multi-provider AI proxy

A multi-provider AI proxy is a gateway service that sits between your application and multiple AI model providers (for example: OpenAI, Anthropic, Google, Azure, local Ollama/vLLM, etc.). Your app talks to the proxy once; the proxy routes requests to the right provider based on rules you define.

What it does

• Unified API: one consistent interface for chat, embeddings, moderation, tools, etc.
• Routing / failover: choose provider by model, cost, latency, region, or availability.
• Load balancing: spread traffic across providers.
• Fallbacks: automatically retry on another provider if one fails.
• Observability: logs, metrics, token usage, latency, errors.
• Policy enforcement: redact sensitive data, block unsafe prompts, cap spend.
• Key management: keep provider API keys server-side instead of in apps.

Typical architecture

1. Client sends request to the proxy.
2. Proxy inspects request:
   • model requested
   • tenant/user
   • budget / quota
   • prompt size / required capabilities
3. Proxy picks a provider using routing logic.
4. Proxy forwards the request to provider API.
5. Proxy normalizes the response back to your app’s format.

Common routing strategies

• Primary/fallback: use preferred provider, fallback on errors.
• Cost-based: route cheaper tasks to cheaper models.
• Latency-based: route to fastest available provider.
• Capability-based: use the provider/model best suited for the task.
• Policy-based: certain tenants or data types go to specific providers.
• A/B testing: compare model performance across providers.

Benefits

• Easier multi-model experimentation
• Vendor lock-in reduction
• Better reliability
• Centralized auditing and governance
• Simplified app code

Risks / tradeoffs

• Added complexity and another failure point
• Possible latency overhead
• Need to normalize different APIs and streaming behaviors
• Harder debugging if routing is opaque
• Provider-specific features may not map cleanly

Popular ways to build one

• Reverse proxy / gateway in Node, Python, Go, or Envoy
• OpenAI-compatible proxy so existing SDKs can be reused
• Managed router platforms if you don’t want to run it yourself

If you want to implement one

A minimal version usually includes:

• request authentication
• provider adapters
• routing policy engine
• retries/timeouts
• response normalization
• usage logging
• secrets management

If you want, I can also provide:

1. a reference architecture,
2. a sample implementation in Node/Python/Go, or
3. a decision matrix for choosing providers dynamically.