REST vs GraphQL: When to Use Each (And Why It Matters Less Than You Think)

REST vs GraphQL: which is better? REST APIs use multiple endpoints with fixed data structures (GET /users, GET /users/1/posts). GraphQL uses a single endpoint where clients specify exactly what data they need. REST is simpler, more cacheable, and has wider tooling support. GraphQL reduces over-fetching, enables flexible client queries, and works well for complex data relationships. For most web applications, REST is the simpler, better-supported choice. GraphQL makes sense when you have multiple client types (web, mobile, third-party) with different data needs or deeply nested, relational data.

Here's the thing nobody wants to say out loud: for the vast majority of web applications, both REST and GraphQL work fine. The choice between them will not make or break your project. Your database queries, your caching strategy, and your frontend architecture matter far more than which API paradigm you pick.

We've seen teams spend weeks debating this decision, only to build an app that would have worked identically with either approach.

That said, there are real differences. And in specific situations, one is genuinely better than the other. This guide covers those situations honestly — without the tribal loyalty that usually poisons this conversation.

The comparison at a glance

Factor	REST	GraphQL
Simplicity	Simple — HTTP verbs, URLs, status codes	More complex — schema, resolvers, query language
Caching	Native HTTP caching (CDN, browser, proxy)	Requires custom caching (no native HTTP cache)
Tooling	Massive — Postman, Swagger, every HTTP client	Growing — Apollo, Relay, GraphiQL, but smaller ecosystem
Flexibility	Fixed responses — server decides the shape	Flexible — client requests exactly what it needs
Learning curve	Low — most developers already know it	Medium — new concepts (schemas, resolvers, fragments)
Over-fetching	Common — endpoints return fixed payloads	Eliminated — clients request only needed fields
Under-fetching	Common — multiple requests for related data	Eliminated — nested queries in one request
File uploads	Native support (multipart/form-data)	Awkward — requires workarounds or separate REST endpoint
Real-time	Webhooks, SSE, polling	Built-in subscriptions
Error handling	HTTP status codes (clear, standard)	Always returns 200 — errors in response body (confusing)

Neither column is all green. That's the point.

When REST wins

Simple CRUD applications

If your API is mostly create-read-update-delete operations on well-defined resources — users, products, orders, posts — REST maps perfectly to this model. GET /products, POST /orders, PUT /users/123. Clean, predictable, understood by every developer on Earth.

GraphQL adds overhead here (schema definition, resolver boilerplate, query complexity) without providing meaningful benefits.

Public APIs and third-party integrations

If external developers will consume your API, REST is the safer choice. Every developer knows REST. Every HTTP client supports it. Documentation tools like Swagger/OpenAPI are mature and standardized.

GraphQL has a steeper learning curve for consumers, and exposing a flexible query language to external users introduces security considerations (query depth limits, rate limiting by query complexity) that REST doesn't have.

Caching-heavy applications

REST APIs can be cached at every layer — browser, CDN, reverse proxy, application — using standard HTTP headers. GET /products/123 returns the same response every time, and caching it is trivial.

GraphQL sends POST requests to a single endpoint with varying request bodies. Standard HTTP caching doesn't work. You need application-level caching (Apollo Client cache, persisted queries, CDN-level query caching). It's solvable, but it's extra work that REST gets for free.

Small teams with limited bandwidth

REST is less infrastructure. No schema server. No code generation. No query complexity analysis. If your team is small and you need to ship fast, the simplicity of REST means less to set up, less to maintain, and fewer things to debug.

When the API is internal and the client is known

If you're building an API for a single frontend that you also control, you can design your REST endpoints to return exactly what the frontend needs. The "over-fetching" problem that GraphQL solves is largely a problem of API design, not API paradigm. A well-designed REST API for a known client doesn't over-fetch.

Microservices and service-to-service communication

Service-to-service communication within a backend is almost always REST (or gRPC for high-performance needs). GraphQL adds unnecessary complexity for machine-to-machine calls where both sides are known and fixed. Keep GraphQL for client-facing APIs if you use it at all.

When GraphQL wins

Multiple client types with different data needs

This is GraphQL's strongest use case. If the same backend serves a web app, a mobile app, a partner API, and an admin dashboard — each needing different subsets of the same data — GraphQL shines. The mobile app requests only the fields it needs (saving bandwidth). The admin dashboard requests everything. No one is over-fetching or under-fetching.

With REST, you'd either build separate endpoints for each client (maintenance nightmare) or accept that some clients get more data than they need.

Complex, deeply nested data relationships

When your data model has deep relationships — users who have organizations that have projects that have tasks that have comments that have authors — fetching this in REST means either multiple sequential requests (slow) or custom "include" parameters that reinvent GraphQL poorly.

GraphQL handles nested queries naturally. One request, one response, exactly the shape you need.

Rapid frontend iteration

When the frontend team is iterating quickly — changing what data appears on each page, experimenting with layouts, A/B testing different views — GraphQL lets them adjust queries without waiting for backend changes. No new endpoints. No "can you add this field to the response?" pull requests. The frontend team is unblocked.

This matters most in larger organizations where frontend and backend teams operate independently.

Strongly typed API contracts

GraphQL schemas are self-documenting. The schema defines every type, every field, every relationship. Tools like GraphQL Code Generator produce TypeScript types directly from the schema — meaning your frontend code has compile-time guarantees that it's using the API correctly.

REST can achieve this with OpenAPI/Swagger, but it's opt-in and often incomplete. GraphQL makes it mandatory.

