LLM Gateways: The Straightforward Guide

TL;DR

• LLM gateways are the backbone of your AI stack. They standardize APIs, manage keys, add failovers, and let you swap models without rewriting everything.
• If you care about reliability, cost, or scaling, you need a gateway. If you want one that’s actually fast and easy, use Bifrost.
• Bifrost is an OpenAI-compatible gateway that connects you to 12+ providers in one shot. It adds almost zero latency, handles failover, load balancing, and semantic caching, and comes with a web UI.
• Benchmarks show Bifrost crushing 5,000 RPS with just 11 microseconds of added latency on t3.xlarge and a 100% success rate.
• Want to scale without headaches? Go with a real gateway. Want it to just work? Use Bifrost.

Quick Links:

• What is an LLM Gateway? (Deep Dive)
• Bifrost Product Page
• Bifrost GitHub
• Bifrost Benchmarks
• Bifrost Docs
• LLM Observability Guide
• What are AI Evals?
• Evaluation Workflows for AI Agents
• Book a Maxim Demo

What Is an LLM Gateway?

An LLM gateway is a layer that sits between your app and all those messy LLM providers. It gives you one API, handles keys and rate limits, manages failovers, and lets you switch models or providers without rewriting code. If you’re tired of duct-taping together a dozen SDKs, this is for you.

In plain English: An LLM gateway is like a universal remote for your AI models. Plug it in, and you control everything from one place.

Why Do You Need an LLM Gateway?

LLMs are everywhere. But in production, reality bites:

• Every provider has a different API, auth, and rate limits.
• Models get deprecated, costs change, outages happen.
• You want to A/B test, add fallbacks, hit SLAs, and actually track what’s happening.

You can hack together glue code for each provider. That’s fine until you want to scale or swap anything. Then it’s a mess.

An LLM gateway fixes all that. You get one API, one place to manage keys, traffic, failovers, budgets, and logs. You can finally stop firefighting and start shipping.

What Does an LLM Gateway Actually Do?

Here’s the checklist:

LLM Gateway vs. Simple Wrappers

Where Do LLM Gateways Fit in Your AI Stack?

Here’s how a real setup looks:

1. Your app talks to a single OpenAI-compatible endpoint.
2. The gateway routes to OpenAI, Anthropic, Bedrock, Vertex, Groq, or Mistral.
3. The gateway handles key rotation, rate limits, and failovers.
4. Metrics and logs get sent to your monitoring stack (and Maxim for deep AI evals).
5. Budgets and policies are set in one place.

Want to dig deeper? Check out LLM Observability, What are AI Evals?, and Evaluation Workflows for AI Agents.

Meet Bifrost: The Gateway That Just Works

Bifrost is open source, built for speed, and doesn’t mess around.

• One OpenAI-compatible API for over 12 providers: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, Ollama, Groq, and more.
• Automatic failover and load balancing.
• Semantic caching, multimodal support, streaming.
• Plugins for governance, logging, telemetry, and Maxim observability.
• Web UI for zero-config startup, real-time monitoring, and analytics.
• Drop-in replacement for OpenAI, Anthropic, and Google GenAI SDKs. Just change your base URL.
• Written in Go for ultra-low overhead.

See the Bifrost GitHub for the code and Bifrost Docs for setup.

Bifrost Performance: Real Numbers

Let’s cut to the chase. Here’s how Bifrost stacks up (Bifrost README and Bifrost Benchmarks):

• Bifrost can handle 5,000 requests per second on commodity hardware.
• 100% success rate, even under load.
• Just 11 microseconds of extra latency on a t3.xlarge.

Want to see the raw numbers? Read the full benchmarks.

When Should You Use a Gateway?

• You want provider redundancy and zero downtime.
• You need to optimize cost, speed, and quality across models.
• You have multiple teams or customers and need budgets, virtual keys, and governance.
• You want metrics, logs, and traces without wiring a dozen SDKs.

If any of that sounds like you, stop hacking together scripts. Use a gateway.

How to Use Bifrost (No Headaches)

Option 1: Gateway Mode (NPX or Docker)

1. Open the web UI:
   open http://localhost:8080

Make your first API call:

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!"}]
  }'

Start the gateway:

npx -y @maximhq/bifrost

or

docker run -p 8080:8080 maximhq/bifrost

Option 2: Drop-In for Your Existing SDK

Just change your base URL:

# OpenAI SDK
base_url = "http://localhost:8080/openai"
# Anthropic SDK
base_url = "http://localhost:8080/anthropic"
# Google GenAI SDK
api_endpoint = "http://localhost:8080/genai"

See the Integration Guides for more.

Option 3: Go SDK for Embedded Performance

go get github.com/maximhq/bifrost/core

Plug it into your Go app and add custom plugins as needed.

