Bifrost

Bifrost places a single API in front of every model provider an application might reach for. More than 23 of them, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, Groq, and Ollama, sit behind one OpenAI-compatible endpoint, so changing provider stops being a code change.

Starting it takes one command through npx or Docker, after which a built-in web interface handles configuration, live monitoring, and analytics. Providers can also be set up dynamically, through the API, or from files, and existing SDK code usually needs one line changed to point at the gateway.

The gateway layer is where the reliability and cost work happens.

• Automatic failover: requests move between providers and models when one of them fails.
• Load balancing: traffic is distributed across multiple API keys and providers.
• Semantic caching: responses are reused when a new request is close enough in meaning, cutting cost and latency.
• Governance: usage tracking, rate limiting, virtual keys, and budgets scoped to teams and customers.
• Observability: native Prometheus metrics, distributed tracing, and request logging.
• MCP tools: models call external tools such as filesystem access, web search, and databases.

The codebase is modular, separating core provider implementations, a framework layer for configuration, log, and vector storage, HTTP transports, and the web UI. Text, images, audio, and streaming all pass through the same interface. Private deployments add clustering, guardrails, an MCP gateway, and adaptive load balancing for teams running at scale.