Verified project record
maximhq/bifrost
Bifrost is a self-hosted, OpenAI-compatible AI gateway that unifies access to 23+ providers with automatic failover, load balancing, and semantic caching. It can be started in about 30 seconds via npx and managed through a built-in web interface.
Project overview
It reports near-zero gateway overhead (11 microseconds at 5k RPS) while covering 1000+ models and 23+ providers, and positions as a one-line drop-in replacement for OpenAI, Anthropic, and GenAI APIs.
- Project type
- MCP · Model Runtime · Infrastructure
- Use cases
- Coding & Development
- Deployment
- Refer to project documentation
- License
- Apache-2.0
Best for
- Developers and AI engineers who need one OpenAI-compatible interface over multiple providers and want failover and load balancing for reliability.
- Enterprise teams that need usage tracking, rate limiting, and fine-grained access control around model API traffic.
- Teams already using the OpenAI, Anthropic, Google GenAI, AWS Bedrock, LiteLLM, or Langchain SDKs that can switch endpoints with minimal code changes.
Key capabilities
- Provides a single unified, OpenAI-compatible API interface for all supported providers.
- Supports multiple providers, including for example OpenAI, Anthropic, AWS Bedrock, Google Vertex, and Azure, among the documented 23+ providers.
- Fails over between providers and models with the goal of zero downtime when a provider becomes unavailable.
- Distributes requests across multiple API keys and providers.
- Caches responses based on semantic similarity to reduce costs and latency.
- Enables AI models to use external tools such as filesystem, web search, and databases via MCP.
- Built-in web interface for visual configuration, real-time monitoring, and analytics.
- Usage tracking, rate limiting, and fine-grained access control.
Limitations and risks
- Unless a local provider such as Ollama is configured, model inference is served by remote, generally paid APIs including OpenAI, Anthropic, AWS Bedrock, and Google Vertex. Running the gateway locally does not change where inference occurs.
- No telemetry behavior is documented in the supplied material, so teams with data-reporting requirements should verify this independently before deployment.
- The 11 microsecond overhead at 5k RPS and the 1000+ models / 23+ providers figures are reported by the project; teams with strict latency requirements should benchmark against their own traffic mix.
Getting started
- Documented first-success path: run 'npx -y @maximhq/bifrost', open http://localhost:8080, and configure providers via the web UI. Setup difficulty is documented as easy, with zero-config startup and a 30-second setup via npx.
Alternatives and comparisons
- A related multi-provider LLM gateway project; compare its routing and management features against Bifrost for your use case.
- A related project that aggregates multiple providers behind a unified interface; compare provider coverage and deployment model before choosing.
Project comparisons
Evidence and sources
- GitHub project description: Fastest enterprise AI gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
- README: Bifrost is a high-performance AI gateway that unifies access to 23+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, and more) through a single OpenAI-compatible API. Dep…
- README: Go from zero to production-ready AI gateway in under a minute.
- README: npx -y @maximhq/bifrost
- README: docker run -p 8080:8080 maximhq/bifrost
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