When building APIs, the choice between GraphQL and REST isn't just about technical preferences—it directly impacts your SDK strategy, developer adoption, and long-term maintenance overhead. Both approaches can power successful APIs, but they handle data fetching, versioning, and client integration in fundamentally different ways.
This guide breaks down the practical differences between GraphQL and REST from an implementation perspective, covering data workflows, performance trade-offs, SDK generation strategies, and how both approaches work with emerging standards like the Model Context Protocol for AI agents.
What are GraphQL and REST APIs
The fundamental difference between GraphQL and REST lies in how they handle data fetching and structure endpoints. REST APIs expose multiple endpoints, each returning a fixed data structure, which can force clients to make several requests or receive more data than needed. In contrast, GraphQL uses a single endpoint where the client submits a detailed query, allowing it to request and receive only the exact data it requires in one go.
Both approaches are powerful ways to build APIs. Modern tooling can generate SDKs from either, often by converting a GraphQL schema into an OpenAPI Specification first, though you may need to create OpenAPI specs manually for some APIs.
Define request models
REST is an architectural style that uses standard HTTP methods on specific resource URLs. You might GET /tasks to list all tasks or POST /tasks to create a new one. The server dictates the shape of the data you get back from each endpoint.
GraphQL is a query language for your API. Clients send query or mutation operations to a single endpoint, like /graphql. The structure of the request itself defines the structure of the response, giving the client full control.
Describe schema contracts
Both API styles rely on a schema, which acts as a contract between the client and server.
REST: Typically uses the OpenAPI Specification (formerly Swagger). This document defines every endpoint, its parameters, and the shape of its responses.
GraphQL: Uses the GraphQL Schema Definition Language (SDL). This single schema defines all available data types and operations, which clients can also discover programmatically via introspection.
Outline common use cases
While you can build almost anything with either, they each have sweet spots.
REST excels at traditional CRUD (Create, Read, Update, Delete) APIs where resources are clearly defined and interactions are straightforward.
GraphQL is often favored for applications with complex data needs, like mobile apps or frontend Backend for Frontend (BFF) layers, where fetching nested data efficiently is critical.
Compare data workflows
The way data moves between the client and server is where the differences become most practical. These workflows directly impact developer experience and application performance.
Handle overfetching
Overfetching happens when an endpoint returns more data than the client needs.
Imagine you only need user names for a list. A typical REST endpoint like GET /users might return the full user object for each user, including addresses and permissions you don't need. With GraphQL, your query would be query { users { name } }, and you would only receive a list of names.
Enforce schemas
A strong schema is your best defense against runtime errors. Both OpenAPI and GraphQL schemas enable the generation of strongly-typed client libraries. When you generate an SDK, these schemas are used to create types and classes that provide editor autocompletion and catch mistakes before you even run your code.
Manage version changes
APIs evolve, and handling those changes without breaking existing clients is crucial.
REST: Versioning is often handled in the URL, like
/v2/tasks. This creates a completely separate set of endpoints for the new version.GraphQL: The schema can be evolved by adding new fields and deprecating old ones. Clients can adopt new fields at their own pace, and deprecated fields can be removed later.
An automated SDK release flow can manage versioning for either style, creating new release pull requests and updating changelogs based on your schema changes.
Evaluate performance and caching
Performance isn't just about speed; it's about efficiency, reliability, and cost. Here, REST and GraphQL present different trade-offs.
Reduce request count
A common performance issue in REST is the N+1 problem, where you first fetch a list of items, then make N additional requests to fetch related data for each item. GraphQL can solve this by fetching the primary resource and its nested relationships in a single, structured query. However, be cautious of overly complex GraphQL queries, which can strain the server.
Cache responses
REST APIs benefit from standard HTTP caching out of the box. Since each resource has a unique URL, responses can be easily cached by browsers, CDNs, and proxies using headers like ETag and Cache-Control.
GraphQL's single endpoint makes HTTP caching less straightforward. Caching is typically handled at the client level, with libraries like Apollo Client implementing sophisticated normalized caches.
Control query cost
Controlling resource usage is key to maintaining a healthy API.
Feature | REST Approach | GraphQL Approach |
|---|---|---|
Rate Limiting | Based on the number of requests per minute to specific endpoints. | Based on query complexity or depth analysis to prevent expensive queries. |
Reliability | Client-side retries with exponential backoff for transient errors. | Client-side retries are also standard practice. |
Idempotency |
|
|
A robust SDK will automatically handle retries with exponential backoff and allow for passing idempotency keys to prevent accidental duplicate operations.
