REST vs GraphQL vs gRPC: Which API Style Fits Each Boundary

This comparison is for developers, architects and technical founders choosing an API style for a new product, a mobile backend or a set of internal services. It compares REST, GraphQL and gRPC on style, payload, caching, tooling and browser support, gives a decision flow you can walk through in five minutes, and covers the mobile backend question specifically. By the end you will be able to pick a style for each boundary in your system rather than one style for everything.

REST vs GraphQL vs gRPC: the three styles in plain terms

REST models your system as resources at URLs and uses HTTP methods, status codes and headers as the protocol. A customer is at one URL, their orders at another, and the client composes what it needs from several calls. Payloads are usually JSON. It is the most widely understood style and the one every language, proxy, cache and gateway supports without extra work.

GraphQL exposes a typed schema at a single endpoint. The client sends a query describing exactly the fields it wants, across related objects, and receives one response in that shape. Mutations handle writes and subscriptions handle live updates. It shifts control over data shape from the server team to the client team.

gRPC is a remote procedure call framework built on HTTP/2 and Protocol Buffers. You define services and messages in a contract file, generate client and server code in your languages, and call methods as if they were local functions. Messages are binary, connections are multiplexed, and four call types are supported: unary, server streaming, client streaming and bidirectional streaming.

For a deeper look at the first two only, our earlier REST vs GraphQL post goes into schema design and tooling. This article adds gRPC and the decision framework.

API architecture comparison table

Criterion	REST	GraphQL	gRPC
Style	Resources at URLs, HTTP verbs	Typed schema, client-defined queries	Procedure calls defined in a contract file
Payload	JSON, sometimes over-fetched	JSON, exactly the requested fields	Binary Protocol Buffers, smallest and fastest to parse
Transport	HTTP/1.1 or HTTP/2	HTTP, usually POST to one endpoint	HTTP/2 required, with multiplexing
Caching	Native HTTP caching, CDNs, ETags	Client-side normalised caches; HTTP caching needs persisted queries	Application-level only, no HTTP cache semantics
Streaming	Server-sent events or WebSockets added separately	Subscriptions over WebSockets	Built in, both directions
Contract and codegen	OpenAPI, optional	Schema is the contract, strong codegen	Protocol Buffers contract, codegen is mandatory and excellent
Browser support	Full	Full	Needs a translating proxy and a web variant
Learning curve	Low	Medium; server complexity around resolvers and N+1	Medium; tooling and HTTP/2 infrastructure
Error handling	HTTP status codes	200 with an errors array by convention	Status codes defined by the framework
Where it wins	Public APIs, integrations, simple CRUD, cacheable reads	Many clients with different data needs, deeply related data	Internal microservices, high-throughput, streaming, polyglot teams

Payload size and latency

gRPC has the smallest payloads and the fastest parsing because Protocol Buffers are binary and field names are not sent, only numbered tags. For a message with a few dozen fields, the encoded size is commonly a third to a half of the equivalent JSON, and encoding and decoding are cheaper on the CPU. HTTP/2 multiplexing means many calls share one connection without head-of-line blocking at the HTTP layer.

GraphQL reduces payload size by a different mechanism: the client asks only for the fields it needs, so a mobile list screen fetches five fields per item instead of fifty. The cost is on the server, where a single query can fan out into many database calls unless you batch and cache resolvers carefully.

REST payloads are the largest in the naive case because an endpoint returns a fixed shape. Sparse field selection and compression narrow the gap considerably, and for most business applications the difference between a 4 KB and a 9 KB response is invisible next to the database query behind it. Optimise the query plan before you change API styles for performance reasons.

Caching: where REST still wins

REST reads map to GET requests with URLs, so browsers, CDNs, reverse proxies and API gateways cache them with no application code. Cache-Control headers, ETags and conditional requests are decades old and universally supported. A product catalogue or a public pricing endpoint can be served from the edge for pennies.

GraphQL sends most requests as POST to one endpoint, which defeats HTTP caching by default. The common fixes are persisted queries, where the client sends a hash that the server maps to a known query and which can be issued as a GET, plus normalised caches in the client library. Both work, both add setup.

gRPC has no HTTP cache semantics. Caching happens inside your services with an in-memory or distributed cache, which is fine for internal traffic and unsuitable for public read-heavy endpoints that you would prefer a CDN to absorb.

Tooling, contracts and versioning

gRPC forces a contract. The Protocol Buffers file is the single source of truth, code generation produces typed clients and servers in every mainstream language, and a backward-compatible evolution rule set tells you how to add fields without breaking old clients. Teams with services in several languages get the most value here.

