Bifrost: The Fastest Open Source LLM Gateway

Source: Dev.to

TL;DR

Bifrost is an open‑source, high‑performance LLM gateway built in Go by Maxim AI that delivers 50× faster performance than LiteLLM with only 11 µs overhead at 5,000 requests per second. It offers zero‑configuration deployment, unified access to 12+ providers through an OpenAI‑compatible API, automatic failovers, semantic caching, and enterprise‑grade features. Available on GitHub under an open‑source license, Bifrost enables teams to build production‑ready AI applications without compromising on performance, flexibility, or control.

The Performance Challenge in Production AI

As AI applications move from prototype to production, the infrastructure layer becomes critical. Many teams discover that their LLM gateway becomes the bottleneck, adding hundreds of milliseconds of latency and consuming excessive memory at scale. Python‑based solutions, while convenient for rapid prototyping, struggle with the inherent limitations of the GIL (Global Interpreter Lock) and async overhead when handling thousands of concurrent requests.

Bifrost was built specifically to solve this performance problem. Written from the ground up in Go, it treats the gateway layer as core infrastructure that should add virtually zero overhead to AI requests.

Real Performance Numbers

The performance difference between Bifrost and alternatives isn’t marketing hype. Published benchmarks running on identical hardware reveal dramatic differences in production behavior.

• 500 RPS on AWS t3.xlarge: Bifrost maintains a P99 latency of 520 ms, while LiteLLM reaches 28 000 ms.
• 1 000 RPS: Bifrost remains stable with 1.2 s P99 latency; LiteLLM crashes due to memory exhaustion.
• Overhead: Bifrost adds just 11 µs per request at 5 000 RPS compared to ~600 µs for Python‑based alternatives.

This 50× performance advantage compounds at scale. For applications processing millions of daily requests, lower gateway overhead translates directly to better user experience, reduced infrastructure costs, and the ability to handle traffic spikes without degradation.

Zero‑Configuration Enterprise Features

Despite its exceptional performance, Bifrost requires no complex configuration. Installation takes seconds via Docker or npx, and the gateway dynamically discovers providers based on API keys. This zero‑config approach eliminates weeks of infrastructure setup while providing production‑grade capabilities from day one.

• Unified interface supports 12+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Cohere, Mistral AI, Ollama, Groq, etc.) through a single OpenAI‑compatible API.
• Drop‑in migration: Teams using existing OpenAI, Anthropic, or Google SDKs can migrate with a one‑line change, pointing their base URL to Bifrost’s endpoint.
• Automatic fallbacks & adaptive load balancing ensure applications stay online even when individual providers experience issues, routing around throttling and failures based on real‑time performance metrics.
• Semantic caching goes beyond traditional HTTP caching by understanding when prompts are semantically similar. This embedding‑based approach can reduce costs by up to 95 % for applications with repetitive queries (e.g., customer‑support bots, FAQ systems).

Open Source Flexibility with Enterprise Capability

Being open source on GitHub gives teams complete transparency and control over their AI infrastructure. The codebase is well‑structured with clear separation between core functionality, framework components, transport layers, and an extensible plugin system.

• Custom plugins enable teams to extend Bifrost without forking. The pre‑hook and post‑hook architecture allows implementing custom authentication, rate limiting, request modification, or analytics while maintaining upgrade compatibility.
• Enterprise features include hierarchical budget management with virtual keys, team‑level spending limits, and per‑customer quotas.
• SSO integration with Google and GitHub simplifies user management.
• Vault support provides secure API key management through HashiCorp Vault.

Advanced Capabilities for Modern AI Applications

• Model Context Protocol (MCP) support enables AI models to use external tools such as filesystem access, web search, and database queries, unlocking sophisticated agentic workflows where models autonomously gather information and execute actions.
• Native observability provides Prometheus metrics, distributed tracing, and comprehensive logging without performance impact. This integrates seamlessly with Maxim’s AI evaluation and monitoring platform, offering end‑to‑end visibility from development through production.
• Teams building multi‑agent systems benefit from combining Bifrost’s high‑performance gateway with Maxim’s agent simulation and evaluation tools, enabling testing across hundreds of scenarios, custom quality metrics, and production monitoring.

When to Choose Bifrost

Bifrost is the right choice when your application requires ultra‑low latency, handles high‑throughput workloads above 500 RPS, needs enterprise compliance features, or demands complete infrastructure control. The open‑source model provides transparency and flexibility while maintaining production‑grade reliability.

For teams prioritizing AI reliability and trustworthiness, Bifrost’s performance characteristics ensure the infrastructure layer never becomes a quality bottleneck. Combined with proper evaluation workflows and observability practices, teams can build AI applications that scale reliably from prototype to production.

The published benchmarks are fully reproducible, allowing teams to validate performance on their own hardware before committing. Getting started takes less than a minute with Docker, making it easy to evaluate whether Bifrost’s performance advantages matter for your specific use case.

Ready to experience production‑grade LLM infrastructure? Explore Bifrost’s documentation or schedule a demo to see how Maxim’s complete platform accelerates AI development.