LLM applications are rapidly becoming a critical part of production today. But behind the scenes, it's almost always the same thing, namely dozens of providers, different SDKs, keys, limits, backups, and more. One failure from the provider means that the entire AI layer is falling.
A concrete example, we all start with OpenAI, Anthropic and other providers, but most often several are used on large projects at once, which complicates the routing logic, application monitoring is spread across services. Yes, in general, support for such a complication takes up a huge amount of development team resources.
So, the Bifrost project appeared just to provide some kind of intermediate layer between your application and LLM providers. It unites more than 15 platforms under a single compatible API, which is convenient to integrate and easier to monitor, and most importantly, errors in the provider will not stop the entire application, since in this case another provider will be connected.
Let's get started!
👀 What exactly is Bifrost?
To be honest, I can describe the project simply and quickly. If you want a powerful LLM Gateway for your project, and at the same time deploy it in a user-friendly interface without setting up a bunch of configs and other things, then this project is for you.
To set up a project, just enter a command in the terminal and wait 5 seconds:
npx -y @maximhq/bifrost
After that, along the way http://localhost:8080 you will see the following interface:
On the left is a menu with a huge number of settings for your Gateway for the project. On the right is the content part. We are greeted by 6 tabs, from where we can conveniently copy a test request to the server and check the work.
⚙️ How to use it?
Let's connect our first LLM provider in literally 2 minutes and test its work. Let's go to the Model Providers tab (for example, select the popular OpenAI) and click on the "Add Key" button:
After that, select the model, enter the key and give a name to your key. I'll call it "My First Key" :)
After that, click on the save button and behold, our first Provider is connected! Now, we can send a test request by entering the following command in the terminal:
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!"}
]
}'
The first request should go and after that there should be objects with data about the successful request.
📊 Benchmark
Many may wonder about the advantages of Bifrost over other popular solutions. Let's take the most popular of them, LiteLLM. Let's run benchmarks and check how well they both cope in different tasks:
As you can see, in most popular tests, Bifrost outperforms LiteLLM in terms of performance. Now, let's present the throughput test in the form of a diagram:
~9.5x faster, ~54x lower P99 latency, and uses 68% less memory than LiteLLM — on t3.medium instance (2 vCPUs) with tier 5 OpenAI Key.
📦 Go-based architecture
Also, thanks to Go and its minimalistic architecture, Bifrost maintains stable latency even under peak loads, reducing the risk of degradation of the user experience with increasing AI traffic.
Key Performance Highlights:
- Perfect Success Rate - 100% request success rate even at 5k RPS
- Minimal Overhead - Less than 15 µs additional latency per request
- Efficient Queuing - Sub-microsecond average wait times
- Fast Key Selection - ~10 ns to pick weighted API keys
Also, thanks to this architecture, you can use Bifrost not only as an npx script, but also as a go package:
go get github.com/maximhq/bifrost/core@latest
This allows you to embed Bifrost directly into Go applications, integrating it into existing Go-based workflows without using Node.js.
✅ Functional features
Besides speed, Bifrost also offers features such as adaptive load balancing, semantic caching, unified interfaces, and built-in metrics. For example, the metrics look like this:
# Request metrics
bifrost_requests_total{provider="openai",model="gpt-4o-mini"} 1543
bifrost_request_duration_seconds{provider="openai"} 1.234
# Cache metrics
bifrost_cache_hits_total{type="semantic"} 892
bifrost_cache_misses_total 651
# Error metrics
bifrost_errors_total{provider="openai",type="rate_limit"} 12
And this is only a small part of what the package can do both under the hood and in integration with other tools!
💬 Feedback
If you have any questions about the project, our support team will be happy to answer them in the comments or on the Discord channel.
🔗 Useful links
You can find more materials on our project here:
Thank you for reading the article!
Top comments (7)
Interesting project
What makes this project different from the Vercel AI gateway and OpenRouter?
What do you think about this module?
This is interesting, and the problem it targets is very real — multi-provider LLM setups do get messy fast (keys, rate limits, failover, metrics, routing logic, etc.). A gateway layer like this makes a lot of sense once AI becomes a core production dependency, not just an experiment.
That said, I’m curious where you see the break-even point. For smaller teams or single-provider setups, the extra abstraction might be overkill, while for high-traffic or multi-tenant apps the reliability + observability gains are obvious. How do you usually advise teams to decide when introducing Bifrost is worth the added layer?
isnt multi llm provider dangerous as you can get very different outputs if it uses a failover if "A" is down and goes with "B", you might not get the response you are wanting and get inconsistent results.
This is a solid overview. Anyone who has run LLMs in production knows how painful provider outages and juggling multiple APIs can be. Having a single gateway with fallbacks and good performance makes a lot of sense. Curious to see how teams adopt this in real projects.
you guys just keep making the vps/cloud provider even more richer