Bifrost is an AI gateway that puts more than 23 model providers, including OpenAI, Anthropic, AWS Bedrock, and Google Vertex, behind a single OpenAI-compatible API. Applications send every request to Bifrost, which routes it to a provider, fails over to another provider or model when one is down, and balances load across multiple API keys and providers.
Bifrost runs as an HTTP gateway with a built-in web UI for configuration, monitoring, and analytics, or embeds directly into Go programs through its Go SDK. It also works as a drop-in replacement for the OpenAI, Anthropic, and Google GenAI SDKs: an existing application changes only its base URL. Providers are configured through the web UI, an API, or a configuration file. Plugins add governance, logging, semantic caching, telemetry, and mock responses for testing.
Beyond routing, Bifrost caches responses by semantic similarity, gives models access to external tools through the Model Context Protocol (MCP), and governs usage with virtual keys, rate limits, and hierarchical budgets per team or customer. Observability comes through Prometheus metrics, distributed tracing, and request logs. Private deployments add adaptive load balancing, clustering, guardrails, an MCP gateway, and OIDC user provisioning.
In sustained benchmarks at 5,000 requests per second, Bifrost reports:
| Metric | t3.medium | t3.xlarge |
|---|---|---|
| Added latency (Bifrost overhead) | 59 µs | 11 µs |
| Success rate at 5k RPS | 100% | 100% |
| Average queue wait time | 47 µs | 1.67 µs |
| Average request latency (including provider) | 2.12 s | 1.61 s |
Features
- Unified interface: one OpenAI-compatible API for every configured provider
- Multi-provider support: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama, Groq, and more
- Automatic fallbacks: fails over between providers and models when a request fails
- Load balancing: distributes requests across multiple API keys and providers
- Semantic caching: returns cached responses for semantically similar requests to cut cost and latency
- MCP: lets models call external tools such as file systems, web search, and databases
- Drop-in replacement: OpenAI, Anthropic, and Google GenAI SDKs switch over by changing the base URL
- Governance and budgets: virtual keys, rate limits, access control, and hierarchical budgets for teams and customers
- Observability: Prometheus metrics, distributed tracing, and request logging
- Custom plugins: middleware for analytics, monitoring, and custom logic
- Deployment options: an HTTP gateway with web UI, or a Go SDK for embedding
- Private deployments: clustering, adaptive load balancing, guardrails, and OIDC user provisioning
