Here’s an Open-Source AI Gateway that’s 50X Faster than LiteLLM

It does not take much to get an LLM feature working. One provider, one API key, one model, done.

Keeping that setup clean as the system grows is a different story.

A second provider gets added. Some requests need to go to different models. Failures need fallbacks. Costs need limits. Teams want usage visibility. Security wants audit logs. Before long, model access is no longer a thin integration. It becomes a platform problem.

From my own experience working with AI systems, I would not build around a single model provider unless the use case is extremely narrow. I usually switch between models from OpenAI and Anthropic depending on the task, response quality, and cost. Once you start working that way, you need a reliable gateway sitting between your application and the model providers.

Bifrost is one way to handle that orchestration.

It is an AI gateway that provides a single OpenAI-compatible API in front of many providers, while also adding the things teams usually end up building around those providers anyway: routing, failover, semantic caching, virtual keys, observability, and governance controls.

What Bifrost actually is

Bifrost is a gateway layer for AI traffic. You put it between your application and your model providers, then send requests through one interface instead of wiring every provider directly into the app.

Bitfrost infrastructure

Bifrost infrastructure

Instead of spreading retry logic, provider selection, key management, cost controls, and monitoring across multiple services, those things can live in one place.

Bifrost’s overview page calls out fallback, weighted load balancing, routing, virtual keys, semantic caching, observability, Prometheus metrics, and OpenTelemetry support as part of the open-source feature set.

So the short version is this:

your app talks to Bifrost
Bifrost talks to model providers
the routing and control logic lives in the gateway instead of your app code

That is the basic idea.

How to Set Up Bifrost Locally

If you just want to see how Bifrost works, the local setup is the easiest place to start.

The command is simple:

npx -y @maximhq/bifrost

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If you need a specific transport version, use this command:

npx -y @maximhq/bifrost --transport-version v1.3.9

That starts the HTTP gateway locally on port 8080 by default. The setup page also documents flags such as -port, -host, -log-level, -log-style, and -app-dir.

Once the service is running, open the web interface at http://localhost:8080. The UI is used for provider setup, live configuration updates, request logs, metrics, analytics, virtual keys, and governance-related controls.

Bitfrost localhost UI

Bifrost localhost UI

After that, you can send a first request through the standard chat completions endpoint:

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello, Bifrost!"}]
  }'

If local setup is only the first step, there is also a Docker path in the same guide. For teams that need clustering, private networking, stronger governance, or larger production deployment options, the enterprise docs and product pages go further than the local and Docker setup.

Why AI gateway show up later than people expect

A lot of teams do not start by asking for an AI gateway.

They start by shipping a feature.

The gateway becomes necessary when the loose ends pile up. One provider has an outage. Another provider has a better model for a certain task. Internal teams need usage controls. Security asks for logs and permissions. Engineering wants fewer custom branches in the application code.

That is why the feature list matters less than the grouping behind it. Bifrost is trying to cover four areas that usually get handled in separate ways:

traffic management
governance and budgets
observability
deployment and availability

Read it that way, and the product makes more sense.

Unified API, but not just a unified API

The OpenAI-compatible surface means existing code can often stay mostly the same while the gateway handles provider routing behind the scenes. It maintains compatibility with SDK ecosystems and frameworks such as OpenAI, Anthropic, LiteLLM, LangChain, PydanticAI, Bedrock SDK, and Google GenAI.

That helps with migration, but the more useful part is what comes after migration.

Once your application talks to one stable endpoint, you can change routing decisions without rewriting the app every time the provider mix changes. You can move traffic, add fallbacks, or apply controls at the gateway layer instead of touching every service that makes model calls.

MCP support and tool control

Bifrost also has an MCP gateway side.

It can act as both an MCP client and server, connect to external tool servers, and expose tools to clients such as Claude Desktop. The same section also mentions tool filtering, OAuth support, tool execution controls, and agent mode.

Bitfrost MCP architecture

Bifrost MCP architecture

For many teams, the hard part is no longer just model access. It is tool access: which tools are available, who can use them, and what approval or security checks sit in the middle. Bifrost’s MCP features suggest it is trying to be relevant not only for model routing, but also for tool-facing agent systems.

For enterprise readers, that may be one of the more important sections of the product.

50x faster than LiteLLM

The GitHub repo describes Bifrost as “50x faster than LiteLLM,” and the benchmark page gives the numbers behind that claim.

Bitfrost performance comparison with LiteLLM

Bifrost performance comparison with LiteLLM

In the published 500 RPS test on AWS t3.medium, the benchmark page reports a 100% success rate for Bifrost versus 88.78% for LiteLLM. It reports P50 latency of 804 ms versus 38.65 s, P99 latency of 1.68 s versus 90.72 s, and max latency of 6.13 s versus 92.67 s. The same page also reports throughput of 424 req/s versus 44.84 req/s and peak memory of 120 MB versus 372 MB.

There is also a gateway-overhead section and a higher-throughput stress section.

Bitfrost performance comparison with LiteLLM

Bifrost performance comparison with LiteLLM

The benchmark says the published tests include 500 and 5,000 RPS scenarios, and the overview states that in sustained benchmarks at 5,000 requests per second, Bifrost adds only 11 microseconds of overhead per request.

That said, benchmark claims should be read as benchmark claims. They are useful, but they are still published by the vendor. The reasonable conclusion is not “these exact numbers will appear in every environment.” The reasonable conclusion is that Bifrost is engineered as a low-overhead gateway and that performance is clearly one of the project’s priorities.

Where Bifrost fits best

This open source tool makes the most sense for teams that already know they will have more than one model or more than one provider, or for teams that want one control point in front of AI traffic.

It focuses on provider unification, routing, key management, observability, semantic caching, and governance rather than just on API compatibility.

It is a reasonable fit for:

internal AI platforms shared across teams
SaaS products with model-backed features
enterprise environments that need logs, controls, and private deployment paths
agent systems that need model routing and tool governance in the same place

Final thoughts

Bifrost makes more sense when you look at it as infrastructure rather than just a model proxy.

It gives teams a single API in front of multiple providers, but that is only one part of its role. The more useful part is the operational layer around that API: routing, failover, observability, usage controls, caching, and governance. Those are the areas that usually become more important as AI systems expand across teams, products, and use cases.

That also lines up with my own experience.

I had been using Vercel AI Gateway for a long time, but I switched to Bifrost after the Vercel security breach. That was a reminder that companies should not only care about features, performance, or how quickly they can ship. They also need to pay attention to whether their infrastructure is secure, reliable, and something they can trust over time.

The LiteLLM comparison is still part of the discussion, especially because performance is one of the areas Bifrost emphasizes. Still, it is more than a benchmark result. What stands out more is how it brings provider access, traffic control, and operational tooling into one place.

For developers, that can mean less provider-specific logic in the application. For teams and enterprises, it can mean better visibility and more consistent control over how model access is handled.