API Middleware for Claude and GPT Models

What "API middleware" means for LLM apps

If you're searching for API middleware for Claude and GPT models, you're probably trying to solve one of two problems: you want a single interface that talks to multiple model providers without rewriting your app for each one, or you want a layer that sits between your product and the raw provider API to handle auth, rate limits, logging, and billing cleanly. Both are the same underlying idea — middleware is the code (or service) that normalizes, secures, and observes traffic between your application and the model provider.

The short answer: you either build this yourself as a thin abstraction layer in your backend, or you use a hosted middleware service that already does key management, request normalization, and usage tracking for you. This article covers what the middleware layer actually needs to do, how to build a minimal version, and when it makes more sense to use something like SubToAPI instead of maintaining it yourself.

Why you need a middleware layer at all

Calling Claude or GPT directly from your frontend or a single backend function works fine for a prototype. It breaks down once you have more than one thing going on at the same time:

• Multiple consumers — a web app, a mobile app, internal scripts — all needing access without each holding a raw provider credential.
• Multiple providers — Claude for reasoning-heavy tasks, GPT for something else, and you don't want provider-specific code scattered across your codebase.
• Usage accounting — knowing which customer, feature, or team burned how many tokens.
• Key security — provider API keys are long-lived and expensive to leak; you want short-lived, revocable, scoped credentials instead.
• Consistent error handling and retries — providers throttle and fail differently, and you don't want that logic duplicated in every service that calls a model.

Middleware is where all of this lives once, instead of being copy-pasted into every service that needs to call a model.

Core responsibilities of LLM middleware

1. Request normalization

Claude and GPT have different message formats, different ways of expressing tool/function calls, and different streaming event shapes. Middleware should give your application code one consistent request/response shape regardless of which model is actually handling the call. This is the biggest win if you plan to switch providers or run them side by side for different tasks.

2. Authentication and key isolation

Your application should never hand out the underlying provider key to individual services or client apps. Instead, middleware issues its own scoped keys — one per app, environment, or team — and translates those into the actual provider credential internally. If a key leaks, you revoke it without touching your primary provider account.

3. Streaming support

Token-by-token streaming needs to be handled consistently. If your middleware buffers responses instead of passing chunks through, you lose the responsiveness that makes chat UIs feel fast. Any middleware layer worth using needs to support server-sent events or chunked transfer without adding meaningful latency.

4. Usage metadata

Every response should come back with token counts and cost data attached, so you can bill, alert, or forecast. This is one of the most commonly skipped features in DIY middleware — it's easy to forward a request and hard to consistently track usage across every model and endpoint.

5. Tool use passthrough

If you're using function/tool calling, your middleware needs to pass tool definitions and tool results through without mangling the schema. This is where a lot of custom middleware breaks — subtle differences between how Claude and GPT structure tool calls cause silent failures if you're not careful.

Building a minimal version yourself

A basic middleware layer is just an Express (or equivalent) route that accepts a normalized request, maps it to the right provider format, and returns a normalized response:

app.post('/v1/messages', async (req, res) => {
  const { model, messages, stream } = req.body;

  const provider = model.startsWith('claude') ? 'anthropic' : 'openai';
  const payload = mapToProviderFormat(provider, messages);

  const response = await callProvider(provider, payload, { stream });

  if (stream) {
    response.pipe(res);
  } else {
    res.json(normalizeResponse(provider, response));
  }
});

This works, but mapToProviderFormat, normalizeResponse, retry logic, streaming edge cases, and usage tracking are where the real effort goes. Most teams underestimate how much ongoing maintenance this needs as providers update their APIs.

When to use a hosted middleware service instead

If your middleware needs are mostly "give my app a clean API key, handle streaming and tool use correctly, and show me usage per key," building this from scratch is a lot of surface area to maintain for something that isn't your core product.

SubToAPI is built specifically for this: it turns your existing Claude access into a proper HTTPS API. You get application-scoped keys (sub_live_...), streaming, tool use, and usage metadata out of the box, plus a dashboard for managing team seats. Instead of writing and maintaining your own normalization and key-issuing layer, you point your app at the SubToAPI endpoint:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Summarize this ticket."}]
  }'

Each application or environment gets its own key, so you can revoke access without affecting anything else, and every request is tracked with usage data you can review in the dashboard. See the quickstart for setup, messages docs for request formatting, streaming docs for SSE handling, and tools docs for function calling. Plans start at Solo €9, with Team (€19/seat) and Scale (€49/seat) tiers for larger setups — full breakdown on pricing, and you can start on a free trial at signup.

Choosing the right approach

• Prototype or single-provider app — call the provider SDK directly, skip middleware for now.
• Multiple internal services or teams sharing one Claude account — you need key isolation and usage tracking; build a thin layer or use a hosted one.
• Multi-provider routing (Claude + GPT) — normalization logic is the hard part; decide whether maintaining it yourself is worth the engineering time versus buying it.
• Need it working this week — a hosted service gets you scoped keys, streaming, and usage metadata without writing the plumbing yourself.

questions

Is API middleware the same as an LLM gateway? Largely yes — both terms describe a layer that sits between your app and one or more model providers, handling auth, routing, and normalization. "Gateway" is sometimes used for multi-provider routing specifically, while "middleware" is the broader term.

Can middleware add latency to streaming responses? It can if it buffers the full response before forwarding. Properly built middleware passes chunks through as they arrive, adding negligible overhead compared to calling the provider directly.

Do I need middleware if I only use one provider? If you have a single app calling a single provider, probably not. Middleware becomes valuable once you have multiple consumers, need scoped keys, or want centralized usage tracking across services.