gRPC vs REST for Public-Facing API Design
REST vs gRPC for public APIs isn't a speed contest, no matter how many benchmark decks say otherwise. The real decision comes down to who's calling your API, from what device, and under what network conditions, and those constraints usually point in one direction before performance ever enters the conversation. Most teams default to REST out of habit and never run the analysis; gRPC advocates counter with throughput numbers and call it settled. Both sides are arguing about the wrong thing, and the data backs that up. According to the RapidAPI Developer Survey cited by JSONConsole, 83% of public APIs ran on REST as of Q4 2024, and that number says more about inertia than it does about anyone doing the math.
What REST and gRPC actually are under the hood
REST is a style, a set of conventions for using HTTP the way it was already built to work. Resources live at URLs, verbs like GET, POST, PUT, and DELETE say what you want to do to them, and the whole thing is stateless and cacheable because HTTP already gives you that for free.
gRPC came out of Google in 2015, built first for talking between internal microservices, then open-sourced once it proved itself. It rides on HTTP/2 and serializes data with Protocol Buffers instead of JSON. Three things combine to make gRPC behave the way it does: HTTP/2 multiplexing lets you run many requests over one connection without paying setup cost each time, HPACK header compression shaves bytes off every request, and Protobuf packs data into a compact binary format defined by a schema.
Here's why REST's plain old HTTP approach still matters for public APIs. Its verbs map cleanly onto CDN behavior, GET is cacheable, POST isn't, and that distinction runs the whole edge caching model. JSON is readable by a human being with zero tools, so curl, browser dev tools, and Postman all just work, and nobody needs to install anything special to call a REST endpoint. No numbers yet on speed, since that comparison only makes sense once we've looked at how each one is built, which is what we're doing right now.
The browser compatibility wall that makes gRPC a non-starter for many public APIs
Here's the flat truth: you cannot call a gRPC service directly from a browser, and it's structurally impossible, not merely inconvenient. Browsers don't give you fetch or XMLHttpRequest access to raw HTTP/2 frames, and gRPC needs that access to function. No API exposes it, so there's nothing to configure your way around.
Any public API with a web app in its list of consumers can't ship plain gRPC, full stop. You need something standing between the browser and the service.
Option one is gRPC-Web, a JavaScript library that lets browsers talk to gRPC through a proxy, usually Envoy. Sounds good until you hit the catch: client-side streaming and bidirectional streaming don't work in gRPC-Web. Those are the two features that usually justify picking gRPC over REST in the first place, and they're gone.
Option two is ConnectRPC, built by Buf. It's wire-compatible with gRPC and uses the same proto schemas, but it's framed to work natively in browsers and at the edge, no Envoy required. Unary JSON requests through ConnectRPC behave like ordinary REST calls, complete with real HTTP status codes your existing monitoring already understands. Anthropic runs its SDKs on ConnectRPC, including a ConnectRPC library it maintains in Rust, which tells you this isn't a toy solution. Adopting it, though, means you've made a separate protocol decision, and you're not using off-the-shelf gRPC clients anymore.
If your API needs to be called straight from a browser and you want streaming that actually works both ways, REST or a deliberate ConnectRPC adoption is the real path, since gRPC-Web only gets you partway there and stops.
How gRPC's POST-only design breaks CDN caching for read-heavy public APIs
Every gRPC call, by default, goes out as an HTTP POST. POST isn't safe, isn't idempotent, and CDNs like Cloudflare, Fastly, and Akamai won't cache it, no matter how cacheable the underlying data actually is. On top of that, gRPC ignores ETag, Cache-Control, and Last-Modified headers entirely, because it operates a layer above plain HTTP.
The result is that every single read hits your origin server. For a high-traffic public API, that means your infrastructure bill scales in a straight line with request volume, with no edge offload and no relief.
Vercel's platform assumes responses can be cached at the edge and that GET is a safe, repeatable operation; gRPC breaks that assumption by design, not by accident. Buf's Connect protocol has added support for HTTP GET, which lets Connect RPCs get cached at the edge, but that's a Connect feature, not something vanilla gRPC does on its own.
A well-configured REST API can serve most of its read traffic straight from cache, never touching the origin at all, and that's just how the architecture works. For any public API where reads dominate and caching is part of your cost model, REST has a structural edge gRPC can't close without stepping outside its own stack.
Where gRPC's performance advantage is real and where it disappears
The benchmarks aren't fake. Tech Insider reported gRPC delivering up to 77% lower latency on small payloads in April 2026, 2.3 ms versus 10.1 ms at the p50 mark. A 2025 microservice study from Markaicode clocked gRPC at 25,800 requests per second against REST's 12,450, with average latency of 12.8 ms versus 24.5 ms. Under the same load, gRPC used 19% less CPU, 34% less memory, and 41% less bandwidth in that same study.
Here's the twist even those benchmarks admit: on local tests, REST can actually beat gRPC on latency for small payloads, while gRPC pulls ahead as payload size grows and connections stay open longer. This is payload- and topology-dependent rather than a universal win, and anyone quoting the top-line number without that context is skipping the fine print.
None of it transfers cleanly to public API design, either. Callers are out on the open internet, so network round-trip time swamps whatever you saved on serialization. Edge caching, REST's home-field advantage, wipes out origin latency for anything cacheable; no throughput number beats a cache hit, because a cache hit doesn't touch your servers at all. Your callers, on top of that, are strangers running whatever client they picked, not a fleet of internal services you control.
Where gRPC's speed actually pays off: internal microservice meshes with known callers and long-lived connections, mobile apps talking to a backend over constrained bandwidth, and AI inference or IoT pipelines pushing high-frequency writes. Square's fraud-detection platform is the honest best-case example. Handling 200,000 transactions per second with sub-100ms decisions, Square moved its inference path from REST plus WebSockets to bidirectional gRPC streaming in 2024, and saw p99 latency drop 35% with a 60% cut in connection count per node. Worth noting, though, that this is an internal inference pipeline, not a developer-facing public API, so it's a different animal entirely.
