LLM API Proxy for Multiple Providers: Guide

An LLM API proxy for multiple providers is a single backend layer that sits between your application and the various model APIs you use—Claude, GPT-4, Gemini, Mistral, open-weight models on your own infrastructure—so your app talks to one consistent interface instead of juggling different SDKs, auth schemes, and response formats.

Teams build or adopt one for a simple reason: every provider has its own request shape, header format, rate limits, and error conventions. Without a proxy, switching providers or running A/B tests between models means rewriting integration code in every service that calls an LLM. With one, you change a config value or a routing rule, and the rest of your codebase doesn't notice.

Why You Need a Proxy Layer

If you only ever call one model from one place, you don't need this. But most products end up here for a few reasons:

• Cost and quality experiments. You want to route some traffic to a cheaper model and some to a stronger one, and compare results without duplicating application logic.
• Vendor risk. If a provider has an outage or changes pricing, you want a fallback path that doesn't require an emergency deploy.
• Internal governance. Security and finance teams want one place to see usage, enforce rate limits, and rotate keys—not five.
• Format normalization. OpenAI's chat completions, Anthropic's messages API, and others structure tool calls, streaming chunks, and system prompts differently. A proxy translates once instead of in every client.

What a Good Multi-Provider Proxy Actually Does

A proxy that's worth building (or paying for) handles these concerns, not just forwarding requests:

1. Unified authentication — one API key format for your internal services, regardless of how many upstream provider keys it manages behind the scenes.
2. Request/response normalization — a consistent schema for messages, roles, and tool definitions so your application code doesn't branch on provider.
3. Streaming support — server-sent events or chunked responses that behave the same way whether the upstream model is Claude or anything else.
4. Usage and cost metadata — token counts and attribution per request, so you can bill internal teams or track spend by feature.
5. Routing and fallback logic — the ability to pick a model by rule (latency, cost, capability) and retry on a different provider if the first one errors or times out.
6. Tool/function calling parity — translating your tool schema into whatever format each provider expects, and normalizing the results coming back.

Missing any of these turns your "proxy" into a thin HTTP forwarder that doesn't save you much work.

Build vs Buy

Building this yourself is straightforward at first and gets harder fast. A basic router that picks between two providers based on a config flag takes an afternoon. The hard parts show up later: handling partial streaming failures gracefully, keeping tool-call translation correct as providers update their APIs, managing per-team rate limits, and giving non-engineers visibility into usage without exposing raw provider keys.

If your actual need is "give my product a dependable HTTPS layer on top of Claude access, with keys, streaming, tool use, and usage visibility I can hand to a team," that's a narrower and more solvable problem than building a full multi-provider router from scratch. That's the gap SubToAPI fills: it turns your existing Claude access into standard sub_live_... application API keys with streaming, tool use, and per-key usage metadata, so the Claude leg of your proxy setup is already production-ready instead of something you maintain yourself. You can sit a thin routing layer in front of it and other provider endpoints, and let SubToAPI handle the Claude side specifically — key management, streaming behavior, and team seats — while you focus routing logic on the decision of which model to call.

Here's what that looks like in practice, routing between two upstream APIs based on a simple rule:

async function routeRequest(messages, { preferFast } = {}) {
  const provider = preferFast ? "claude" : "fallback";

  if (provider === "claude") {
    const res = await fetch("https://api.subtoapi.app/v1/messages", {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        model: "claude-3-5-sonnet",
        max_tokens: 1024,
        messages,
      }),
    });
    if (res.ok) return res.json();
  }

  // fallback to another provider's endpoint here
  return callOtherProvider(messages);
}

The shape of the call matches what you'd expect from a standard messages API — see /docs/messages and /docs/streaming for the full request/response contract, and /docs/tools if your routing layer needs to pass through tool definitions consistently across providers.

Common Pitfalls

A few mistakes show up repeatedly in proxy setups:

• Silent format drift. When a provider updates its API, normalization logic breaks quietly. Add contract tests that check response shape, not just status codes.
• Losing streaming semantics in translation. Converting one provider's stream format to another's can introduce latency or buffering bugs. Test streaming under real network conditions, not just localhost.
• No per-key usage tracking. If you can't tell which team or feature is driving cost, you can't make routing decisions based on real data.
• Treating fallback as free. Retrying on a different provider mid-request can double your cost per user request if not rate-limited carefully.

If you're starting from an existing Claude integration, getting a clean, keyed API layer in place first — before you add multi-provider routing on top — makes the rest of this much easier to reason about. Check /pricing for plan details and /signup to start a free trial, or jump straight to /docs/quickstart to see how the keys and endpoints work.

questions

Do I need a proxy if I only use one LLM provider today? Not strictly, but adding a thin abstraction layer early — even just a normalized request/response shape — makes it much cheaper to add a second provider or model later without rewriting application code.

What's the difference between an LLM proxy and an LLM gateway? The terms are used interchangeably in most contexts. Both describe a layer that normalizes requests, manages auth, and routes traffic to one or more underlying model APIs.

Can SubToAPI route between different LLM providers? SubToAPI turns your Claude access into a standard HTTPS API with keys, streaming, tool use, and usage metadata. It's the Claude leg of a multi-provider setup — you'd pair it with your own routing logic if you need to call other providers too.