by maximhq
Fastest enterprise AI gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
# Add to your Claude Code skills
git clone https://github.com/maximhq/bifrostBifrost is a high-performance AI gateway that unifies access to 15+ 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.

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
No comments yet. Be the first to share your thoughts!
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:
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 and other features designed for enterprise-grade scale and reliability.
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 storages
│ ├── logstore/ # Request logging storages
│ └── 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
Choose the deployment method that fits your needs:
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
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
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 = "https://api.anthropic.com"
+ base_url = "http://localhost:8080/anthropic"
# Google GenAI SDK
- api_endpoint = "https://generativelanguage.googleapis.com"
+ api_endpoint = "http://localhost:8080/genai"
Learn More: Integration Guides
Bifrost adds virtually zero overhead to your AI requests. In sustained 5,000 RPS benchmarks, the gateway added only 11 µs of overhead per request.
| Metric | t3.medium | t3.xlarge | Improvement | |--------|-----------|-----------|-------------| | Added latency (Bifrost overhead) | 59 µs | 11 µs | -81% | | Success rate @ 5k RPS | 100% | 100% | No failed requests | | Avg. queue wait time | 47 µs | 1.67 µs | -96% | | Avg. request latency (incl. provider) | 2.12 s | 1.61 s | -24% |
Key Performance Highlights:
Complete Benchmarks: Performance Analysis