Streaming: the one capability where gRPC genuinely has no REST equivalent
gRPC gives you four service types, all defined right in the proto schema and strongly typed: unary, server streaming, client streaming, and bidirectional streaming. REST has no native version of any of the streaming ones. You can approximate server streaming with Server-Sent Events or long polling, approximate client streaming with chunked upload, and approximate bidirectional streaming with WebSockets, but that last one means negotiating an entirely separate protocol.
These workarounds aren't broken, to be clear, and SSE and WebSockets are mature, well-supported, and used everywhere. What you lose is the single schema that makes gRPC streaming type-safe end to end; REST's version stitches together multiple protocol layers instead of one clean contract.
Bidirectional streaming earns its keep on a public API in a few specific cases: live data feeds where both sides push and receive at once (trading data, multiplayer game state, live telemetry), AI inference with streaming token output (the pattern behind Anthropic's SDK, running on ConnectRPC rather than vanilla gRPC but sharing the same proto schemas), and IoT devices that report status and receive commands over one persistent connection.
Remember the browser problem from earlier, though. gRPC-Web strips out client-side and bidirectional streaming, which means the one thing gRPC does that REST genuinely can't disappears exactly where most public API consumers are sitting: in a browser tab. Streaming is a real reason to pick gRPC for a public API, but only when your consumers are native clients, mobile apps, servers, CLIs, and only when the streaming pattern actually runs both directions instead of just pushing data one way.
Developer experience gap between REST and gRPC for third-party API consumers
REST's biggest developer-experience win is that it asks nothing of you upfront. Any HTTP client can call it, with no generated stubs, no proto files, and no client library to install first, and curl or a browser tab get you exploring the API in under a minute.
JSON adds to that. Responses show up readable in a browser, in Postman, in a terminal, with no decoding step, and a frontend developer can poke at a payload without knowing what a schema even is.
gRPC asks more of everyone who touches it. Callers need the.proto file just to generate a client, and handing that file out to a large, changing pool of third-party developers turns into its own coordination job. Protobuf responses are binary, so debugging without the right tooling means decoding bytes by hand. Some corporate firewalls and proxies, on top of that, still have known problems with HTTP/2, which can quietly break gRPC calls in ways that never show up on a REST integration.
To gRPC's credit, the tooling has come a long way. Postman, with more than 30 million users, added full gRPC support in 2023, streaming, reflection, proto file management, the works. Buf runs a schema registry with breaking-change detection, lint rules, and code generation across languages, which cuts down on schema drift for teams running a lot of proto services. OpenTelemetry plugs into gRPC servers and clients for observability, too.
None of that closes the real gap, though. Those tools help internal teams that already run Buf and gRPC reflection in their pipeline. A third-party developer hitting your public API for the first time is far more likely to open curl or a browser when something goes sideways, and gRPC doesn't meet them there. If broad developer adoption is the goal, whether that's a public developer platform, a partner integration program, or an open ecosystem, REST's zero-setup accessibility carries real weight, and it's often the deciding factor.
Schema evolution and versioning over a public API's lifetime
Protobuf bakes backward compatibility into the wire format itself. Add an optional field and old clients just ignore the new tag number, no harm done. Remove a field and reserve its tag number, and you're forward-compatible too, and rename a field and the wire format is unaffected, because Protobuf uses tag numbers rather than field names to identify data.
REST plus JSON has no such formal guarantee. Adding a field is usually fine, but removing one breaks any client still depending on it, and renaming a key breaks every client that reads it. None of that is enforced anywhere; it's a social agreement between you and whoever's calling your API, not something the protocol checks for you.
For internal teams, this is gRPC's strongest long-term case. Schema drift between services causes some of the worst operational headaches in large gRPC deployments, and Buf built its entire schema registry business around solving exactly that problem.
Public APIs shift the math, though. REST's URL versioning, /v1/, /v2/, is something any third-party developer already understands without touching new tooling. Shipping updated.proto files out to thousands of external consumers is its own coordination headache, one that eats into the formal guarantees Protobuf gives you on paper. REST teams can adopt schema tooling of their own, closing part of the gap.
Net result: gRPC's schema model is the better engineering answer when you control both ends of the wire. Once your consumers are a scattered, uncontrolled crowd, REST's versioning conventions are the simpler operational choice, even if they're less rigorous on paper.
The operational overhead gRPC adds that public API teams rarely anticipate
Choosing gRPC for a public API means taking on infrastructure most REST teams never think about. You need HTTP/2 support end to end, proxy layers like Envoy if browsers are anywhere in the picture, and a process for versioning and distributing proto files to everyone who calls your service. None of that is exotic if you're already running a service mesh internally, but it's a new cost if your public API team has spent its whole existence running plain REST behind a load balancer.
Debugging changes shape, too. Support tickets that used to get solved by asking a customer to paste a curl command now require someone on your side to decode a Protobuf payload before they can even see what went wrong. Firewall and proxy issues with HTTP/2 show up in the field in ways that are hard to reproduce and harder to explain to a partner who just wants their integration to work.
None of this makes gRPC the wrong choice, but it makes it a choice with a bill attached, one that internal microservice teams often don't notice because their infrastructure already absorbed it years ago. Public API teams adopting gRPC for the first time are paying that bill fresh, usually without budgeting for it up front.
The decision comes down to who's calling the API, what device they're on, and whether your cost model depends on the edge doing most of the work, not some abstract measure of "better." Answer those three questions honestly, and the protocol choice tends to make itself.
