Every LLM gateway we tested failed at scale – ended up building Bifrost

When you're building AI apps in production, managing multiple LLM providers becomes a pain fast. Each provider has different APIs, auth schemes, rate limits, error handling. Switching models means rewriting code. Provider outages take down your entire app.

At Maxim, we tested multiple gateways for our production use cases and scale became the bottleneck. Talked to other fast-moving AI teams and everyone had the same frustration - existing LLM gateways couldn't handle speed and scalability together. So we built [Bifrost](https://getmaxim.ai/bifrost).

What it handles:

Unified API - Works with OpenAI, Anthropic, Azure, Bedrock, Cohere, and 15+ providers. Drop-in OpenAI-compatible API means changing providers is literally one line of code.

Automatic fallbacks - Provider fails, it reroutes automatically. Cluster mode gives you 99.99% uptime.

Performance - Built in Go. Mean overhead is just 11µs per request at 5K RPS. Benchmarks show 54x faster P99 latency than LiteLLM, 9.4x higher throughput, uses 3x less memory.

Semantic caching - Deduplicates similar requests to cut inference costs.

Governance - SAML/SSO support, RBAC, policy enforcement for teams.

Native observability - OpenTelemetry support out of the box with built-in dashboard.

It's open source and self-hosted.

Anyone dealing with gateway performance issues at scale?