Generate SDKs for GraphQL and REST with Stainless
Regardless of which API style you choose, a high-quality SDK is no longer a nice-to-have; it's a strategic necessity. It improves the developer experience, reduces support load, and provides valuable analytics once the SDK ships.
You can use the Stainless SDK generator to create idiomatic, production-ready SDKs from any OpenAPI specification. If your API uses GraphQL, you can use one of several open-source tools to convert your GraphQL schema and operations into an OpenAPI document, then use that to generate your client libraries.
Build client libraries
The generation process is designed for automation. You provide your OpenAPI spec and a configuration file, and the generator produces SDK code in a staging repository. Once you connect a production repository that you own, new versions are pushed there, ready for publishing.
Maintain backward compatibility
Keeping SDKs in sync with a rapidly changing API can be a major challenge. A modern generation pipeline automates this by creating a pull request for each new release. The version number is automatically bumped based on semantic changes detected in your API spec, and a detailed changelog is generated.
Add custom helpers
You can add custom code, like domain-specific helper methods or detailed examples, directly to the generated SDK repository. These changes are preserved across future generations, allowing you to build rich libraries on top of the generated foundation.
Expose APIs to AI agents with MCP
The next frontier for APIs is making them accessible to AI agents. The Model Context Protocol (MCP) is an open standard that allows LLMs to discover and interact with APIs as "tools." You can automatically generate an MCP server from an OpenAPI spec, making your API instantly AI-ready.
Map endpoints to tools
The generator maps your API endpoints directly to MCP tools. For example, a POST /tasks endpoint becomes a create_task tool that an LLM can understand and invoke. The tool's input schema is derived from your endpoint's parameters, ensuring the LLM provides the correct data.
Control schema complexity
LLMs work best with simple, clear schemas. An MCP server generator can automatically transform complex OpenAPI schemas to be more AI-friendly, addressing many of the challenges encountered when converting complex OpenAPI specs to MCP servers.
Deploy remote servers
While local MCP servers are great for developers, remote servers are needed for web-based AI applications. You can deploy your generated MCP server as a Cloudflare Worker, which includes support for OAuth-based authentication flows, or package it as a Docker image for easy deployment anywhere.
Choose the right approach for your API
The "GraphQL vs. REST" debate isn't about picking a winner; it's about choosing the right tool for the job. Your decision should be based on your product's needs, your team's expertise, and your long-term goals.
Assess data shaping needs
If you have a diverse set of clients that all require different views of the same data, GraphQL's flexibility is a major advantage. If your data models are straightforward and clients typically need the full resource, REST is often simpler.
Evaluate team skill sets
REST is built on fundamental web principles that most developers already understand. GraphQL introduces new concepts, tooling, and a learning curve for both frontend and backend engineers. Consider your team's current skills and their capacity to adopt a new stack.
Plan long-term maintenance
Think about how your API will evolve. REST's URL-based versioning is explicit but can lead to maintaining multiple versions. GraphQL's schema evolution is more fluid but requires careful management of deprecated fields. An automated SDK pipeline significantly eases the maintenance burden for either approach.
Frequently asked questions about GraphQL vs REST
What is one benefit of GraphQL over REST approaches?
GraphQL's primary benefit is allowing clients to request exactly the data they need and nothing more, which solves the problems of overfetching and underfetching in a single request.
What is the difference between GraphQL and REST versioning?
REST typically versions the entire API via the URL path (e.g., /v2), creating distinct API surfaces. GraphQL APIs evolve by adding new fields and marking old ones as deprecated within a single, versionless schema.
Why move from REST to GraphQL?
Teams often migrate to GraphQL to improve mobile application performance, simplify data fetching for complex UIs, and give frontend developers more autonomy over the data they consume.
Can I generate SDKs for both GraphQL and REST APIs?
Yes, you can generate SDKs for both by using a tool to convert your GraphQL schema into an OpenAPI specification, which can then be used by an SDK generator.
How do GraphQL and REST APIs work with AI agents?
Both can be exposed to AI agents via a Model Context Protocol (MCP) server, which translates API endpoints into "tools" that an LLM can discover and use.
Whether you choose REST, GraphQL, or a hybrid approach, providing a great developer experience is key. Get started for free and see how a first-class SDK can transform your API.