REST vs. GraphQL vs. gRPC: The Battle for Your Backend Architecture
REST vs. GraphQL vs. gRPC: The Battle for Your Backend Architecture
For a long time, the answer to “How should our services talk to each other?” was simply: REST. It was the default. But in 2025, with the explosion of complex microservices and bandwidth-constrained mobile clients, “defaulting” to REST can be a costly architectural mistake.
We now have three heavy hitters in the ring: the reliable veteran (REST), the flexible graph (GraphQL), and the speed demon (gRPC).
I want to break down how to choose between them, because the “best” choice is almost entirely dependent on your specific constraints regarding caching, payload size, and developer experience.
- REST: The Standard for Public APIs
REST remains the king of caching. Because it leverages standard HTTP caching mechanisms (ETags, Cache-Control headers), it is incredibly robust for content that doesn’t change every millisecond.
- Best for: Public-facing APIs, simple CRUD applications, and systems where intermediary proxies need to cache responses to save load.
- The Downside: The dreaded “Over-fetching” (getting too much data) and “Under-fetching” (needing to make 5 calls just to assemble one screen).
- GraphQL: The Frontend Developer’s Dream
GraphQL solved the rigidity of REST. It gives the client the power to ask for exactly what it needs and nothing more.
- Best for: Mobile applications (where bandwidth is expensive), complex dashboards with nested data, and teams where frontend and backend need to iterate independently.
- The Downside: Caching is hard. Because almost every request is a POST to a single endpoint, you lose the free HTTP caching capabilities of REST. You often have to build complex application-level caching logic.
- gRPC: High-Performance Internal Comm
gRPC uses Protocol Buffers (Protobuf) a binary format that is much smaller and faster to serialize than JSON. It runs over HTTP/2, supporting bidirectional streaming.
- Best for: Low-latency internal microservices (Service-to-Service), real-time streaming, and polyglot environments (where Service A is Python and Service B is Go).
- The Downside: It’s binary, meaning you can’t just curl it and read the output easily without tools. Browser support also generally requires a proxy (like Envoy) to translate HTTP/1.1 to gRPC web.
The “Hidden” Variable: Versioning
This is where the headaches start.
- REST: Usually requires “global” versioning (e.g., /v1/users, /v2/users). It’s clean but can lead to code bloat.
- GraphQL: Encourages “Evolution” rather than versioning. You depreciate old fields and add new ones, but you technically never break the schema.
gRPC: Protobuf has built-in backward compatibility rules (numbering fields). If you stick to the rules, old clients can still read new messages.
That’s a really good breakdown of how each option fits different backend needs. It’s easy to see why REST, GraphQL, and gRPC each have their sweet spot depending on the performance requirements and how teams work.
I especially liked the point about versioning, which often gets overlooked until it becomes a problem. The way GraphQL handles schema evolution vs. gRPC’s built-in compatibility rules really shows how important long-term maintainability is when choosing an architecture.
Great write-up!