If you're building production LLM applications, you've probably hit these problems:

• Managing multiple provider APIs (OpenAI, Anthropic, AWS, Google)
• Handling provider outages and rate limits
• Tracking costs across different models and providers
• Ensuring security and compliance

AI gateways solve these problems by sitting between your application and LLM providers. One API, multiple providers, built-in failover, cost tracking, and security features.

Here are the 5 AI gateways you should evaluate.

1. Bifrost by Maxim AI

What it is: High-performance AI gateway built for production deployments. Zero config, automatic failover, semantic caching built in.

Why you'd use it:

You need production-ready infrastructure without spending weeks on configuration. Bifrost gives you a unified API for 12+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, Ollama, Groq) with automatic failover and load balancing.

Key features:

• Zero-config startup: npx -y @maximhq/bifrost and you're running. Add provider keys through the web UI. No YAML files.
• Automatic failover: Primary provider down? Requests automatically route to backups. No manual intervention.
• Semantic caching: Intelligent caching based on meaning, not exact text matching. Reduces costs by up to 90% for common queries.
• MCP support: Enable AI models to use external tools (filesystem, web search, databases).
• Budget management: Set spending limits at organization, team, application, and provider levels.
• Drop-in replacement: Change one line of code to migrate from direct OpenAI/Anthropic calls.

# Install and run locally
npx -y @maximhq/bifrost

# Or use Docker
docker run -p 8080:8080 maximhq/bifrost

# Open the built-in web interface
open http://localhost:8080

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!"}]
  }'

Setup:

# NPX
npx -y @maximhq/bifrost

# Docker
docker run -p 8080:8080 maximhq/bifrost

Then point your app at http://localhost:8080/v1 instead of the OpenAI endpoint.

Best for: Teams building production AI apps who need reliability and observability without configuration overhead.

Docs: https://docs.getbifrost.ai

2. LiteLLM

What it is: Open-source abstraction layer supporting 100+ LLM providers. Available as Python SDK or proxy server.

Why you'd use it:

You're a platform team building internal LLM infrastructure and you need extensive provider coverage. LiteLLM supports pretty much every LLM provider that exists.

Key features:

• Support for 100+ providers
• Python SDK and proxy server options
• Cost tracking across providers
• Rate limiting and authentication
• Observability integrations (Langfuse, MLflow, Helicone)

Best for: ML platform teams managing multi-provider infrastructure with Python-heavy workflows.

Docs: https://www.litellm.ai/

3. OpenRouter

What it is: Model marketplace providing access to 300+ AI models from 60+ providers through a unified API.

Why you'd use it:

You want to experiment across lots of models without managing provider integrations yourself. OpenRouter handles the provider connections, you just pick models.

Key features:

• Access to 300+ models (GPT-4, Claude, Gemini, Llama, Mistral, and more)
• Automatic fallback routing
• Zero Data Retention (ZDR) mode for privacy
• Response healing for malformed JSON
• Pay-as-you-go pricing

Best for: Developers who want model variety and marketplace flexibility, especially for experimentation and prototyping.

Docs: https://openrouter.ai/

4. Cloudflare AI Gateway

What it is: AI request management built on Cloudflare's edge network. Global caching, rate limiting, observability.

Why you'd use it:

You're already using Cloudflare infrastructure and you want edge-optimized caching for global users. Cloudflare's network serves 20% of internet traffic, so they know how to cache and route at scale.

Key features:

• Edge caching reducing latency by up to 90%
• Rate limiting and request retries
• A/B testing across models
• Integration with Cloudflare Workers AI
• Free tier available

Best for: Teams on Cloudflare infrastructure who need global latency optimization through edge caching.

Docs: https://developers.cloudflare.com/ai-gateway/

5. Kong AI Gateway

What it is: Enterprise API management platform extended with AI-specific features. Semantic routing, PII sanitization, automated RAG.

Why you'd use it:

You're an enterprise with existing Kong deployments and you need advanced governance features. Kong brings enterprise API management capabilities to LLM traffic.

Key features:

• Semantic routing across multiple LLMs
• PII sanitization (20+ categories, 12 languages)
• Automated RAG injection
• Token-based throttling
• MCP and agent workflow support

Best for: Enterprises with Kong infrastructure who need compliance features and semantic capabilities.

Docs: https://developer.konghq.com/ai-gateway/

Quick Comparison

Common Mistakes to Avoid

Mistake 1: Picking based on provider count alone. More providers doesn't mean better. Pick based on which providers you actually use.

Mistake 2: Not testing with your actual workload. What works for 100 requests/day breaks at 100,000. Load test before committing.

Mistake 3: Ignoring self-hosting requirements. If you have data residency requirements, cloud-only solutions won't work.

Mistake 4: Not monitoring actual provider usage. You configure failover but don't track which provider is handling requests. Always monitor.

Ready to get started? Bifrost setup guide