2026
API Gateway: The Front Door to Your Backend Services
When you have more than one backend service, clients need a single place to send requests. An API gateway provides that entry point and handles authentication, rate limiting, routing and observability in one layer so your services do not have to.
When you have one service, every client talks to it directly. When you have ten services, that starts to break down. Do clients need to know the address of each one? Does each service implement its own authentication? What happens when you need to rate limit a specific caller? These questions come up quickly once an architecture grows past a single backend.
An API gateway is the answer to all of them at once. It sits at the front of your backend services and acts as the single entry point for every inbound API request. Clients talk to the gateway. The gateway talks to whichever service needs to handle the request. Everything in between — authentication, rate limiting, routing, logging — happens in one place without any individual service needing to know about it.
That centralisation is the point. It is not about adding complexity. It is about deciding where concerns belong. The gateway handles the cross-cutting ones so the services behind it can focus on what they are actually supposed to do.
What the gateway is responsible for
Different gateway implementations prioritise different things, but the core set of responsibilities shows up everywhere. These are the problems a gateway exists to solve.
Routing
The gateway receives every inbound request and decides which upstream service should handle it. Routing rules can be based on the request path, the HTTP method, headers or query parameters. A request to /orders/123 goes to the orders service. A request to /users/profile goes to the user service. The caller knows nothing about this separation.
Authentication and authorisation
Rather than every backend service implementing its own token verification, the gateway handles it once. It validates the bearer token or API key, rejects unauthenticated requests before they reach a service and optionally attaches identity information to forwarded headers. Services behind the gateway can trust that an authenticated identity has already been established.
Rate limiting
The gateway counts how many requests a caller is making within a time window and starts returning 429 responses when a threshold is exceeded. This protects backends from being overwhelmed by a single client and gives you a place to enforce usage quotas for API consumers without touching service code.
TLS termination
Inbound HTTPS connections are decrypted at the gateway. Traffic between the gateway and upstream services can then travel over plain HTTP on an internal network. This centralises certificate management and offloads the cryptographic overhead from individual services.
Request and response transformation
Some gateways can modify requests before forwarding them. Headers can be added or removed, request bodies can be rewritten, response fields can be filtered out before the payload reaches the caller. This is useful for versioning APIs without changing backend behaviour.
Observability
Because every request passes through the gateway, it is the natural place to record timing, status codes and error rates. Metrics and access logs captured here give a complete picture of API usage across all services without requiring each backend to emit the same data independently.
Gateway versus reverse proxy
A reverse proxy and an API gateway look similar from the outside. Both sit in front of backends and forward requests. The difference is in what they understand about those requests. A reverse proxy thinks about connections and hosts. A gateway thinks about APIs.
In practice the boundary is blurry. nginx with the right modules can do much of what a dedicated gateway does. Kong runs on top of nginx. Traefik started as a reverse proxy for containers but now includes middleware for rate limiting, auth forwarding and circuit breaking. The label matters less than knowing which features you need.
Routing patterns
The routing layer is where a lot of the gateway's practical value shows up. Simple path-based routing is the baseline. The more advanced patterns are what let you manage deployments without changing client behaviour.
Path-based routing
Requests are routed to different backends based on the URL path. /api/v1/payments goes to the payments service. /api/v1/notifications goes to the notifications service. The client sees a single origin. The gateway splits traffic by path.
Header-based routing
The gateway inspects a specific header value and routes accordingly. A common use is routing mobile clients to a different backend than web clients based on the User-Agent header. Canary deployments use this pattern too: a custom header controls whether a request goes to the stable version or the new one.
Version routing
API versions are maintained as separate upstream services. The gateway routes /v1/ requests to the v1 backend and /v2/ requests to the v2 backend. Old clients keep working without modification. The v1 service can be decommissioned once traffic to it drops to zero.
Weighted routing
Traffic is split between two upstream targets by percentage. Five percent goes to the new deployment. Ninety-five percent goes to the stable one. If the new deployment behaves correctly under real traffic, the ratio shifts gradually until the rollout is complete.
Authentication at the gateway layer
Every service implementing its own token validation is the kind of duplication that causes problems. The logic gets out of sync. Someone forgets to check token expiry in one service. A new authentication mechanism needs to be rolled out across a dozen codebases. Centralising auth at the gateway solves all of this.
