The Tool Desk
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Compare the four API styles at a glance
| Decision axis | REST | GraphQL | tRPC | gRPC |
|---|---|---|---|---|
| Interface model | Resources and a uniform interface, commonly using HTTP methods and status codes | A typed graph schema; clients select fields in queries | Procedures whose types are inferred from TypeScript implementation | Declared RPC methods and message schemas |
| Strong fit | Public interfaces, conventional CRUD, and broad HTTP client support | Multiple clients with differing data needs or reads spanning related entities | A full-stack TypeScript application whose team controls both sides | Controlled service-to-service links, polyglot contracts, and streaming |
| Contract workflow | HTTP semantics; OpenAPI is a common optional interface definition | GraphQL schema | Types inferred from the TypeScript implementation | .proto interface definition and generated code |
| Primary advantage | Interoperability, familiar tooling, HTTP semantics, and cache potential | Client-selected response shape and query composition | Low-friction end-to-end typing in a TypeScript codebase | Generated typed clients, binary messages, and streaming |
| Cost to evaluate | Endpoint proliferation or mismatched payloads and round trips; contracts still need discipline | Resolver design, query-cost controls, authorization, and caching strategy | Coupling to TypeScript and the shared code boundary | Schema evolution, code generation, gateway and client compatibility, and operational complexity |
| Key caveat | REST is an architectural style, not simply JSON over HTTP | Flexible queries need guardrails and do not guarantee fewer backend calls | Type-safe does not mean language-neutral | Performance depends on workload and must be measured |
This comparison follows the interface models and tradeoffs described in Microsoft Learn’s API design guidance, the official GraphQL learning material, tRPC documentation, and gRPC documentation. REST’s architectural tradeoffs are also described in Roy Fielding’s dissertation, Chapter 5, “Representational State Transfer (REST)”.
Start with the boundary and its callers
The right interface depends first on who calls it and what contract they can share. A public API has to work for client applications beyond the service team’s control; an internal service boundary may instead prioritize performance, streaming, or a shared language-neutral schema. Microsoft Learn makes this distinction explicitly: “A public API must be compatible with client applications, like browser applications or native mobile applications.” Its API design page was last updated November 20, 2025.
Before choosing a style, identify whether the callers are third parties, browser or mobile applications, internal services, or one full-stack application. Then determine whether consumers need a stable contract independent of implementation language, or whether sharing TypeScript types is an intentional advantage. The same backend may need different answers for different boundaries.
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When REST is the right fit
REST organizes an interface around resources and a uniform set of semantics. In common web APIs, HTTP methods and status codes convey standard meanings, and HTTP with JSON is supported by a wide range of clients and infrastructure. Resource-oriented operations also suit many conventional create, read, update, and delete workflows.
REST is often a practical choice for public interfaces, especially when interoperability and familiar HTTP behavior matter more than letting each client specify a custom response shape. Its uniform interface can reduce coupling between clients and servers, but a standardized representation may include data that a particular application does not need. Poorly chosen endpoints can also force extra requests or return oversized payloads.
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Understand REST’s operational tradeoffs
- Stateless requests: Each request carries the information needed to understand it. This can improve visibility and scalability, but repeated request data may add overhead.
- Caching: Cache constraints can reduce interactions and latency. Cached responses can also become stale, so cache behavior and invalidation need deliberate design.
- HTTP semantics: Use methods, status codes, and idempotency consistently so callers and infrastructure can reason about operations and side effects.
- Contract discipline: REST does not by itself define every API detail. Teams still need a consistent contract and compatibility policy; OpenAPI is a common optional interface definition.
These are tradeoffs of the style, not automatic performance benefits. Fielding’s description of REST explains how statelessness, caching, and a uniform interface shape system behavior. Also, an API that sends JSON over HTTP is not necessarily RESTful in the architectural sense.
When GraphQL is the right fit
GraphQL gives clients a schema and query language for requesting specific fields. It is useful when clients have materially different data needs or when a screen or workflow needs to combine related entities without requiring a new narrowly tailored endpoint for every response shape.
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The official GraphQL learning material describes queries, mutations, subscriptions, validation, resolver execution, and responses that may include both data and errors. That flexibility is a design choice: the server must still determine which callers may access which data and how much work a query can trigger.
Plan for query governance
- Resolvers: Design resolver behavior so fetching related fields does not accidentally create excessive backend work.
- Query cost: Set appropriate limits and protections for expensive or deeply nested requests.
- Authorization: Enforce access rules at the relevant data and operation boundaries rather than assuming the schema alone provides them.
- Caching: Decide how caching works for the queries and data access patterns the service actually supports.
GraphQL does not automatically make an API faster or reduce the number of backend calls. Microsoft’s API design guidance suggests considering query-oriented APIs for diverse data requirements and complex cross-entity filtering. It also identifies reasons to avoid that approach, including simple CRUD needs, strict service boundaries, explicit access-control requirements, or a team without experience implementing query APIs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When tRPC is the right fit
tRPC infers types from a TypeScript implementation and shares them across the client/server boundary, avoiding a separately maintained schema or code-generation step. That makes it appealing when one application team owns both ends of a TypeScript API and wants quick iteration with end-to-end type inference. The official documentation covers adapters, request batching, subscriptions, and integrations.
The tradeoff is the contract boundary: its type model is tied to TypeScript. For independent consumers, teams using other languages, or an API expected to remain stable apart from its implementation, a language-neutral contract may be a better fit. This is an architectural consequence of tRPC’s documented inference model, not a claim that it lacks adapters or integrations. A team can use tRPC internally while exposing a separate interface where external or cross-language consumers need one.
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When gRPC is the right fit
gRPC is an RPC framework built around declared services and messages, typically defined in Protocol Buffers, with generated client and server code. Its binary serialization and streaming capabilities make it a candidate for controlled service-to-service communication, including systems whose services use different programming languages.
Those capabilities come with a schema and code-generation workflow that teams must maintain as interfaces evolve. The client path matters too: public browser-facing applications may need a translation layer, depending on the client stack and supported protocol path. Verify that gateways, proxies, service meshes, authentication policies, monitoring, and deployment tooling work with the intended setup.
Microsoft describes gRPC interfaces as typically faster than REST over HTTP, but that is qualitative guidance, not a universal speed guarantee or a comparison benchmark across all four styles. Serialization speed and payload size are only part of end-to-end behavior; test representative requests on the target system before selecting gRPC for performance reasons.
Quick Recap
How to choose for a cloud-native backend
- List the callers. Separate public third parties, browser and mobile clients, internal services, and a single full-stack application. Their compatibility and performance constraints may differ.
- Set the contract requirement. Decide whether consumers need a stable, language-neutral contract or can share a TypeScript implementation contract.
- Map the interaction shape. Determine whether the boundary is mainly resource operations, client-selected fields, command-style procedures, streaming, or asynchronous workflows.
- Check the delivery path. Confirm compatibility with gateways, proxies, service meshes, browser and mobile clients, authentication policies, monitoring, and deployment tooling.
- Compare costs and failure modes. Account for resolver and query governance, REST endpoint and payload design, TypeScript coupling, or gRPC schema and code-generation workflows as applicable.
- Load-test representative requests. Measure the workload and operational behavior that matter to this boundary. Microsoft’s API design guidance recommends early performance and load testing for REST scenarios; no universal four-way benchmark establishes one style as fastest in every workload.
- Use a hybrid when boundaries differ. Assign each boundary the interface that suits its callers, and document where translation occurs and which team owns each contract.
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