Implementing an API Gateway Pattern for Microservices

Implementing an API Gateway Pattern for Microservices

Imagine: you have 15 microservices, each with its own API. The frontend makes dozens of requests to different endpoints, and authentication is duplicated in every service. One bad refactor — and clients get 500 errors. An API Gateway solves this pain: a single entry point with centralized routing, authentication, and rate limiting. Instead of calling multiple services directly, the client talks to one gateway that routes requests, aggregates data, and enforces security policies.

This pattern simplifies security, reduces load on services, and makes the system scalable. Our engineers with 7+ years of experience in microservices implement API Gateway turnkey — from tool selection to monitoring setup. Contact us for a consultation on your project.

Technical Problems with Microservice Architecture

Note: when a client requests data from different services, multiple calls arise. For example, a profile page requires user data, their orders, and notifications. Without a gateway, the client makes 3 requests. With an API Gateway — one. The gateway collects data in parallel via Promise.allSettled and returns a single response. This reduces latency by 40% and simplifies client logic.

Another problem is duplicated authentication. Each microservice must verify JWTs, increasing latency and error risk. An API Gateway checks the token once and passes user data in headers. This reduces load on services and centralizes security.

How an API Gateway Solves the N+1 Request Problem

Request Aggregation (BFF Pattern in the Gateway)

A mobile client gets data from multiple services in one request. The gateway calls them in parallel and assembles the response into a single JSON. Example implementation on Express:

// Gateway aggregates data from multiple services
app.get('/api/dashboard/:userId', jwtMiddleware, async (req, res) => {
  const { userId } = req.params;

  const [user, orders, notifications] = await Promise.allSettled([
    userService.get(`/users/${userId}`),
    orderService.get(`/orders?customerId=${userId}&limit=5`),
    notificationService.get(`/notifications/${userId}/unread`)
  ]);

  res.json({
    user: user.status === 'fulfilled' ? user.value.data : null,
    recentOrders: orders.status === 'fulfilled' ? orders.value.data : [],
    unreadCount: notifications.status === 'fulfilled'
      ? notifications.value.data.count : 0
  });
});

What Metrics Does an API Gateway Improve?

TTFB decreases due to aggregation, and the number of network requests drops by 3-5 times. Core Web Vitals (LCP, CLS) improve because the client gets all data in one response. The gateway also offloads microservices — they only process validated requests.

Case in point: On a project with 15 microservices, we reduced frontend requests from 12 to 2, cutting TTFB by 60%. The client’s page load time dropped from 8s to 3.2s.

How We Implement an API Gateway

Functions We Configure

• Routing — /api/orders → Order Service, /api/users → User Service
• Authentication and Authorization — JWT/OAuth2 checked once at the gateway
• Rate Limiting — abuse protection
• SSL Termination — TLS terminates at the gateway, microservices communicate via HTTP inside the cluster
• Request/Response Transformation — format changes, header additions
• Request Aggregation — one client request → multiple service requests
• Circuit Breaker — protection against cascading failures
• Logging and Tracing — single point for access logs

Tool Comparison

Kong is best for complex scenarios with custom plugins, Traefik excels in Kubernetes integration. We choose the tool based on your stack. Our experience includes projects with Kong (over 50 services) and Traefik (K8s clusters up to 30 nodes).

Kong Configuration

Kong is the most popular self-hosted gateway:

# kong.yaml (declarative configuration)
_format_version: "3.0"

services:
  - name: order-service
    url: http://order-service:3000
    routes:
      - name: orders-route
        paths: ["/api/orders"]
        methods: ["GET", "POST", "PUT", "DELETE"]

  - name: user-service
    url: http://user-service:3001
    routes:
      - name: users-route
        paths: ["/api/users"]
        methods: ["GET", "PUT"]

plugins:
  - name: jwt
    config:
      claims_to_verify: ["exp"]
  - name: rate-limiting
    config:
      minute: 100
      hour: 5000
      policy: local
  - name: request-transformer
    config:
      add:
        headers: ["X-Service-Version:1.0"]

