API Gateway for Claude and GPT: What You Actually Need

If you're searching for an "API gateway for Claude and GPT," you're probably trying to solve one of two problems: you want a single, consistent interface to call multiple LLM providers from your app, or you want to turn an existing Claude or ChatGPT subscription into something your code can actually hit with HTTPS requests instead of a browser tab.

These are related but different problems, and most articles on this topic blur them together. This one won't. Below is what an LLM gateway actually does, what it doesn't do, and how to pick the right setup depending on which problem you actually have.

What "API gateway for Claude and GPT" usually means

In practice, people use this phrase to describe three different things:

1. A provider-abstraction layer — code that lets your app call chat() once and route to Claude, GPT, or another model depending on config, cost, or availability.
2. A proxy/governance layer — a service sitting in front of provider APIs that adds auth, rate limiting, logging, and usage tracking across your team.
3. A subscription-to-API bridge — a way to get programmatic API access from a plan that doesn't natively offer it, or to get cleaner API key management than the provider console gives you.

If you already have raw API keys from Anthropic and OpenAI and just want one abstraction layer, you need (1). If you have a team sharing accounts and no visibility into who's burning tokens, you need (2). If you're paying for Claude but don't have (or don't want to manage) direct API billing, you need (3).

Option 1: Build a thin abstraction layer

This is the right call if you're already comfortable managing two sets of API keys and billing relationships. A minimal router looks like this:

async function callModel(provider, messages) {
  if (provider === "claude") {
    return fetch("https://api.anthropic.com/v1/messages", {
      method: "POST",
      headers: {
        "x-api-key": process.env.ANTHROPIC_KEY,
        "anthropic-version": "2023-06-01",
        "content-type": "application/json",
      },
      body: JSON.stringify({ model: "claude-sonnet-4", messages, max_tokens: 1024 }),
    });
  }
  if (provider === "gpt") {
    return fetch("https://api.openai.com/v1/chat/completions", {
      method: "POST",
      headers: {
        Authorization: `Bearer ${process.env.OPENAI_KEY}`,
        "content-type": "application/json",
      },
      body: JSON.stringify({ model: "gpt-4o", messages }),
    });
  }
}

This works fine for a prototype. It breaks down once you need per-key rate limits, usage dashboards, team seats, or streaming normalization across both providers — at that point you're building a small internal platform team just to maintain a router.

Option 2: Put a governance layer in front

If the real pain is "we have five people with API keys in Slack messages and no idea who spent what," a gateway in front of the provider is the fix, regardless of whether you also need multi-provider routing. This layer should give you:

• Per-key or per-user usage metadata, not just a total invoice
• Rate limiting so one runaway script doesn't exhaust a shared quota
• Centralized key rotation instead of keys scattered across repos and .env files
• Streaming support that doesn't break when you add auth in front of it

This is where a dedicated proxy earns its keep, and it's also where a lot of teams discover that the hardest part isn't routing requests — it's getting clean, per-application API access out of a subscription in the first place.

Option 3: Turning a Claude subscription into an API

This is the part most "gateway" tutorials skip, because they assume you already have direct API billing with the provider. If you don't — or you want application-scoped keys, usage breakdowns, and team seats without setting up separate billing infrastructure — that's specifically what SubToAPI does for Claude.

SubToAPI issues scoped application keys (sub_live_...) against your existing Claude access, so each app or environment gets its own key, its own usage metadata, and its own rate limits, without you having to build that layer yourself:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "messages": [{"role": "user", "content": "Summarize this ticket."}],
    "max_tokens": 512
  }'

It supports streaming responses, tool use, and team seats, so if your "gateway" requirement is really "give engineers and services clean, separate access to Claude with visibility into usage," you don't need to build a proxy at all. See the quickstart, messages endpoint, streaming guide, and tool use docs for specifics.

Worth being precise here: SubToAPI handles the Claude side of this. It doesn't proxy GPT. If your stack genuinely needs both providers behind one gateway, a practical pattern is to use SubToAPI for the Claude leg (with clean keys and per-app usage data) and route GPT calls directly or through whatever layer OpenAI's platform gives you, then merge routing logic at the application level shown in Option 1.

Choosing between these three

• Prototype, single provider, no team: direct API keys, no gateway.
• Multiple providers, need routing/fallback logic: build the thin abstraction layer yourself — it's genuinely not much code.
• Team sharing Claude access, need per-app keys and usage visibility: this is the actual gap most people mean when they say "gateway," and it's solved without custom infrastructure — see pricing and start with a free trial.

FAQ

Do I need a gateway if I only use one model provider?

Usually no. If you're only calling Claude or only calling GPT directly with one API key, a gateway adds complexity without solving a real problem. It becomes worth it once you have multiple apps, multiple team members, or multiple providers that each need separate visibility and limits.

Can one gateway route between Claude and GPT automatically?

You can build simple routing logic (try Claude, fall back to GPT on error, or pick by cost) in your own application code fairly easily, as shown above. Full automatic failover with response normalization across providers is more involved and usually only worth building once you're running production traffic against both.

Is a subscription-to-API bridge the same as a gateway?

Not exactly. A bridge like SubToAPI turns subscription-based Claude access into scoped, programmatic API keys with usage tracking and team seats — solving the access and visibility problem. A multi-provider gateway solves the routing problem. Many teams only need the former.