Bifrost is a free, open source ai development platforms project written in Go and released under Apache-2.0. It has 8,142 GitHub stars, 1,227 forks and 1,014 open issues, and was last pushed 3 hours ago. On this registry it ranks #50 of 61 tracked projects in AI Development Platforms, with 5 head-to-head comparisons available. It gained 146 stars over the last 6 tracked days.

What is Bifrost?

Bifrost is an open-source, Go-based AI gateway that puts 23+ model providers — OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama, Groq and more — behind one OpenAI-compatible API, built for teams running AI features in production that need failover, load balancing and caching without rewriting client code.

What it is

Bifrost is a high-performance AI gateway written in Go and licensed under Apache-2.0. It exposes a single OpenAI-compatible endpoint, POST /v1/chat/completions, and translates requests to whichever upstream provider a routing rule selects. It ships as a local binary or container, with a built-in web interface for configuration, real-time monitoring and analytics. Deployment is deliberately configuration-free at the start: the gateway boots and becomes usable before any provider keys are wired in.

The concrete problem it solves is provider sprawl. Applications that talk to several model vendors normally carry a separate SDK, key store, retry path, quota tracker and cost report for each one. Bifrost replaces that per-provider integration code with one gateway process that handles automatic fallbacks between providers and models, load balancing across multiple API keys, semantic caching, governance through virtual keys, and usage tracking. It also implements the Model Context Protocol so models can call external tools such as filesystem access, web search and databases through the same gateway.

Key capabilities

  • Unified OpenAI-compatible API over 23+ providers, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama and Groq.
  • Automatic fallbacks and retries between providers and models, plus load balancing across multiple API keys for intelligent request distribution.
  • Semantic caching that serves responses based on semantic similarity to lower cost and latency.
  • Multimodal and streaming support for text, images and audio behind one interface.
  • Model Context Protocol (MCP) client support for external tool use.
  • Governance features: virtual keys, usage tracking, rate limiting and fine-grained access control.
  • Custom plugins — an extensible middleware architecture for analytics, monitoring and custom logic — and a Go SDK for direct in-process integration.
  • Adaptive load balancing, cluster mode, guardrails and MCP gateway available in enterprise deployments.

Who uses it and how

  • Platform and infrastructure teams consolidating several provider integrations behind one endpoint instead of maintaining SDKs per vendor.
  • Enterprise teams running private deployments with custom security controls, private networking and governance for production AI systems at scale.
  • Cost and reliability owners routing traffic across providers and keys, relying on failover and semantic caching to avoid downtime and duplicate spend.
  • Observability and LLMOps teams adding usage tracking, rate limiting and analytics through the gateway rather than instrumenting each application.
  • Developers evaluating routing behaviour locally before committing to a deployment.

Getting started

Run npx -y @maximhq/bifrost for a local install, or docker run -p 8080:8080 maximhq/bifrost for a container, then open http://localhost:8080 to configure providers through the web UI. Setup guides cover both the HTTP gateway and the Go SDK.

How it compares

The project positions itself against LiteLLM, claiming to be 50x faster with under 100 µs of overhead at 5k RPS. Both are open-source gateways offering an OpenAI-compatible interface over many providers; Bifrost's differentiators in the stated facts are its Go runtime, its cluster mode, and enterprise features such as adaptive load balancing, guardrails and the MCP gateway.

When to use it — and when not to

A self-hoster operates the gateway process itself — binary or container — plus the http://localhost:8080 web interface, provider credentials and any plugin middleware; the README does not describe a managed hosting option, so operational responsibility stays with the deploying team. Adaptive load balancing, clustering, guardrails and the MCP gateway are gated behind enterprise deployments, so teams wanting those without a commercial arrangement should look elsewhere. The repository also carries over 1,000 open issues, and the README excerpt ends mid-section, so the public documentation is less complete than the feature list suggests.

project readme (upstream, from github) — read inline

Bifrost AI Gateway

maximhq%2Fbifrost | Trendshift

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The fastest way to build AI applications that never go down

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. Deploy in seconds with zero configuration and get automatic failover, load balancing, semantic caching, and enterprise-grade features.

Quick Start

Get started

Go from zero to production-ready AI gateway in under a minute.

