Find your build path

Choose your build track on Livepeer: AI inference, real-time video AI, video streaming, custom containers, or OSS contribution.

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Livepeer supports AI inference, real-time AI video, video transcoding, custom containerised models, and protocol-level contribution. These paths share infrastructure but have different APIs, SDKs, and architectural requirements.

What are you building?

Call AI inference from my application

Path: AI Gateway API via Studio

You send REST requests to a Livepeer gateway and receive processed outputs. No GPU required. The Studio-managed gateway handles routing to the network.

Nine batch pipelines are available: text-to-image, image-to-image, image-to-video, image-to-text, audio-to-text, text-to-speech, upscale, segment-anything-2, and LLM.

curl -X POST https://livepeer.studio/api/beta/generate/text-to-image \
  -H "Authorization: Bearer $LIVEPEER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a glowing neural network in a dark room", "model_id": "SG161222/RealVisXL_V4.0_Lightning"}'

Build real-time AI video (live webcam or stream effects)

Path: ComfyStream

ComfyStream applies ComfyUI workflows to live video streams at up to 25 FPS on an NVIDIA RTX 4090. Build your pipeline as a ComfyUI workflow JSON file; ComfyStream handles WebRTC transport and frame processing.

Requirements: NVIDIA GPU (RTX 3090 or better), ComfyUI, Python 3.12, CUDA 12.5+.

Supported models: StreamDiffusion, ControlNet, IPAdapter, FaceID, LoRA, Whisper (audio), Gemma (video understanding), SuperResolution.

Deploy a custom AI model or container

Path: BYOC (Bring Your Own Container)

Package your model in Docker, implement PyTrickle’s FrameProcessor interface, and register the container as a worker on the Livepeer network. Your container receives and publishes live video frames over the trickle streaming protocol.

Requirements: Docker on Linux with NVIDIA GPU, Python model code (PyTorch recommended).

BYOC reached production-grade in Phase 4 (January 2026). Embody SPE and Streamplace run production BYOC workloads today.

Integrate video transcoding or streaming

Path: Studio API — Video

The Studio REST API and SDKs manage the full lifecycle of livestreams and VOD assets: create streams, get ingest URLs, transcode assets, manage playback, and set access control.

SDKs: npm install livepeer / pip install livepeer / go get github.com/livepeer/livepeer-go

Free tier: 1,000 transcoding minutes per month. Growth: $100/month minimum.

Contribute to the Livepeer protocol codebase

Path: OSS Contribution

Four primary repositories: go-livepeer (protocol node, Go), ai-runner (AI inference runtime, Python), ComfyStream (real-time AI video, Python), protocol (Solidity contracts).

Requirements: Go 1.21+ for go-livepeer; Python 3.12 for ComfyStream and PyTrickle; local testnet for integration testing.

Evaluate Livepeer for my use case

Path: Evaluation

Start with the developer stack concept page for the three access layers, then the workload fit page to match your use case to a path.

Key questions:

• Batch vs real-time AI — different APIs, latency, and infrastructure
• Studio-managed vs self-hosted gateway — managed is simpler; self-hosted controls cost at scale
• Standard pipelines vs custom model — BYOC for fully custom; gateway API for supported pipelines

then

Build path reference

Related pages

Concepts

Understand the developer stack, AI pipelines, video infrastructure, and the OSS codebase.

Get Started

Quickstarts for AI inference, ComfyStream, video transcoding, and OSS contribution.

Custom AI Workflows

Deep guides for BYOC, ComfyStream, SDK integration, and model support.