KrakenD API Gateway Turnkey Setup for Web Applications

N+1 Queries and Fragmented APIs: A Typical Architectural Pain

Your SPA makes 30 requests per page; N+1 queries in GraphQL haven't solved the API fragmentation problem. Each microservice returns its own data, and client code is littered with call chains. We are a team of engineers with 5 years of experience implementing KrakenD. Our specialty is high-load API Gateways handling up to 70,000 RPS on a single core. KrakenD is a stateless API Gateway written in Go with a declarative configuration in a single JSON/YAML file. Its standout feature is aggregating responses from multiple backend services into one response to the client (API composition). This reduces latency and simplifies client code.

How KrakenD Solves the API Aggregation Problem

KrakenD makes parallel requests to backends and merges them into a single response. The client sees only one endpoint, and the gateway itself collects the data. This eliminates the need to write a BFF or use workarounds like server-side includes.

Why KrakenD Is Faster Than Kong

In benchmarks, KrakenD achieves up to 70K RPS per core—2–3 times higher than Kong (25K RPS). The reason is a minimalistic codebase in Go and the absence of plugins that slow down processing. For high-load projects, this is critical: you save on hardware and keep low latency. According to official KrakenD benchmarks, the difference is especially noticeable when aggregating multiple backends.

Aggregation in Action

A typical endpoint /api/v1/user-profile calls three services: users, billing, prefs. KrakenD invokes them in parallel and returns a combined JSON. In the configuration, this looks like:

{
  "$schema": "https://www.krakend.io/schema/v3.json",
  "version": 3,
  "name": "MyApp API Gateway",
  "timeout": "3000ms",
  "cache_ttl": "300s",
  "output_encoding": "json",
  "port": 8080,
  "endpoints": [
    {
      "endpoint": "/api/v1/user-profile",
      "method": "GET",
      "output_encoding": "json",
      "backend": [
        {
          "url_pattern": "/users/{JWT_CLAIMS.user_id}",
          "host": ["http://users-service:3000"],
          "mapping": { "data": "user" }
        },
        {
          "url_pattern": "/subscriptions/active?user_id={JWT_CLAIMS.user_id}",
          "host": ["http://billing-service:3001"],
          "mapping": { "plan": "subscription_plan" }
        },
        {
          "url_pattern": "/preferences/{JWT_CLAIMS.user_id}",
          "host": ["http://prefs-service:3002"],
          "mapping": { "settings": "preferences" }
        }
      ],
      "extra_config": {
        "auth/validator": {
          "alg": "RS256",
          "jwk_url": "http://auth-service:4000/.well-known/jwks.json",
          "cache": true,
          "cache_duration": 300
        }
      }
    }
  ]
}

The client makes one GET request and receives {"user":{"id":"123","name":"Alice"},"subscription_plan":"pro","preferences":{"theme":"dark"}}. Without KrakenD, you would have to make 3 requests and merge on the client—increasing complexity and loading time.

Rate Limiting and Circuit Breaker

To protect backends from overload, we configure rate limiting (by IP or token) and a circuit breaker. Example configuration:

{
  "endpoint": "/api/v1/search",
  "backend": [{ "url_pattern": "/search", "host": ["http://search:3000"] }],
  "extra_config": {
    "qos/ratelimit/router": {
      "max_rate": 100,
      "client_max_rate": 10,
      "strategy": "ip",
      "capacity": 100,
      "client_capacity": 10
    },
    "qos/circuit-breaker": {
      "interval": 60,
      "timeout": 10,
      "max_errors": 5,
      "name": "search-cb",
      "log_status_change": true
    }
  }
}

The circuit breaker disables a problematic backend for 10 seconds after 5 errors, preventing cascading failures. JWT validation errors are also cut off at the gateway level, reducing load on services.

Caching and Additional Transformations

KrakenD supports response caching (cache_ttl) and transformation using Lua scripts or built-in filters. This allows, for example, filtering sensitive fields before sending to the client. For frequent requests, caching can reduce latency by up to 80%.

KrakenD Setup Process

We follow a clear plan to ensure you get a stable gateway without surprises.

Analysis and Architecture Design

We study your architecture: list of services, endpoints, authentication and security requirements. We create an aggregation schema and choose a caching strategy.

Installation and Configuration

Installation via Docker:

docker run -p 8080:8080 \
  -v $PWD/krakend.json:/etc/krakend/krakend.json \
  devopsfaith/krakend:2.7 run -c /etc/krakend/krakend.json

We create a configuration with your endpoints, JWT validation, rate limiting, and circuit breaker. We use KrakenD Designer for visual verification.

Monitoring Integration

We set up Prometheus metrics and Grafana dashboards. Example monitoring section:

{
  "extra_config": {
    "telemetry/metrics": {
      "collection_time": "60s",
      "proxy_disabled": false,
      "router_disabled": false,
      "backend_disabled": false,
      "endpoint_disabled": false,
      "listen_address": ":9091"
    },
    "telemetry/opentelemetry": {
      "service_name": "krakend-gateway",
      "exporters": {
        "prometheus": [{ "port": 9090, "disable_metrics": false }],
        "otlp": [{
          "name": "jaeger",
          "host": "jaeger",
          "port": 4317,
          "use_http": false
        }]
      }
    }
  }
}

You get dashboards with LCP, TTFB, error count, and backend load.

Testing and Deployment

We perform load testing, check aggregation, timeouts, circuit breaker. After successful testing, we deploy to your environment (Kubernetes, bare metal, cloud).

Documentation and Training

We hand over complete documentation: configuration schema, instructions for adding endpoints, dashboard addresses. We conduct a training session for your team.

What's Included in the Setup?

Why Choose Us?

We are certified engineers with 5 years of experience working with KrakenD. We have launched 50+ projects, including high-load systems with 100K RPS traffic. We guarantee 99.9% uptime and deliver the project with full documentation. Thanks to aggregation and caching, our clients save up to 40% on infrastructure costs. Payback period for setup is typically 3–4 months.

Order a turnkey KrakenD setup — from consultation to monitoring. We will assess your project within 1 day. Get a ready-made gateway with documentation and team training. Contact us to get started.

Estimated Timelines

Basic setup with aggregation, JWT, and rate limiting — 2–3 business days. Complex configuration with transformations and observability — 4–5 days. The cost is calculated individually after analyzing your architecture.

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.