Step 1: Start Bifrost Gateway

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

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

Step 2: Configure via Web UI

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

Step 3: Make your first API call

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!"}]
  }'

That's it! Your AI gateway is running with a web interface for visual configuration, real-time monitoring, and analytics.

Complete Setup Guides:


Enterprise Deployments

Bifrost supports enterprise-grade, private deployments for teams running production AI systems at scale. In addition to private networking, custom security controls, and governance, enterprise deployments unlock advanced capabilities including adaptive load balancing, clustering, guardrails, MCP gateway, and other features designed for enterprise-grade scale and reliability.

Explore enterprise capabilities


Key Features

Core Infrastructure

  • Unified Interface - Single OpenAI-compatible API for all providers
  • Multi-Provider Support - OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama, Groq, and more
  • Automatic Fallbacks - Seamless failover between providers and models with zero downtime
  • Load Balancing - Intelligent request distribution across multiple API keys and providers

Advanced Features

  • Model Context Protocol (MCP) - Enable AI models to use external tools (filesystem, web search, databases)
  • Semantic Caching - Intelligent response caching based on semantic similarity to reduce costs and latency
  • Multimodal Support - Support for text, images, audio, and streaming, all behind a common interface.
  • Custom Plugins - Extensible middleware architecture for analytics, monitoring, and custom logic
  • Governance - Usage tracking, rate limiting, and fine-grained access control

Enterprise & Security

  • Budget Management - Hierarchical cost control with virtual keys, teams, and customer budgets
  • User Provisioning (OIDC) - OAuth 2.0 / OIDC login with background directory sync for teams, roles, and business units
  • Observability - Native Prometheus metrics, distributed tracing, and comprehensive logging
  • Secrets Management - Secure API key management with environment variables and deployment secrets

Developer Experience


Repository Structure

Bifrost uses a modular architecture for maximum flexibility:

bifrost/
├── npx/                 # NPX script for easy installation
├── core/                # Core functionality and shared components
│   ├── providers/       # Provider-specific implementations (OpenAI, Anthropic, etc.)
│   ├── schemas/         # Interfaces and structs used throughout Bifrost
│   └── bifrost.go       # Main Bifrost implementation
├── framework/           # Framework components for data persistence
│   ├── configstore/     # Configuration storage backends
│   ├── logstore/        # Request logging storage backends
│   └── vectorstore/     # Vector storages
├── transports/          # HTTP gateway and other interface layers
│   └── bifrost-http/    # HTTP transport implementation
├── ui/                  # Web interface for HTTP gateway
├── plugins/             # Extensible plugin system
│   ├── governance/      # Budget management and access control
│   ├── jsonparser/      # JSON parsing and manipulation utilities
│   ├── logging/         # Request logging and analytics
│   ├── maxim/           # Maxim's observability integration
│   ├── mocker/          # Mock responses for testing and development
│   ├── semanticcache/   # Intelligent response caching
│   └── telemetry/       # Monitoring and observability
├── docs/                # Documentation and guides
└── tests/               # Comprehensive test suites

Getting Started Options

Choose the deployment method that fits your needs:

1. Gateway (HTTP API)

Best for: Language-agnostic integration, microservices, and production deployments

# NPX - Get started in 30 seconds
npx -y @maximhq/bifrost

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

Features: Web UI, real-time monitoring, multi-provider management, zero-config startup

Learn More: Gateway Setup Guide

2. Go SDK

Best for: Direct Go integration with maximum performance and control

go get github.com/maximhq/bifrost/core

Features: Native Go APIs, embedded deployment, custom middleware integration

Learn More: Go SDK Guide

3. Drop-in Replacement

Best for: Migrating existing applications with zero code changes

## OpenAI SDK
- base_url = "https://api.openai.com"
+ base_url = "http://localhost:8080/openai"

## Anthropic SDK
- base_url

readme truncated — read the full docs on github

Frequently asked questions

Is Bifrost free to use?

Bifrost is open source under the Apache-2.0 licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does Bifrost do?

Ultra-fast AI gateway with enterprise-grade reliability

What is Bifrost written in?

Bifrost is primarily written in Go. Its source is publicly available at https://github.com/maximhq/bifrost, and it has 8,142 GitHub stars.