GraphQL's schema is also a contract, with introspection, strong editor tooling and generated types for the front end. Versioning is handled by adding fields and deprecating old ones rather than by publishing a version two of the API. Server-side, you need discipline around resolver batching, query depth limits and cost analysis to protect against expensive queries.

REST has the widest tooling because everything speaks HTTP, but the contract is optional. Teams that maintain an OpenAPI description, generate clients from it and validate requests against it get most of the benefits of the other two styles. Teams that skip it end up with undocumented endpoints, which is the most common REST failure we see in codebases we inherit.

Which API style for a mobile backend

Mobile apps have three constraints that shape the choice: unreliable networks, limited battery and the need to support old app versions for months because users do not update promptly.

• REST for a mobile backend: the default for apps with a modest number of screens. Version the API in the URL or a header, keep responses small, and use HTTP caching for catalogue-style data. Every mobile platform has mature HTTP clients, and offline caching libraries expect REST-shaped data.
• GraphQL for a mobile backend: worth it when the same backend serves a web app, an iOS app and an Android app with different screens over a rich domain, such as a marketplace or a social product. Each client fetches exactly what its screen shows, and adding a field for one client does not touch the others. The client library adds to app size and complexity, so it earns its place only when the data needs really do differ.
• gRPC for a mobile backend: possible and used by some large apps, especially for streaming and telemetry, but the tooling on mobile is heavier than REST and the debugging experience is poorer. For most business apps we do not recommend gRPC as the primary mobile API. It is more common to keep gRPC internal and put a REST or GraphQL gateway in front for the app.

Our mobile app development work typically pairs a REST or GraphQL API for the app with whatever internal style the backend uses, and we design the API contract before the first screen is built.

Decision flow: how to choose in five questions

1. Is the consumer outside your organisation, such as partners, customers or a public developer audience? If yes, choose REST with an OpenAPI description. It is the style every integrator already knows and every gateway supports. Stop here for that boundary.
2. Is the consumer a browser or mobile app you control? If yes, go to question 3. If the consumer is another one of your own backend services, go to question 5.
3. Do several clients need noticeably different shapes of the same data across many related types? If yes, GraphQL. If your clients mostly show the same screens over a modest domain, REST.
4. Do cacheable public reads dominate the traffic, such as catalogues, articles or pricing? If yes, prefer REST for those endpoints even if the rest of the app uses GraphQL, so a CDN can absorb the load.
5. For service-to-service calls, do you need low latency, streaming, or strict contracts across multiple languages? If yes, gRPC. If the services are few, written in one language and call each other rarely, REST between them is simpler to debug and perfectly adequate.

Run each boundary in your system through the flow separately. The correct answer for a product is very often a mix, and picking one style for everything usually means fighting that style at one of the boundaries.

What JK Tech Hub has seen across REST, GraphQL and gRPC projects

We build web and mobile products from Rajkot, India for clients in the US, UK, UAE, Australia and Europe, and across those projects REST remains the most common API style by a wide margin. About 70% of the APIs we deliver are REST, roughly 20% are GraphQL, and gRPC appears in about 10% of projects, almost always between internal services rather than facing an app.

On a marketplace product with a web app and two mobile apps for a client in the UK, moving the app-facing API from REST to GraphQL cut the number of network round trips on the main feed screen from six to one and reduced the data transferred per screen by roughly 40%. The trade-off arrived on the server: two resolver N+1 problems reached production before we added batching and query cost limits as a standard part of the setup.

On a logistics platform with services in Node.js and Go, gRPC between the services gave us typed contracts that stopped a whole category of integration bugs, and server streaming replaced a polling loop for vehicle positions. The mobile app still talks REST to a gateway; nobody on the team wanted to debug binary payloads from a phone. On a dealer ordering system for a manufacturing group in Gujarat, plain REST with an OpenAPI file and CDN caching for the price list has run for four years with no reason to change. Our Node.js development page covers the backend stack we use for all three.

How to decide

Choose REST unless you can name the specific problem GraphQL or gRPC solves for you. If the problem is many clients with different data needs, GraphQL at the app boundary is worth its server-side cost. If the problem is internal latency, streaming or contracts across languages, gRPC between services pays for itself. Keep the public and partner boundary on REST regardless.

Related reading and next step

• REST vs GraphQL: a closer look at the two app-facing styles
• Mobile app development with a well-designed backend API
• Web application development services
• Node.js backend development
• How we deliver for clients worldwide

If you want an API designed and built for a web app, a mobile app or a set of internal services, send a short description through the contact page or on WhatsApp at +91 7265004040 and we will reply with a fixed quote within two working days.