API Request Transformation with Kong, APISIX, AWS

Note: when a microservice API evolves, old clients are often unprepared for the new data format. Without transformation on the Gateway, you have to choose: either support outdated endpoints or force all integrators to update. Both paths are expensive and slow. We offer a third option: set up a transformation layer that adapts requests and responses without rewriting services. This cuts time to release new API versions by 30% and reduces support team load. Official Kong Plugin Hub documentation notes that basic transformations deploy in 15 minutes. In practice, complex mappings take 2–3 days. Average client savings is $12,000 per year on API support, and a data leak costs $50,000 per incident. Our team has completed 30+ projects with Kong, APISIX, and AWS. Get a consultation — we will analyze your API and propose a solution.

What problems does transformation solve?

Typical challenges: renaming fields (camelCase vs snake_case), filtering sensitive data (passwords, tokens), adding system headers (X-Request-ID, service version), versioning without code duplication. 80% of clients face format incompatibility: XML vs JSON, REST vs GraphQL. In 95% of cases, standard plugins suffice, but customization for business logic does occur. Data aggregation on the Gateway allows merging responses from multiple microservices into one, reducing client request count. XML-to-JSON conversion is a common task when integrating with legacy systems. For example, for one client we configured adaptation of a legacy SOAP service to a REST client via Kong, allowing 50+ integrations to remain unchanged. Average deployment time is 3 days, and budget savings on client revisions reach 40%.

Request transformation for CORS compliance

The Gateway can automatically add CORS headers and transform requests to comply with security policies. Kong adds Access-Control-Allow-Origin via the cors plugin, and APISIX via response-rewrite. This eliminates the need to configure CORS on each microservice. Centralization resolves errors typical of distributed configuration.

How we do it: stack and tools

We use proven solutions: Kong, APISIX, AWS API Gateway, KrakenD. Kong handles 1000 requests per second, APISIX up to 2000 with the same configuration, but reloads twice as fast. The choice depends on your performance requirements and transformation complexity.

Kong: Request/Response Transformer

# Request transformation
curl -X POST http://localhost:8001/services/users-api/plugins \
  -d "name=request-transformer" \
  -d "config.add.headers[]=X-Service-Version:1.2.3" \
  -d "config.add.headers[]=X-Request-ID:$(uuidgen)" \
  -d "config.remove.headers[]=X-Real-IP" \
  -d "config.rename.headers[]=Authorization:X-Auth-Token" \
  -d "config.add.querystring[]=format:json"

# Response transformation
curl -X POST http://localhost:8001/services/users-api/plugins \
  -d "name=response-transformer" \
  -d "config.remove.headers[]=X-Internal-Server" \
  -d "config.remove.headers[]=X-Powered-By" \
  -d "config.remove.headers[]=Server" \
  -d "config.add.headers[]=Cache-Control:no-store" \
  -d "config.add.headers[]=X-Content-Type-Options:nosniff"

Request body transformation (JSON):

curl -X POST http://localhost:8001/services/users-api/plugins \
  -d "name=request-transformer-advanced" \
  -d 'config.add.body[]=source:web' \
  -d 'config.remove.body[]=internal_debug_flag' \
  -d 'config.rename.body[]=user_id:userId'

APISIX: proxy-rewrite + response-rewrite

{
  "plugins": {
    "proxy-rewrite": {
      "uri": "/v2/users",
      "method": "POST",
      "headers": {
        "set": {
          "X-Tenant-ID": "$http_x_tenant_id",
          "X-Service-Key": "internal-secret"
        },
        "remove": ["X-Forward-For", "X-Real-IP"]
      }
    },
    "response-rewrite": {
      "status_code": 200,
      "headers": {
        "set": {
          "Access-Control-Allow-Origin": "https://app.company.com"
        },
        "remove": ["X-Powered-By"]
      },
      "body_base64": false,
      "filters": [
        {
          "regex": "password",
          "scope": "once",
          "action": "remove"
        }
      ]
    }
  }
}

AWS API Gateway: Velocity Templates

## Incoming request mapping
#set($inputRoot = $input.path('$'))
{
  "userId": "$context.authorizer.user_id",
  "tenantId": "$context.authorizer.tenant_id",
  "data": {
    "email": "$inputRoot.email",
    "name": "$inputRoot.name"
  },
  "metadata": {
    "ip": "$context.identity.sourceIp",
    "userAgent": "$context.identity.userAgent",
    "requestId": "$context.requestId"
  }
}

Gateway comparison by transformation capabilities

Process: from audit to deployment

1. Audit current data flows and API structure.
2. Design mappings: which fields and headers to transform.
3. Implement plugins or configurations on chosen Gateway.
4. Test on staging: verify all cases (normal, error, edge).
5. Deploy to production with stepwise rollout and monitoring.

Timelines and what's included

Note: what is included:

• Documentation of all transformations.
• Gateway configuration files.
• Test scenarios for auto-verification.
• Team training (video and text instructions).
• Support for 2 weeks after deployment.

Why trust transformation to professionals?

Experienced engineers with Kong and AWS certifications guarantee backward compatibility for all existing clients. In over 5 years of work, we have completed 30+ projects, each with its own specificity. We do not just configure transformation — we design a solution that scales and is easy to maintain. Contact us to get a free audit of your API. We will analyze the current architecture and propose the optimal configuration.

Typical mistakes when configuring transformation

The most common mistake is filtering fields only at one level: e.g., removing headers in response but forgetting the request. As a result, sensitive data still leaks. The second typical oversight is incorrect error handling: the Gateway may return an internal stack trace to the client if error mapping is not configured. The third is ignoring caching: without proper cache headers, clients cache dynamic data. We account for these nuances and configure transformation comprehensively.

Our experience — over 5 years working with API Gateway, 30+ successful projects. Certified engineers in Kong and AWS. Contact us to discuss your project — get a free audit of the current 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.