Core Bifrost Features (At a Glance)

• Unified Interface: One API for all your models.
• Multi-Provider Support: OpenAI, Anthropic, Bedrock, Vertex, Azure, Cohere, Mistral, Ollama, Groq, and more.
• Automatic Failover & Load Balancing: Stay online, even if a provider fails.
• Semantic Caching: Cut costs and latency with smart caching.
• Multimodal & Streaming: Handle text, images, audio, and streaming in one place.
• Governance & Budgeting: Virtual keys, budgets, usage tracking.
• Observability: Native Prometheus metrics, tracing, built-in dashboard.
• SSO & Vault Support: Google/GitHub auth, HashiCorp Vault integration.
• Plugins & Extensibility: Analytics, logging, monitoring, Maxim integration.
• Clustering & VPC Deployments: Enterprise-grade, private cloud ready.

Full feature list in the Bifrost Docs.

Feature Checklist for Picking a Gateway

Bifrost checks every box.

Real-World Example: Fallback and Routing

Here’s a sample config for a fallback chain and weighted routing:

{
  "providers": [
    {
      "name": "openai",
      "keys": [{"value": "env.OPENAI_API_KEY", "weight": 0.7}]
    },
    {
      "name": "anthropic",
      "keys": [{"value": "env.ANTHROPIC_API_KEY", "weight": 0.3}]
    }
  ],
  "fallback": ["openai", "anthropic"]
}

Want more? See the config docs for advanced routing, key pools, and plugin setup.

Operational Best Practices

• Centralize keys in the gateway. Use virtual keys and budgets by team.
• Define fallback chains and weighted routing to avoid downtime.
• Track latency and error budgets. Scrape metrics from the built-in Prometheus endpoint.
• Store logs for AI-specific analysis. For deep evals and tracing, connect Maxim.
• Run realistic load tests. Test streaming, big payloads, and tool usage.
• Version your gateway config. Roll forward and back without drama.
• Watch for model deprecations. Swap models safely, and always eval before you switch.

For more, check LLM Observability and AI Reliability.

From Gateway to Quality: Where Maxim AI Fits

Bifrost handles speed. Maxim AI handles quality. Here’s why you want both:

• Monitor all providers and models in one dashboard.
• Run automated evals for accuracy, safety, and consistency.
• Trace multi-agent workflows and see exactly where things break.
• Set budgets and governance at the team or customer level.

Case studies:

• Clinc: Banking AI Confidence
• Thoughtful: Smarter AI Flows
• Atomicwork: Enterprise Support at Scale

If you’re running Bifrost, turn on the Maxim observability plugin and get the full picture .

Common Pitfalls and How to Dodge Them

• Mixing SDK versions? Keep your endpoints aligned with the gateway.
• Underestimating rate limits? Distribute across keys and providers.
• No budget guardrails? Use virtual keys and set caps. Turn on alerts.
• Blind model swaps? Always eval with real traffic before switching.
• Missing traces? Turn on logging and tracing from day one.
• Ignoring deprecations? Version your config and keep your model list fresh.

FAQs

What is an LLM gateway?
An LLM gateway is middleware that gives you one API for all your LLM providers. It handles keys, failover, observability, and lets you swap models without rewriting your app. Learn more.

Why use a gateway instead of coding to OpenAI or Anthropic directly?
Because production changes. Gateways let you switch, add fallbacks, control costs, and monitor everything, no rewrites.

Is Bifrost OpenAI-compatible?
Yes. Just point your SDK to Bifrost’s endpoint and you’re set. See the docs.

How hard is it to deploy Bifrost?
It’s a one-liner with NPX or Docker. There’s a web UI for setup and live monitoring.

What about performance?
Bifrost adds just 11 microseconds at 5,000 RPS on t3.xlarge. 100% success rate. See benchmarks.

Does Bifrost support multiple providers?
Yes. OpenAI, Anthropic, Bedrock, Vertex, Azure, Cohere, Mistral, Ollama, Groq, and more.

Can I see metrics and traces?
Yes. Built-in Prometheus metrics, tracing, and logs. For deep AI evals, connect Maxim.

Can it control spend?
Yes. Virtual keys, budgets, and governance are built in.

Does it support caching?
Yes. Semantic caching is included.

Where can I read more?
Start with the LLM Gateway deep dive, Bifrost product page, Bifrost GitHub, and the Bifrost launch blog. For evals and monitoring, check Maxim AI’s articles. Book a demo if you want to see it live.

Putting It All Together

Here’s the playbook:

• Deploy Bifrost with NPX or Docker.
• Add your providers and keys in the web UI.
• Use weighted routing and fallbacks to keep uptime high.
• Scrape metrics and traces, export to your stack.
• Connect Maxim for evals and monitoring.
• Version your config and keep your model catalog fresh.
• Test everything before you switch defaults.

If you’re building AI for real, a gateway isn’t optional. Bifrost gives you speed, safety, and leverage, without the headaches.

References

• What is an LLM Gateway? (Deep Dive)
• Bifrost Product Page
• Bifrost: A Drop-In LLM Proxy, 40x Faster Than LiteLLM
• Bifrost GitHub README
• Bifrost Docs
• LLM Observability
• What are AI Evals?
• Evaluation Workflows for AI Agents
• AI Reliability
• Clinc Case Study
• Thoughtful Case Study
• Atomicwork Case Study
• Book a Maxim Demo

Last updated: September 10, 2025