Bifrost: Open-Source AI Gateway for LLM Providers

What Bifrost is

Bifrost is an AI gateway from Maxim that sits between an application and the model providers it calls. Its README describes it as a high-performance gateway that unifies access to 23+ providers, including OpenAI, Anthropic, AWS Bedrock and Google Vertex, behind a single OpenAI-compatible API. Instead of wiring each provider SDK, API key and retry policy into application code, clients point at Bifrost and the gateway handles routing, failover, load balancing across keys, caching and usage controls.

The audience is teams that already call more than one LLM provider, or expect to, and want those calls to survive a provider outage or rate limit without code changes. It also targets platform teams who need to hand out model access across an organization while keeping spend under control. Bifrost is written in Go and runs either as a standalone HTTP gateway with a built-in web UI, or as a Go library embedded directly in a service.

How Bifrost works

The repository is split into modules that map closely onto the request path:

• core/ holds the main Bifrost implementation, provider-specific adapters under core/providers/, and the shared schemas used throughout the codebase.
• framework/ contains persistence components: a config store, a log store for request logging, and vector stores.
• transports/bifrost-http/ is the HTTP gateway layer that exposes the OpenAI-compatible API.
• ui/ is the web interface served by the HTTP gateway for configuration, monitoring and analytics.
• plugins/ contains governance (budgets and access control), logging, semantic caching, telemetry, a mocker for fake responses, a JSON parser utility, and an integration with Maxim's observability platform.

A request arrives at the gateway, names a provider and model using a provider/model scheme (the quick-start example uses openai/gpt-4o-mini), passes through any enabled plugins, and is forwarded upstream using a selected API key. If the call fails, configured fallbacks retry against another provider or model. Because plugins are middleware, governance, caching and logging are composable pieces rather than hard-wired behaviour, and the README describes the plugin system as extensible for custom analytics, monitoring and logic.

Bifrost also exposes provider-shaped endpoints so existing SDKs can be redirected with only a base URL change: the /openai, /anthropic and /genai paths on the gateway stand in for the respective upstream APIs. That is what the README calls drop-in replacement.

Key features

• Unified OpenAI-compatible API: one request format for every configured provider.
• Multi-provider support: the README lists OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama and Groq, among others.
• Automatic fallbacks: failover between providers and models when an upstream call fails.
• Load balancing: request distribution across multiple API keys and providers, with weighted key selection.
• Semantic caching: responses are cached by semantic similarity rather than exact string match, to cut cost and latency on repeated questions.
• Model Context Protocol: models routed through Bifrost can use external tools such as filesystem access, web search or databases via MCP.
• Multimodal and streaming: text, images, audio and streaming behind a common interface.
• Governance and budgets: virtual keys, rate limiting, usage tracking and hierarchical budgets across teams and customers.
• Observability: native Prometheus metrics, distributed tracing and request logging.
• Flexible configuration: web UI, API-driven or file-based config, with API keys referenced from environment variables instead of stored in plain config.
• SDK integrations: documented integrations for the OpenAI, Anthropic, AWS Bedrock and Google GenAI SDKs, plus LiteLLM and LangChain.

On performance, the README states that in sustained 5,000 RPS benchmarks the gateway added 11 µs of overhead per request on a t3.xlarge instance and 59 µs on a t3.medium, with a 100% success rate at that load and roughly 10 ns to pick a weighted API key. These are the project's own numbers; the linked benchmarking docs describe the methodology, and it is worth reproducing them against a realistic traffic profile before relying on them for capacity planning.

Getting started

The fastest route is the NPX launcher or the Docker image:

# Install and run locally
npx -y @maximhq/bifrost

# Or use Docker
docker run -p 8080:8080 maximhq/bifrost

For a Docker setup that keeps configuration and logs across restarts, the README mounts a data directory:

docker run -p 8080:8080 -v $(pwd)/data:/app/data maximhq/bifrost

Once it is running, open the web UI to add providers and keys:

# Open the built-in web interface
open http://localhost:8080

A first request then looks like an ordinary OpenAI chat completion, with the provider prefixed to the model name:

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello, Bifrost!"}]
  }'

To embed Bifrost in a Go service rather than run it as a separate process, pull in the core module:

go get github.com/maximhq/bifrost/core

For existing applications the drop-in path is a base URL swap. With the OpenAI SDK, https://api.openai.com becomes http://localhost:8080/openai; Anthropic and Google GenAI clients follow the same pattern with /anthropic and /genai. The README positions the HTTP gateway as the option for language-agnostic and microservice setups, and the Go SDK for direct integration where you want the gateway logic in-process.

Use cases

• Provider redundancy for production apps: configure a primary model plus fallbacks so an outage or rate limit at one provider is absorbed by another without an application deploy.
• Key pooling: spread traffic across several API keys for the same provider to stay under per-key rate limits.
• Internal model platform: issue virtual keys per team or customer, set budgets and rate limits, and track usage centrally instead of sharing raw provider keys.
• Cost control on repetitive traffic: support bots and FAQ-style workloads, where semantic caching can answer near-duplicate questions without a provider call.
• Leaving a single provider: move an app written against the OpenAI or Anthropic SDK onto a multi-provider setup by changing its base URL.
• Local development and tests: the mocker plugin returns mock responses, which avoids paid calls in CI or while building UI against model output.
• Tool-using models: connect MCP tool servers once at the gateway instead of in every client.

How Bifrost compares

The closest open-source comparison is the LiteLLM proxy, which is widely used for the same job: an OpenAI-compatible API over many providers with fallbacks, keys and budgets. The visible differences are runtime and packaging. Bifrost is a Go binary with an embedded web UI and a Go SDK, while LiteLLM is a Python project. Bifrost's docs also list a LiteLLM SDK integration, so the two are not strictly either/or. Because Bifrost's latency figures are self-reported, anyone choosing between gateways should benchmark both on representative payloads rather than rely on either project's published numbers.

Bifrost also overlaps with hosted gateways offered by routing and observability vendors. The main distinction is that Bifrost is self-hostable under Apache-2.0, so prompts, responses and provider keys stay on your own infrastructure. It is a gateway, not an inference server: it forwards to providers or to self-hosted endpoints such as Ollama rather than running model weights itself.

Things to know before adopting

• Open-source and enterprise split: the README describes enterprise deployments that add adaptive load balancing, clustering, guardrails, an MCP gateway and private networking, and several doc links (custom plugins, clustering, OIDC user provisioning) sit under an enterprise section. Confirm the features you need are in the open-source build before committing.
• Vendor integration: one bundled plugin targets Maxim's own observability product. Prometheus metrics, tracing and logging are listed as native features, so that plugin is optional.
• Branching: the default branch is dev, so pin to tagged releases or published images rather than tracking the default branch.
• Persistence: the gateway uses config and log stores; mount a volume (as in the README's Docker example) or configured state is lost when the container restarts.
• Maturity: the repository was created in March 2025, which makes it young relative to some gateways in the same category. Expect configuration and plugin APIs to keep moving.
• Deployment targets: NPX, Docker and a Kubernetes production deployment guide are documented.

Project activity

As of October 2026 the repository has roughly 8,500 stars. It was created on 19 March 2025, is written primarily in Go, and is licensed under Apache-2.0. The source is at github.com/maximhq/bifrost, the product homepage is getmaxim.ai/bifrost, and full documentation lives at docs.getbifrost.ai. Community support runs through the project's Discord.