Evolving APIs without versioning

Adding fields to a GraphQL schema is non-breaking — existing clients simply don't query the new fields. Deprecating fields is explicit (@deprecated directive) and tooling warns consumers. REST APIs typically require versioning (v1, v2) to handle breaking changes, which means maintaining multiple versions simultaneously.

The overhyped claims

"GraphQL is faster than REST"

No. GraphQL doesn't magically make your database faster. If your resolver hits the same database with the same query, performance is identical. In fact, GraphQL can be slower because of:

• N+1 query problem. A naively implemented GraphQL resolver for users { posts { comments } } can trigger hundreds of database queries. DataLoader solves this, but you have to implement it.
• Query complexity. Clients can request deeply nested data that your server didn't anticipate, creating expensive queries. REST endpoints have predictable performance because the server controls the query.
• No HTTP caching. As mentioned, you lose the entire HTTP caching layer unless you add it back yourself.

"REST is outdated"

REST is the backbone of the internet. Every major API — Stripe, Twilio, GitHub (which also offers GraphQL), AWS — offers REST. It's not going anywhere. Calling it outdated is like calling HTTP outdated.

"You should use GraphQL for everything"

GraphQL is a tool, not an ideology. Using it for a simple CRUD API with one client is over-engineering. Using it for a complex data platform with multiple clients is smart engineering. Context matters.

Practical considerations

Team experience matters more than you think

If your team knows REST and has never used GraphQL, adopting GraphQL means weeks of ramp-up — learning schemas, resolvers, DataLoader, caching strategies, error handling patterns. That's weeks not spent shipping features.

If your team already knows GraphQL, the reverse applies.

Our advice: Don't choose a technology that requires your team to learn an entirely new paradigm unless the benefits clearly justify the ramp-up cost.

Tooling maturity

REST tooling is decades mature. Postman, Swagger, curl, every HTTP library in every language. Monitoring, rate limiting, authentication — all well-established patterns.

GraphQL tooling is good and improving — Apollo, Relay, Hasura, GraphiQL, Altair — but it's younger. Edge cases are less documented. Stack Overflow answers are fewer. When something goes wrong, you're more likely to be the first person to encounter the issue.

The hybrid approach works

This is our most practical recommendation: use REST as your default, and add GraphQL for specific complex endpoints where it genuinely helps.

Many teams successfully run a REST API for standard CRUD operations alongside a GraphQL endpoint for complex data-fetching scenarios. You get REST's simplicity for 80% of your API and GraphQL's flexibility for the 20% that needs it.

This isn't a cop-out. It's practical engineering. GitHub does this. Shopify does this. It works.

Common mistakes with REST

1. Deeply nested URLs. /users/123/posts/456/comments/789/likes — this isn't RESTful, it's a mess. Keep URLs shallow.
2. Inconsistent naming. /getUsers, /fetch-products, /order_list — pick a convention and stick to it.
3. Ignoring HTTP methods. Using POST for everything defeats the purpose. Use GET, POST, PUT/PATCH, DELETE appropriately.
4. No pagination. Returning 10,000 records in one response because you didn't implement pagination. Always paginate.
5. No versioning strategy. Breaking changes without versioning breaks every client consuming your API.
6. Returning entire database rows. Sending every column to every client because it's easier than curating responses. This is the "over-fetching" problem GraphQL claims to solve — but well-designed REST endpoints solve it too with response shaping.

Common mistakes with GraphQL

1. Skipping DataLoader. Without it, nested queries generate N+1 database queries that destroy performance. This is the single most common performance issue in GraphQL servers.
2. No query depth limits. Malicious or careless clients can request infinitely nested queries that crash your server. Set limits from day one.
3. Over-exposing the schema. Your GraphQL schema doesn't need to mirror your database. Design it for client needs, not database structure. Exposing internal IDs, join tables, or audit columns is a security and maintenance liability.
4. Ignoring caching. Assuming "Apollo Client handles caching" without understanding cache invalidation strategies. Client-side caching is not a replacement for server-side and CDN caching.
5. Using GraphQL for file uploads. Just use a REST endpoint. GraphQL file uploads are painful and unnecessary.
6. Schema sprawl. Adding every possible field and relation to the schema "because someone might need it." A bloated schema is harder to maintain, slower to introspect, and confusing for consumers.

Our recommendation

For most projects we build at Hunchbite — Next.js applications with TypeScript backends and PostgreSQL databases — REST is our default. It's simpler, faster to set up, easier to cache, and our clients' applications rarely have the multi-client complexity that justifies GraphQL.

We reach for GraphQL when:

• The project has multiple distinct client types with genuinely different data needs
• The data model is deeply relational and the frontend needs flexible access patterns
• The client specifically requests it and has a team experienced with it

For everything else, a well-designed REST API with clear endpoints, consistent patterns, and proper documentation serves the project better.

If you're starting a new project and genuinely unsure, start with REST. You can always add a GraphQL layer later for specific use cases. Going the other direction — migrating from GraphQL to REST — is harder and less common.

What about tRPC?

Worth mentioning: if your frontend and backend are both TypeScript and you don't need a public API, tRPC gives you end-to-end type safety without writing API contracts at all. It's not REST or GraphQL — it's a different paradigm entirely. We use it for internal tools and admin dashboards where the client and server are tightly coupled. For public-facing APIs or projects where the backend might serve non-TypeScript clients, stick with REST or GraphQL.

For more on how we approach API architecture, see our API development services. If you're evaluating broader technology decisions for a new product, our glossary covers the key terms and concepts, and our guide on building from idea to live product walks through the full process.

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