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The Problem
If you’ve ever built production AI applications, you know the pain: you need a gateway to route requests across multiple LLM providers, handle failover, manage rate limits, and get observability. Most teams reach for LiteLLM.
But here’s what nobody talks about: LiteLLM becomes a bottleneck at scale.
We hit this wall hard. At 500 RPS, LiteLLM’s P99 latency spiked to multiple seconds. In some cases, requests took 4 minutes. For a component that’s supposed to be invisible infrastructure, that’s unacceptable.
So we built Bifrost.
The Results
After months of engineering, here’s what we shipped:
<ul>
<li>50x faster than LiteLLM (P99 latency)</li>
<li>11μs overhead at 5,000 RPS</li>
<li>68% less memory consumption</li>
<li>100% open source (MIT license)</li>
</ul>
Not marketing numbers. Real benchmarks on identical hardware, <a href="https://github.com/maximhq/bifrost" target="_blank" rel="noopener nofollow">published on GitHub</a>.
Why Go?
We wrote Bifrost in Go because latency matters. Every microsecond your gateway adds is latency your users feel. Python’s async overhead and GIL limitations make it fundamentally unsuitable for high-throughput proxy workloads.
Go gives us:
<ul>
<li>True concurrency with goroutines</li>
<li>Predictable memory management</li>
<li>Native HTTP/2 support</li>
<li>Battle-tested standard library</li>
</ul>
Architecture Highlights
1. Asynchronous everything
Logging, metrics, plugin execution - all non-blocking. Your LLM requests never wait.
2. Plugin system
Extend Bifrost without forking. Pre-hooks and post-hooks for custom logic:
go
<code>func PreHook(ctx context.Context, req *schemas.CompletionRequest) error { *// Custom auth, rate limiting, request modification* return nil }</code>
3. Built-in observability
Every request traced automatically. Latency, tokens, costs - captured with zero performance impact.
4. Adaptive load balancing
Automatically adjusts traffic based on API key performance. Degraded keys get less traffic, healthy ones get more.
What Makes It Production-Ready
<ul>
<li>In-VPC deployment: Run entirely in your private cloud</li>
<li>Enterprise guardrails: Integrate AWS Bedrock, Azure Content Safety</li>
<li>Real-time monitoring: Prometheus metrics out of the box</li>
<li>Audit logs: SOC 2 / GDPR compliant logging</li>
<li>MCP support: Model Context Protocol for tool calling</li>
</ul>
Open Source from Day One
We didn’t build Bifrost to sell licenses. We built it because we needed it, and we’re open-sourcing it because every AI team hits this problem.
<a href="https://github.com/maximhq/bifrost" target="_blank" rel="noopener nofollow">⭐ Star Bifrost on GitHub</a>
The repo includes:
<ul>
<li>Complete source code</li>
<li>Benchmark suite (run it yourself)</li>
<li>Docker compose setup</li>
<li>Production deployment guides</li>
<li>Comparison tests vs LiteLLM</li>
</ul>
Getting Started (60 seconds)
bash
<code>git clone https://github.com/maximhq/bifrost cd bifrost docker compose up</code>
Add your API keys via the UI at <code>localhost:8080</code>, and you’re routing requests.
When to Use Bifrost
Use Bifrost if you:
<ul>
<li>Route requests across multiple LLM providers</li>
<li>Need &lt;50ms P99 latency for your gateway</li>
<li>Run high-throughput production workloads (1k+ RPS)</li>
<li>Want observability without performance cost</li>
<li>Need enterprise compliance (SOC 2, HIPAA, GDPR)</li>
</ul>
Stick with direct API calls if you:
<ul>
<li>Have a simple app with one provider</li>
<li>Process &lt;100 requests/day</li>
<li>Don’t need failover or load balancing</li>
</ul>
The Benchmark Details
We published <a href="https://github.com/maximhq/bifrost/tree/main/benchmarks" target="_blank" rel="noopener nofollow">full benchmarks</a> you can reproduce:
<code>MetricBifrostLiteLLMP99 Latency (500 RPS)520ms28,000msMemory (5k RPS)1.4GB4.3GBOverhead11μs~600μsMax RPS (stable)5,000+&lt;1,000</code>
At 500 RPS on identical t3.xlarge instances, LiteLLM breaks. Bifrost keeps running.
Contributing
Bifrost is MIT licensed and we welcome contributions:
<ul>
<li>Plugin development: Share your custom plugins</li>
<li>Provider integrations: Add new LLM providers</li>
<li>Performance improvements: Help us go faster</li>
<li>Documentation: Improve guides and examples</li>
</ul>
<a href="https://github.com/maximhq/bifrost" target="_blank" rel="noopener nofollow">Check out the repo →</a>
What’s Next
We’re actively working on:
<ul>
<li>Streaming optimization: Even lower latency for streaming responses</li>
<li>More guardrails: Additional safety provider integrations</li>
<li>Enhanced caching: Semantic caching for cost reduction</li>
<li>Community plugins: Plugin marketplace</li>
</ul>
Try It Today
The fastest way to see the difference:
bash
`# Clone and run
git clone <a href="https://github.com/maximhq/bifrost" target="_blank" rel="noopener nofollow">https://github.com/maximhq/bifrost</a>
cd bifrost &amp;&amp; docker compose up
# Visit localhost:8080# Add API keys# Start routing`
If Bifrost saves you time (or money on LLM gateway costs), give us a star on GitHub. It helps other teams discover the project.
<a href="https://github.com/maximhq/bifrost" target="_blank" rel="noopener nofollow">⭐ Star Bifrost</a> | <a href="https://docs.getbifrost.ai/" target="_blank" rel="noopener nofollow">Read the docs</a> | <a href="https://discord.gg/maxim" target="_blank" rel="noopener nofollow">Join Discord</a>

Pranay Batta

Bifrost is an open-source LLM gateway built in Go that claims 50x faster P99 latency than LiteLLM, with only 11μs overhead at 5,000 RPS and 68% less memory consumption. The project addresses performance bottlenecks in production AI applications by leveraging Go's concurrency model, featuring asynchronous operations, a plugin system, built-in observability, and adaptive load balancing. It supports multi-provider routing, failover handling, enterprise compliance features, and includes comprehensive benchmarks showing LiteLLM breaking at 500 RPS while Bifrost remains stable at 5,000+ RPS.

We built an LLM gateway 50x faster than LiteLLM (and it's open source)