Bifrost is a production-grade LLM gateway from maximhq that consolidates multi-provider AI access behind one OpenAI-compatible endpoint. Written in Go, it adds under 15 µs of overhead per request and sustains 5,000 requests per second with a 100% success rate in benchmarks — roughly 50x faster than LiteLLM at P99 latency where Python's GIL compounds under load. Supports OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Gemini, Groq, Mistral, Cohere, Cerebras, and Ollama. Ships with semantic caching, intelligent provider fallback, virtual keys with hierarchical budgets, real-time guardrails, and SSO via Google and GitHub. The MCP integration layer enables external tool access across all connected providers without per-provider configuration. Apache 2.0 for the core; enterprise tier adds vault support, in-VPC deployment, clustering, and federated MCP authentication.
Bifrost solves a problem that every team running AI at scale hits: managing multiple LLM providers means juggling separate SDKs, rate limit strategies, and failover logic per vendor. Bifrost collapses all of that into a single Go service that speaks OpenAI’s API dialect — so any existing code that calls OpenAI works immediately.
The performance gap versus LiteLLM is the headline, but the operational story matters more in production. At 5k RPS, Python-based gateways accumulate latency from async overhead and GIL contention. Bifrost’s Go implementation eliminates both — keeping overhead under 15 µs per request even under sustained load.
Unified provider routing: One API endpoint for OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Gemini, Groq, Mistral, Cohere, Cerebras, and Ollama. Model selection is configuration, not code — swap providers or add fallbacks without touching application logic.
Intelligent fallback: Define ordered fallback chains per model tier. When Claude Opus hits a rate limit, Bifrost falls over to Gemini 3.1 Pro or a self-hosted Ollama instance automatically. Circuit breakers prevent cascading failures from degraded providers.
Semantic caching: Bifrost detects semantically equivalent prompts and returns cached responses, cutting both latency and spend for repeated or near-identical queries — common in RAG pipelines and agent loops.
Budget governance: Virtual keys let you assign per-team, per-project, or per-user spending limits enforced at the gateway level. Guardrails reject or transform requests before they hit the provider, blocking prompt injection, PII leakage, or policy violations in real time.
MCP integration: Connect external tools (file systems, databases, APIs) once at the gateway. All downstream providers can invoke them without per-provider configuration — a meaningful simplification for multi-agent architectures.
docker compose up -d # zero-config startHelm chart available for Kubernetes. Single binary build for bare-metal. Configuration is a single YAML file — providers, virtual keys, fallback chains, and caching settings.
Engineering teams running multi-provider AI infrastructure who need low-latency routing, failover, and cost controls without maintaining a Python-based gateway under load. Also useful for teams consolidating from direct provider SDKs to a single internal API that survives vendor rate limits or outages.
Persistent memory layer for AI coding agents — benchmark-backed (95.2% on LongMemEval-S), 92% fewer tokens per session vs full-context pasting, zero manual memory.add() calls.
Open-source AI pair programming tool that works in your terminal to edit code across your entire repository.
AWS's AI-powered coding assistant that helps developers build, deploy, and optimize applications on AWS with code generation and transformation.