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

Source: Dev.to

LLM applications are rapidly becoming a critical part of production today

But behind the scenes it’s almost always the same thing: dozens of providers, different SDKs, keys, limits, backups, and more. One failure from a provider can bring the entire AI layer down.

A concrete example: we start with OpenAI, Anthropic, and other providers, but large projects often use several at once. This complicates routing logic, spreads application monitoring across services, and consumes a huge amount of development‑team resources.

Enter Bifrost – an intermediate layer between your application and LLM providers. It unites 15+ platforms under a single compatible API, making integration and monitoring easier. Most importantly, if one provider fails, another can take over, keeping the application alive.

👀 What exactly is Bifrost?

If you need a powerful LLM gateway that’s easy to deploy and doesn’t require a mountain of configuration, this project is for you.

Quick start

npx -y @maximhq/bifrost

After a few seconds open http://localhost:8080 – you’ll see the UI:

• Left – a menu with a huge number of settings for your gateway.
• Right – the main content area with six tabs that let you copy a test request and check the result.

⚙️ How to use it?

1. Add a provider (e.g., OpenAI) via the Model Providers tab and click Add Key.

   

2. Choose the model, paste your API key, and give it a name (e.g., “My First Key”).

   

3. Click Save – the provider is now connected.

4. Test the connection with a simple curl request:

   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!"}
              ]
            }'

   You should receive a JSON response containing the generated reply and request metadata.

📊 Benchmark

How does Bifrost compare to other popular solutions like LiteLLM? Below are the results of a series of benchmarks.

In most tests Bifrost outperforms LiteLLM. The throughput test visualized as a diagram:

Key take‑aways

• ~9.5× faster overall
• ~54× lower P99 latency
• 68 % less memory usage

All measured on a t3.medium instance (2 vCPUs) with a tier‑5 OpenAI key.

📦 Go‑based architecture

Built with Go’s minimalistic, high‑performance runtime, Bifrost maintains stable latency even under peak loads, reducing the risk of user‑experience degradation as AI traffic scales.

Ready to simplify your LLM integration?
Give Bifrost a try and enjoy a resilient, high‑performance gateway for all your AI models.

Key Performance Highlights

• Perfect Success Rate – 100 % request success rate even at 5 k RPS

• Minimal Overhead – 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:

• Adaptive load balancing
• Semantic caching
• Unified interfaces
• Built‑in metrics

Example Metrics

# 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

• GitHub repo –
• Website –
• Blog –

Thank you for reading the article!