Authentication at the Gateway Level

JWT is verified in the gateway, microservices receive already-validated user headers:

// Custom middleware on Express Gateway
async function jwtMiddleware(req, res, next) {
  const token = req.headers.authorization?.replace('Bearer ', '');
  if (!token) return res.status(401).json({ error: 'No token' });

  try {
    const payload = jwt.verify(token, process.env.JWT_SECRET);
    // Pass user data in headers
    req.headers['X-User-Id'] = payload.sub;
    req.headers['X-User-Role'] = payload.role;
    req.headers['X-User-Email'] = payload.email;
    next();
  } catch {
    res.status(401).json({ error: 'Invalid token' });
  }
}

# Traefik IngressRoute
apiVersion: traefik.containo.us/v1alpha1
kind: IngressRoute
metadata:
  name: api-gateway
spec:
  entryPoints:
    - websecure
  routes:
    - match: PathPrefix(`/api/orders`)
      kind: Rule
      services:
        - name: order-service
          port: 3000
      middlewares:
        - name: jwt-auth
        - name: rate-limit
    - match: PathPrefix(`/api/users`)
      kind: Rule
      services:
        - name: user-service
          port: 3001
      middlewares:
        - name: jwt-auth

---
apiVersion: traefik.containo.us/v1alpha1
kind: Middleware
metadata:
  name: rate-limit
spec:
  rateLimit:
    average: 100
    burst: 50
    period: 1m

Implementation Process

Pricing is calculated individually after auditing your architecture. Request a consultation — we will assess your project free of charge.

What’s Included in the Implementation

• Route and security policy design
• Configuration of Kong, Traefik, or a cloud gateway for your infrastructure
• Integration with JWT/OAuth2, LDAP, or another provider
• Configuration of rate limiting, circuit breaker, and monitoring
• API documentation and team instructions
• Access transfer and training for your engineers

We also provide post-implementation support: version upgrades, performance optimization. Our engineers are certified in Kubernetes and Kong, with 7+ years of experience and 50+ successful projects. We guarantee stable gateway operation under load. Get a consultation for your project.

Additional Resources

Learn more about the pattern on Wikipedia. Official Kong documentation is available at konghq.com.

API Development with REST, GraphQL, WebSocket, and tRPC

A client comes to us with a Postman collection of 200 endpoints and says: 'Everything works, but the frontend is slow.' We open the Network tab — 47 sequential requests to load one dashboard page. Each one waits for the previous. This is not a server speed issue — it's an API architecture problem. With 10 years on the market, we've redesigned dozens of such integrations, and we guarantee: the right protocol and contract solve the problem at its root.

When REST stops being enough

REST works well for simple CRUD operations. But as soon as a mobile app appears alongside the web interface, over-fetching begins: the mobile app requests /api/users/123 and gets a 4KB object, but only needs name and avatar. Multiply that by a list of 50 users — 200KB traffic instead of 8KB.

GraphQL solves this with selection sets. The client describes exactly the fields it needs, and the server returns only those. On a project with React Native + Next.js, we migrated from REST to Apollo Server: payload size on the main screen dropped from 340KB to 28KB — a 92% traffic savings. Our certified engineers confirm: the typical pain when adopting GraphQL is N+1 query. A resolver for the author field on a post calls SELECT * FROM users WHERE id = ? for each post in the list. On a page with 20 posts — 21 database queries. Solved with DataLoader — it batches queries and turns them into one SELECT * FROM users WHERE id IN (...).

What is tRPC and how is it better than REST/GraphQL?

