REST: The Established Standard
REST uses HTTP methods (GET, POST, PUT, PATCH, DELETE) and resource-based URLs to perform operations on data. A REST API for a document management product might have endpoints like GET /documents, POST /documents, GET /documents/:id, and DELETE /documents/:id. Each endpoint returns a fixed data structure defined by the server. REST is the baseline expectation for web APIs. Every HTTP client, every programming language, and every API testing tool supports it. Documentation is straightforward. Caching works naturally with HTTP caching semantics at the GET endpoint level. Authentication patterns (Bearer tokens, API keys) are well-understood. For most AI products, REST is the path of least resistance: it is fast to implement, widely understood by any developer consuming the API, and does not require a special client library. The main limitation of REST is over-fetching and under-fetching. Over-fetching means returning more data than the client needs (a list endpoint that returns full document objects when the client only needs titles and IDs). Under-fetching means requiring multiple API calls to assemble the data needed for a single view (fetching a user, then their documents, then each document's tags).
GraphQL: Flexibility at the Cost of Simplicity
GraphQL, developed by Meta and open-sourced in 2015, lets clients specify exactly what fields they need in a query. Instead of calling GET /documents and receiving a fixed structure, a GraphQL client sends a query that says give me the id, title, and createdAt of all documents. The server returns exactly that. No over-fetching, no under-fetching. A single GraphQL request can fetch related data that would require multiple REST calls: give me all documents, and for each document the author's name and the first three tags. This is powerful for complex client applications with many different data needs. The cost is complexity. A GraphQL API requires more setup: a schema definition, resolvers for every field, and a query engine (Apollo Server, Pothos, or similar). Clients need a GraphQL client library (Apollo Client, Urql, TanStack Query with a GraphQL adapter) to benefit from caching and state management. File uploads, streaming, and webhooks require additional tooling beyond the core GraphQL spec. For a team new to GraphQL, the learning curve adds real days to a delivery timeline.
Which Is Better for AI SaaS APIs?
For most AI SaaS products, REST is the better choice for the public-facing API, particularly if you expect third-party developers to integrate with it. The universal tooling support, simpler documentation, and lower integration friction make REST preferable for partner and enterprise integrations. For the internal API between a Next.js frontend and its own backend, either REST (via API routes) or tRPC (which provides type-safe RPC in TypeScript without a formal REST or GraphQL layer) are appropriate choices. GraphQL is worth considering when your product has a complex, graph-like data model where clients need flexible access to related entities (a project management tool where tasks have many relationships to other entities, and different views need very different data shapes). If you are building a developer platform and your primary users are technical developers who will build complex integrations, GraphQL's flexibility is more valuable. For AI products where the primary API operations are task submission and result retrieval, REST is simpler and sufficient.
AI-Specific API Considerations
AI products have specific API characteristics that interact with the REST vs GraphQL choice. Streaming responses (sending partial LLM output to the client as it is generated) are natively supported in REST via Server-Sent Events (SSE) or chunked transfer encoding. GraphQL has a subscriptions mechanism for real-time data, but streaming LLM responses through GraphQL requires specific setup and is less commonly implemented than the REST/SSE approach. Most LLM output streaming implementations (OpenAI streaming, Anthropic streaming) use SSE, and exposing this through your own REST API is straightforward. Exposing it through GraphQL subscriptions adds a layer of complexity without clear benefit. For AI agentic workflows that run asynchronously, the REST pattern of submit a job (POST /jobs), get a job ID, poll for status (GET /jobs/:id), is simple and universally understood. The equivalent GraphQL pattern with subscriptions for real-time job updates is possible but adds client and server complexity.
Caching Differences
REST and GraphQL have fundamentally different caching characteristics. REST GET endpoints can be cached at the HTTP level by CDNs, browsers, and reverse proxies based on the URL. This is automatic, requires no special tooling, and scales well. GraphQL queries are typically sent as POST requests, which HTTP caches do not cache by default. Client-side caching in GraphQL (via Apollo Client's normalized cache) is sophisticated but requires the client library to work and does not benefit from CDN-level caching. For AI products where many read operations return data that changes infrequently (user profile, product list, document metadata), HTTP-level caching of REST endpoints provides real performance benefits with no additional implementation cost. For highly personalised data that cannot be shared across users, the caching advantage of REST is less significant.
Making the Decision for Your Product
The decision process should be straightforward. Start with REST if you are building an API that external developers will integrate with, if your team is not already experienced with GraphQL, if your data model is relatively flat, or if you are under time pressure (as in a 2-3 week MVP). Consider GraphQL if your product has a genuinely complex, interconnected data model, if all of your clients are TypeScript applications where GraphQL's type generation provides significant developer experience benefits, if your team has strong GraphQL experience, and if client-defined data fetching flexibility is a genuine product requirement rather than a hypothetical. tRPC is a strong third option for AI SaaS products where all clients are TypeScript: it provides type-safe, flexible data fetching without the overhead of a REST or GraphQL layer, and is well-suited to the Next.js stack.