The gateway validates the token on every inbound request. If validation fails, the request is rejected before it reaches a service. If it passes, the gateway can forward a trusted header containing the verified user identity. Services read that header without needing to verify anything themselves. They trust the gateway the same way a service inside a perimeter trusts its network boundary.
Request flow with gateway auth
Client → gateway: POST /orders Authorization: Bearer <token>
↓ gateway validates token signature and expiry
↓ token invalid → 401 returned, request stops here
↓ token valid → gateway forwards request upstream
Gateway → orders service: POST /orders X-User-Id: usr_8f3a X-User-Role: customer
↓ orders service trusts the forwarded headers
Orders service → gateway → client: 201 Created
This pattern works well with JWTs because the gateway can verify the signature without making a network call to an auth server on every request. The public key is loaded once. Validation is local. Opaque tokens — tokens that are just random strings — require a round trip to an introspection endpoint on each request, which adds latency and creates a dependency.
Rate limiting
Rate limiting at the gateway level works on a fundamentally different principle than rate limiting inside a service. The gateway sees every request from every client. It can enforce limits per API key, per authenticated user or per IP address before a single byte reaches a backend process.
Common rate limiting algorithms
Fixed window
Count requests in a fixed interval (e.g. 100 per minute). Counter resets at the boundary.
Burst allowed: a caller can exhaust the limit at the end of one window and the start of the next.
Sliding window
Count requests in a rolling interval ending at the current moment. Smoother enforcement.
More expensive to compute — requires storing per-request timestamps.
Token bucket
A bucket refills at a steady rate up to a maximum. Each request consumes a token.
Allows short bursts up to bucket capacity while enforcing a sustained average rate.
When a request is rejected by the rate limiter, the gateway returns a 429 Too Many Requests response. The Retry-After header tells the client how long to wait before trying again. Well-behaved clients respect this. Scraping bots typically do not, which is why IP blocking and CAPTCHA challenges exist as escalating responses beyond basic rate limiting.
Things that go wrong
Gateway problems are hard to diagnose because the gateway is transparent by design. When something breaks, the symptoms appear in the client or the backend before anyone thinks to look at the layer in between.
Putting business logic in the gateway
The gateway is infrastructure. It routes, authenticates and rate-limits. When teams start adding conditional logic that depends on request content or starts making upstream calls to decide where to route, the gateway becomes hard to test and harder to change. Keep that kind of logic in services.
Single point of failure
A gateway that runs as a single instance takes the entire API surface down when it crashes. Production deployments run multiple instances behind a load balancer. Health checks, graceful restarts and connection draining all matter here. The gateway is in the critical path for every request.
Overly permissive CORS configuration
Setting Access-Control-Allow-Origin to * on a gateway that also handles authenticated requests creates a security problem. Browsers will send cookies and auth headers to any origin that asks. CORS policy should be as narrow as the actual set of allowed origins.
Not monitoring the gateway itself
Teams instrument the services behind the gateway but forget to monitor the gateway's own latency and error rate. The gateway can be healthy while silently adding hundreds of milliseconds to every request due to a misconfigured plugin or a saturated connection pool. It needs its own dashboards.
Where it fits
The API gateway sits at the intersection of networking and application concerns. It is further up the stack than a load balancer — which distributes connections without knowing anything about their content — but it operates below the level of application code, which means it can enforce policy consistently without requiring every service to implement it.
In a Kubernetes environment, the gateway usually runs as an ingress controller or a dedicated deployment that Ingress resources point to. Traefik and Kong both have Kubernetes-native modes where routing rules are declared as Kubernetes objects. The gateway reads those objects and configures itself without a manual restart. RBAC controls which teams can write those routing rules, which keeps the gateway configuration part of the same review process as everything else in the cluster.
Managed cloud options like AWS API Gateway, Google Cloud API Gateway and Azure API Management handle the infrastructure side but keep the same logical model. You define routes, attach auth policies, configure rate limits. The provider runs the gateway across availability zones and handles scaling. The tradeoff is less control over the execution environment in exchange for less operational overhead. For teams that do not want to maintain gateway infrastructure, managed gateways are a reasonable default. For teams that need fine-grained control over request processing, self-hosted options give more flexibility.