How Does an API Gateway Work? A Technical Breakdown

An API gateway works by sitting between clients and backend services, intercepting every incoming request before it reaches your actual application logic. It inspects the request, decides what to do with it based on a set of rules, and either forwards it, transforms it, blocks it, or answers it directly — then does the same in reverse for the response. In practice, this means the gateway is doing several distinct jobs at once: authentication, routing, rate limiting, protocol translation, and observability, all in the path of a single HTTP call.

The reason this matters is that without a gateway, every backend service has to implement its own auth checks, its own rate limiting, its own logging — and that logic drifts out of sync across services. A gateway centralizes it. Understanding how it does that, mechanically, is what this article covers.

The Core Mechanism: A Request Pipeline

Think of an API gateway as a pipeline of middleware functions that a request passes through in order. Each stage can modify the request, reject it, or pass it forward. A typical pipeline looks like this:

1. TLS termination — the gateway decrypts HTTPS traffic so it can inspect the request.
2. Authentication — validates an API key, JWT, or OAuth token.
3. Authorization — checks whether that identity is allowed to call this specific route.
4. Rate limiting — checks a counter (usually in Redis or an in-memory store) against a quota.
5. Request transformation — rewrites headers, path, or body format if needed.
6. Routing — matches the request path against a routing table to pick a backend.
7. Backend call — proxies the request to the actual service.
8. Response transformation — reshapes the response, adds headers, strips internal fields.
9. Logging/metrics — records latency, status code, and usage data.

Each of these is implementable as a discrete function, which is why most gateway software (Kong, Envoy, AWS API Gateway, or a custom Node.js/Express layer) is built as a plugin or middleware chain. You can enable or disable stages per route.

Authentication: How It Actually Validates a Request

Most gateways don't store user credentials themselves — they validate a signed token or look up a key against a database or cache. A common pattern:

async function authenticate(req) {
  const authHeader = req.headers['authorization'];
  const token = authHeader?.replace('Bearer ', '');
  if (!token) throw new UnauthorizedError();

  const keyRecord = await keyStore.lookup(token);
  if (!keyRecord || keyRecord.revoked) throw new UnauthorizedError();

  req.identity = keyRecord.owner;
  req.plan = keyRecord.plan;
  return true;
}

The lookup is almost always cached (in Redis or an in-memory LRU) because hitting a database on every single request would add unacceptable latency. This is also where scoped API keys come from — a gateway can attach metadata (plan, permissions, rate limit tier) to the key at validation time and pass it down the pipeline.

Routing: Matching Paths to Backends

Routing works off a table, usually built at startup or updated dynamically, that maps a path pattern (and sometimes a header or hostname) to a backend target:

/v1/users/*     -> user-service:8081
/v1/orders/*    -> order-service:8082
/v1/messages    -> claude-proxy:8083

The gateway matches the incoming path against these patterns — often using a trie or regex-based matcher for performance — and forwards the request accordingly, optionally rewriting the path along the way (stripping a version prefix, for example).

Rate Limiting: Counters, Not Guesses

Rate limiting is implemented with a counter and a time window, most commonly a sliding window or token bucket algorithm stored in Redis so it works across multiple gateway instances:

const key = `ratelimit:${apiKey}:${currentWindow}`;
const count = await redis.incr(key);
if (count === 1) await redis.expire(key, windowSeconds);
if (count > limit) throw new TooManyRequestsError();

This is why rate limit headers (X-RateLimit-Remaining, Retry-After) exist — the gateway computes them from the same counter it just checked, and returns them so clients can back off intelligently instead of guessing.

Transformation and Protocol Translation

A gateway often needs to translate between what the client sends and what the backend expects — REST to gRPC, JSON to XML, or reshaping a request body to match an internal API version. This happens as a discrete transformation step, usually configured declaratively (a mapping template) rather than hardcoded, so it can change without redeploying the backend.

Aggregation: One Call, Multiple Backends

Some gateways also aggregate — a single client request triggers calls to multiple backend services, and the gateway merges the responses before returning them. This avoids making the client manage multiple round trips and is common in BFF (Backend-for-Frontend) patterns.

Why This Matters for AI API Access

This same gateway pattern is exactly what's needed when you want to expose AI model access as a controlled API rather than a personal chat login. If you have Claude access through a subscription and want your application to call it over HTTPS with proper API keys, streaming, and usage tracking, you're essentially asking for gateway functionality: authentication, rate limiting, and metadata, in front of a model provider instead of a microservice.

That's what SubToAPI does for Claude specifically — it wraps your existing access with a standard gateway layer: scoped sub_live_... API keys, streaming support, tool use, and per-key usage metadata, all manageable from one dashboard. Instead of building your own auth, rate limiting, and routing layer just to give your app programmatic access to Claude, you get it out of the box. A basic request looks like this:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "claude-sonnet-4-5", "messages": [{"role": "user", "content": "Explain rate limiting"}]}'

Under the hood, that request goes through the same pipeline described above — auth check, rate limit check, routing to the correct backend, and response logging — before it reaches Claude. See the quickstart and messages docs for the full flow, and pricing for plan details.

Building vs. Using One

If you're deciding whether to build your own gateway or use an existing product, the tradeoff comes down to how much of that pipeline you actually need to control. A handful of routes with simple auth might not justify a full gateway. Dozens of services, multiple auth schemes, and per-client quotas usually do.

Questions

Is an API gateway the same as a reverse proxy? No. A reverse proxy forwards requests and can do basic load balancing, but a gateway adds application-aware logic on top — authentication, per-client rate limits, request/response transformation, and routing based on business rules rather than just IP or path.

Does an API gateway add noticeable latency? Usually a few milliseconds per request if auth and rate-limit checks are cached (Redis, in-memory), since those are the two stages most likely to involve I/O. Poorly cached lookups or synchronous database calls in the pipeline are the usual cause of real latency problems.

Can a gateway handle streaming responses? Yes, but it has to be built for it — the proxy layer needs to stream bytes through rather than buffer the full response before forwarding. This matters for AI APIs in particular, where responses are generated token by token; see streaming for how this works with SubToAPI.