API Development Services — REST & GraphQL APIs Built for Scale

APIs are not a feature of modern software — they are the product. Stripe's API generates over $14 billion in annual revenue. Twilio's API powers communications for millions of businesses. Every significant SaaS platform, mobile application, and digital service is fundamentally an API with interfaces built on top of it. The quality of your API architecture determines your development velocity, integration capabilities, operational reliability, and ultimately your ability to grow.

REST (Representational State Transfer) remains the dominant API paradigm, and for good reason. RESTful APIs map cleanly to HTTP semantics — GET for reading, POST for creating, PUT/PATCH for updating, DELETE for removing. This alignment means REST APIs benefit from decades of HTTP infrastructure: CDN caching, load balancers, browser developer tools, and universal client library support. A well-designed REST API is self-documenting through its URL structure and HTTP status codes, and it can be consumed by any programming language that supports HTTP — which is all of them.

GraphQL, developed by Facebook and open-sourced in 2015, addresses specific limitations of REST for complex data-fetching scenarios. Instead of multiple round-trips to different REST endpoints, a GraphQL query requests exactly the data it needs in a single request. This is transformative for mobile applications operating on variable network conditions, where minimizing request count and payload size directly impacts user experience. GraphQL's type system provides compile-time guarantees about the data shape, and its introspection capability means client developers can explore the entire API surface without reading documentation.

At LevnTech, our API development practice is anchored in schema-first design. For REST APIs, we write the OpenAPI 3.0 specification before writing a single line of implementation code. This spec defines every endpoint, request/response schema, authentication requirement, and error format. Frontend and backend teams can work in parallel — the spec is the contract. We generate TypeScript types, API client SDKs, and mock servers directly from the spec, ensuring that the implementation exactly matches the design.

For GraphQL APIs, we follow a similar schema-first approach using SDL (Schema Definition Language). We design the type graph, define queries and mutations, and implement resolvers against the schema. We use DataLoader for batching and caching to solve the N+1 query problem that naive GraphQL implementations suffer from, and we implement query complexity analysis to prevent abusive queries that could overload the database. Persisted queries reduce the attack surface and improve performance by eliminating query parsing overhead in production.

Authentication and authorization are non-negotiable foundations. We implement OAuth 2.0 with PKCE for user-facing applications, short-lived JWT access tokens (15-minute expiry) with refresh token rotation for session management, and HMAC-signed API keys for server-to-server integrations. Authorization is enforced at the resolver or controller level using policy objects — never by checking roles in business logic. This separation means authorization rules can be audited, tested, and modified without touching core application code.

Rate limiting is implemented at multiple layers: nginx or cloud load balancer for coarse IP-based limits, application-level middleware for per-API-key quotas with sliding window algorithms, and endpoint-specific limits for expensive operations. We implement rate limit headers (X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset) so clients can implement backoff logic before hitting limits. Abuse detection goes beyond rate limiting to include request pattern analysis, payload size validation, and geographic anomaly detection.

Our Node.js API implementations use Express or Fastify with a layered architecture: routes, controllers, services, and repositories. Input validation happens at the controller layer using Zod schemas derived from the OpenAPI spec. Business logic lives in services that are framework-agnostic and independently testable. Database access is encapsulated in repositories using Prisma or Drizzle ORM with parameterized queries exclusively — no string concatenation, no SQL injection vectors. Error handling follows a consistent pattern: domain exceptions are caught at the controller boundary and mapped to appropriate HTTP status codes with structured error bodies.

Every API we ship includes comprehensive test coverage: unit tests for service logic, integration tests for API endpoints (testing the full request/response cycle against a test database), and contract tests that validate the implementation matches the OpenAPI or GraphQL schema. We run these tests in CI on every pull request, and a single test failure blocks merge.

REST and GraphQL are not competing standards — they solve different problems, and many production systems use both.

REST APIs are the right default choice for most applications. They map directly to HTTP caching infrastructure (CDNs cache GET requests automatically), they are universally supported by every language and framework, and they are simple to reason about. Each endpoint has one job, one URL, and one response shape. Debugging is straightforward — you can test any endpoint with curl. REST is ideal for CRUD applications, webhook integrations, public APIs consumed by third-party developers, and any scenario where cacheability and simplicity matter more than query flexibility.

GraphQL shines when clients have diverse data needs. A mobile app might need a user's name and avatar, while the web dashboard needs the user's name, email, role, last login, and associated projects. With REST, you either create multiple endpoints (fragmented maintenance) or return all fields everywhere (wasted bandwidth). GraphQL lets each client request exactly the fields it needs. GraphQL is also superior for deeply relational data — a single query can traverse user -> projects -> tasks -> comments without multiple round-trips. Real-time subscriptions via WebSocket are a first-class GraphQL feature.

The downsides of GraphQL include: no native HTTP caching (every request is a POST), increased server complexity from query parsing and execution planning, potential for expensive queries that require complexity analysis, and a steeper learning curve for frontend developers unfamiliar with the query language. Error handling is also less intuitive — GraphQL returns 200 OK even when queries partially fail, requiring clients to inspect the errors array.

Our recommendation: use REST for public APIs, webhooks, simple CRUD, and any API that benefits from HTTP caching. Use GraphQL for internal APIs serving multiple client applications with different data requirements, or for data-rich applications where minimizing network round-trips measurably improves user experience.