If you’re building LLM applications at scale, your gateway can’t be the bottleneck. That’s why we built Bifrost, a high-performance, fully self-hosted LLM gateway in Go. It’s 50× faster than LiteLLM, built for speed, reliability, and full control across multiple providers.
Key Highlights:
Ultra-low overhead: ~11µs per request at 5K RPS, scales linearly under high load.
Adaptive load balancing: Distributes requests across providers and keys based on latency, errors, and throughput limits.
Cluster mode resilience: Nodes synchronize in a peer-to-peer network, so failures don’t disrupt routing or lose data.
Drop-in OpenAI-compatible API: Works with existing LLM projects, one endpoint for 250+ models.
Full multi-provider support: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, and more.
Automatic failover: Handles provider failures gracefully with retries and multi-tier fallbacks.
Semantic caching: deduplicates similar requests to reduce repeated inference costs.
Multimodal support: Text, images, audio, speech, transcription; all through a single API.
Observability: Out-of-the-box OpenTelemetry support for observability. Built-in dashboard for quick glances without any complex setup.
Extensible & configurable: Plugin based architecture, Web UI or file-based config.
Governance: SAML support for SSO and Role-based access control and policy enforcement for team collaboration.
Benchmarks : Setup: Single t3.medium instance. Mock llm with 1.5 seconds latency
| Metric | LiteLLM | Bifrost | Improvement |
|---|---|---|---|
| p99 Latency | 90.72s | 1.68s | ~54× faster |
| Throughput | 44.84 req/sec | 424 req/sec | ~9.4× higher |
| Memory Usage | 372MB | 120MB | ~3× lighter |
| Mean Overhead | ~500µs | 11µs @ 5K RPS | ~45× lower |
Why it matters:
Bifrost behaves like core infrastructure: minimal overhead, high throughput, multi-provider routing, built-in reliability, and total control. It’s designed for teams building production-grade AI systems who need performance, failover, and observability out of the box.x
Get involved:
The project is fully open-source. Try it, star it, or contribute directly: https://github.com/maximhq/bifrost