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.<p>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](<a href="https://getmaxim.ai/bifrost" rel="nofollow">https://getmaxim.ai/bifrost</a>).<p>What it handles:<p>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.<p>Automatic fallbacks - Provider fails, it reroutes automatically. Cluster mode gives you 99.99% uptime.<p>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.<p>Semantic caching - Deduplicates similar requests to cut inference costs.<p>Governance - SAML/SSO support, RBAC, policy enforcement for teams.<p>Native observability - OpenTelemetry support out of the box with built-in dashboard.<p>It's open source and self-hosted.<p>Anyone dealing with gateway performance issues at scale?