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Bifrost: The fastest way to build AI applications that never go down

AI Summary5 min read

TL;DR

Bifrost is an LLM Gateway that simplifies AI application development by unifying multiple LLM providers under one API. It offers high performance, reliability with automatic failover, and easy deployment via npx or Go package.

Key Takeaways

  • •Bifrost acts as an intermediate layer between applications and LLM providers, unifying over 15 platforms under a single API to simplify integration and monitoring.
  • •It ensures application reliability by automatically switching to another provider if one fails, preventing complete AI layer downtime.
  • •Benchmarks show Bifrost significantly outperforms alternatives like LiteLLM with ~9.5x faster throughput, ~54x lower P99 latency, and 68% less memory usage.
  • •Built with Go-based architecture, it maintains stable performance under load with 100% success rate at 5k RPS and minimal latency overhead.
  • •Offers features like adaptive load balancing, semantic caching, unified interfaces, and comprehensive metrics for production AI applications.

Tags

webdevaiprogrammingopensource

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!

Site

👀 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
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After that, along the way http://localhost:8080 you will see the following interface:

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:

Provider

After that, select the model, enter the key and give a name to your key. I'll call it "My First Key" :)

Provider Saved

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!"}
    ]
  }'
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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:

Benchmark

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:

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.

Highlights

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
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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
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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!

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