openapi/model-apis/completions-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Model APIs when streaming the completions API.
- [Model APIs Detokenization](https://friendli.ai/docs/openapi/model-apis/detokenization.md): Convert a list of token IDs back into text.
- [Model APIs Messages](https://friendli.ai/docs/openapi/model-apis/messages.md): Generate a model response from a conversation using the Anthropic Messages API format. Supports streaming, function tool calls, and extended thinking.
- [Model APIs Messages Chunk Object](https://friendli.ai/docs/openapi/model-apis/messages-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Model APIs when streaming the messages API.
- [Model APIs Overview](https://friendli.ai/docs/openapi/model-apis/overview.md): API reference for Friendli Model APIs. Browse chat, responses, messages, completions, tokenization, and audio transcription endpoints.
- [Model APIs Responses](https://friendli.ai/docs/openapi/model-apis/responses.md): Generate a model response from text or image inputs. Follows the OpenAI Responses API format, with support for streaming, function and custom tool calls, structured outputs, and reasoning controls.
- [Model APIs Responses Chunk Object](https://friendli.ai/docs/openapi/model-apis/responses-chunk-object.md): Schema reference for the streamed chunk returned by Friendli Model APIs when streaming the responses API.
- [Model APIs Tokenization](https://friendli.ai/docs/openapi/model-apis/tokenization.md): Convert text input into token IDs.
- [Friendli Python SDK](https://friendli.ai/docs/sdk/python-sdk.md): Install and use the Friendli Python SDK to access Model APIs, Dedicated Endpoints, and Container. Covers chat, completions, streaming, and async usage.

## OpenAPI Specs

- [openapi](https://github.com/friendliai/friendli-openapi/raw/refs/heads/main/openapi.yaml)

## Optional

- [Home](https://friendli.ai)
- [Models](https://friendli.ai/models)
- [Pricing](https://friendli.ai/pricing)
- [Blog](https://friendli.ai/blog)