If the entire stack is TypeScript (Next.js + Node/Bun), tRPC removes a whole layer of problems. You define a procedure on the server — the client gets full type-safety automatically, without code generation and without Swagger. Renamed a field in the Zod schema — TypeScript highlights all places on the frontend where it's used. tRPC reduces code by 2 times compared to REST + Swagger + openapi-typescript: no need to maintain a separate specification and generate types — everything is inferred from runtime validators. However, tRPC is not suitable if the API is consumed by third-party clients or mobile apps in other languages — in such cases we use GraphQL or REST with OpenAPI specification.

WebSocket and real-time: when SSE, when WS?

HTTP polling every 5 seconds is an illusion of real-time with up to 5 seconds delay and useless server load. For chats, live notifications, collaborative editing — WebSocket or Server-Sent Events. SSE is a one-way stream from server to client, works over ordinary HTTP, automatically reconnects. Suitable for notifications, data streaming, progress bars. WebSocket is bidirectional, needed for chats and collaborative features. Experience shows: 80% of 'real-time' tasks are solved with SSE, not WebSocket — fewer infrastructure complexities.

A typical mistake: opening a WebSocket connection for each page component. On one project, the dashboard opened 12 parallel WS connections. The correct approach is one connection manager at the application level, subscriptions through it. In our work results, we always transfer the connection scheme and a ready solution.

Swagger / OpenAPI as a contract

Documentation written after the fact becomes outdated the day after release. We write the OpenAPI 3.1 specification before development starts; it becomes the contract between frontend and backend. The frontend generates types via openapi-typescript, the backend validates incoming data using generated schemas. Contract deviation from implementation is caught on CI, not during review. For Laravel — l5-swagger or dedoc/scramble. For Node.js — @fastify/swagger or Zod + zod-to-openapi.

How to properly authenticate an API?

JWT with long-lived access tokens without rotation is a source of problems when compromised. The correct scheme: access token for 15 minutes, refresh token for 30 days with rotation on each use. Refresh token stored in an httpOnly cookie, access token in memory (not in localStorage). For inter-service communication — API Keys with scope limitations or mTLS. OAuth 2.0 with PKCE for public clients (SPA, mobile).

How to handle versioning and backward compatibility?

Breaking changes in an API without versioning break clients. Three approaches we use in projects:

We guarantee backward compatibility through automated checks (oasdiff) on CI.

How we develop APIs: step-by-step plan

1. Analysis — audit of current integrations, data schema compilation, protocol selection (REST/GraphQL/tRPC/WebSocket).
2. Contract design — OpenAPI or SDL (GraphQL) before the first line of code.
3. Development — implementation per contract, unit tests for each endpoint.
4. Load testing — k6: 500 virtual users, 10 minutes, p95 latency ≤ 200ms.
5. Deployment — CI/CD with backward compatibility check, automatic documentation publication.
6. Team training — handover of Postman collection or Playground, connection instructions.

What is included (deliverables)

• OpenAPI 3.1 specification (or SDL for GraphQL).
• Generated client types for TypeScript / Dart / Kotlin.
• Set of automated tests covering all endpoints (unit + integration).
• Load tests (k6) and report (p50/p95/p99 latency, RPS).
• Documentation in Swagger UI / Redoc / GraphiQL.
• Team training (2–4 hour workshop).
• Support for 30 days after delivery (per contract).

Our experience

• 10+ years in the API development market.
• 200+ completed projects (REST, GraphQL, WebSocket, tRPC).
• 50+ certified engineers (AWS, Kubernetes, API Design).
• Traffic savings averaging 85% when migrating from REST to GraphQL for mobile apps.
• 100% backward compatibility — not a single broken client in the last 3 years.

Timeline

API development for a typical SaaS project with 30–50 endpoints: from 3 to 8 weeks depending on business logic complexity and number of external integrations. Migration of an existing REST API to GraphQL: from 2 to 6 weeks. Adding a WebSocket layer to an existing backend: from 1 to 3 weeks. Cost is calculated individually after an audit. Get a consultation — contact us to discuss your project.