architecture

gRPC: What It Is and When to Use It for AI Microservices

A high-performance RPC framework by Google using Protocol Buffers for serialisation, enabling efficient service-to-service communication in microservices.

gRPC is a high-performance, open-source remote procedure call (RPC) framework developed by Google. It uses Protocol Buffers (protobufs) as its interface definition language and wire format, HTTP/2 for transport, and supports bi-directional streaming. gRPC enables efficient service-to-service communication in microservices architectures, particularly for high-throughput, low-latency scenarios. For AI products, gRPC is relevant in two specific contexts: service-to-service communication within a distributed AI backend, and streaming inference results from a model serving layer to an application server. For most AI MVPs, REST or tRPC is the more pragmatic choice, but understanding gRPC helps you make informed architecture decisions as your product scales. The most practical reason a UK AI startup would encounter gRPC is when integrating with a self-hosted model serving layer. NVIDIA Triton Inference Server, TensorFlow Serving, and TorchServe all expose gRPC inference APIs as their primary interface. If your AI product runs open-source models on GPU infrastructure rather than calling a commercial API, gRPC is the natural interface to that serving layer. For everything else in a typical Next.js AI product, the performance advantages of gRPC over REST are not detectable in the user experience because LLM inference latency dominates total request time by orders of magnitude. The binary Protocol Buffer format is 5-10 times more efficient than JSON, but when the AI task itself takes 5-30 seconds, the 2 milliseconds saved on serialisation are irrelevant. Make the gRPC decision based on your actual infrastructure requirements, not on general performance reputation.

How gRPC Works

gRPC defines service interfaces using Protocol Buffer (.proto) files. A .proto file specifies the service methods, the message types they accept, and the message types they return. From this definition, gRPC tooling generates client and server code in any supported language (Go, Python, Node.js, Java, C++, and others). The client code looks like a local function call, but the call is serialised as a Protocol Buffer binary, sent over HTTP/2 to the server, processed, and the response is deserialised back into the client's language types. The Protocol Buffer binary format is significantly more compact and faster to serialise and deserialise than JSON. In benchmarks, gRPC typically outperforms REST/JSON by a factor of 5-10x in throughput and 2-4x in latency for equivalent operations. For service-to-service communication in a high-throughput AI backend where services are making thousands of calls per second to each other, this difference is meaningful. For an AI MVP making a few hundred API calls per day, it is not.

gRPC Streaming for AI Inference

gRPC natively supports four communication patterns: unary (single request, single response, like REST), server streaming (single request, stream of responses), client streaming (stream of requests, single response), and bidirectional streaming (stream of requests, stream of responses). Server streaming is particularly relevant for AI inference: a client sends a single inference request, and the server streams back partial results as they are generated, exactly the LLM streaming pattern. For self-hosted model serving (running an open-source model like Llama or Mistral on your own GPU infrastructure), gRPC is commonly used as the inference serving protocol. NVIDIA Triton Inference Server, TensorFlow Serving, and TorchServe all support gRPC. If you are building a model serving layer for a self-hosted AI product, gRPC is the natural choice for the inference serving interface.

gRPC vs REST for AI Product APIs

For most external-facing API needs in AI products, REST wins on pragmatism. Every HTTP client, every browser, and every programming language can call a REST API with no special tooling. gRPC requires a generated client, which is straightforward for internal service communication but adds friction for external developer integrations. Browser support for gRPC (specifically the HTTP/2 trailer frames gRPC relies on) requires a proxy layer (gRPC-Web or Envoy) because browsers cannot natively speak gRPC. This adds infrastructure complexity. For AI product APIs consumed by browsers (the most common case), REST or GraphQL is simpler to implement and maintain. gRPC is most compelling for internal microservices that communicate frequently and where the performance overhead of JSON serialisation and HTTP/1.1 creates a measurable bottleneck. At the scale of an AI MVP, this is rarely the case.

Protocol Buffers and Schema Evolution

Protocol Buffers offer a structured approach to schema evolution that is both a strength and a learning curve. Each field in a protobuf message has a field number. Adding new fields is safe as long as you use new field numbers. Removing fields is safe as long as you retire the field number rather than reusing it. Changing field types is generally not safe. This structured approach to schema evolution is more reliable than JSON schema evolution (where you can accidentally change a field's type and break clients). For AI products with strict inter-service contracts between a model serving layer and application servers, protobuf schema evolution rules provide a governance mechanism for API change management. For teams new to protobufs, the learning curve is real: understanding field numbers, required vs optional fields, repeated fields, and the relationship between .proto definitions and generated code takes time.

gRPC in a Next.js AI Stack

Integrating gRPC into a Next.js application requires a proxy layer for browser clients because browsers cannot speak native gRPC. The standard approach is to use Connect-RPC (from Buf), which provides a REST-to-gRPC transcoding layer that allows browser clients to call gRPC services using standard HTTP/1.1 POST requests. Connect-RPC supports the gRPC, gRPC-Web, and Connect protocols from the same server implementation, making it the most practical choice for Next.js integrations. Alternatively, for purely server-to-server communication (Next.js API routes calling a separate gRPC model serving service), native gRPC clients work without a proxy because the Node.js runtime supports HTTP/2 directly. If your architecture includes a self-hosted model serving layer with gRPC interface, your Next.js backend can call it via gRPC, while the browser calls your Next.js API routes via standard REST or tRPC.

When to Consider gRPC for Your AI Product

gRPC is worth considering when: you are building or integrating with a self-hosted AI model serving layer that exposes a gRPC inference interface; you have a microservices architecture with high-frequency inter-service communication where the performance difference between gRPC and REST is measurable; you need bi-directional streaming for an AI application that requires continuous data exchange between client and server (beyond the standard LLM request-response pattern); or you are building in a polyglot environment where multiple services in different languages need to communicate, and protobuf-based code generation provides value. For a team building an AI MVP in 2-3 weeks using Next.js and external LLM APIs, gRPC adds complexity without benefit. The external LLM APIs you call (OpenAI, Anthropic) use REST. Your own API is most efficiently REST or tRPC. Revisit gRPC when you have a concrete performance problem that it solves.

Frequently Asked Questions

Is gRPC faster than REST for AI APIs?+

Yes, typically 5-10x higher throughput and 2-4x lower latency in benchmarks for equivalent operations, due to binary Protocol Buffer serialisation and HTTP/2 multiplexing. However, for AI products where LLM inference latency (seconds) dominates total request time, the API protocol overhead (milliseconds) is not the bottleneck. The performance advantage of gRPC is meaningful in high-throughput microservices communication where services make thousands of calls per second to each other.

Can I use gRPC in a browser-based AI application?+

Not directly. Browsers cannot speak native gRPC because they do not support HTTP/2 trailers, which gRPC requires. Use gRPC-Web (a modified protocol supported by Envoy and Nginx proxies) or Connect-RPC (which supports multiple protocols from a single server) to expose gRPC services to browser clients. For most AI product frontends, REST or GraphQL is simpler than adding a gRPC-Web proxy layer.

What are Protocol Buffers and do I need to learn them to use gRPC?+

Protocol Buffers (protobufs) are gRPC's interface definition language and binary wire format. You define your service interface in a .proto file, and the protoc compiler generates client and server code in your chosen language. You do need to learn protobuf syntax to use gRPC. The .proto format is straightforward but requires understanding field numbers, types, and the import system. Most teams find the learning curve takes a day or two before protobuf definitions feel natural.

Does gRPC work with Next.js API routes?+

Yes, for server-to-server gRPC calls from Next.js API routes to other services. The @grpc/grpc-js npm package provides a gRPC client for Node.js that works in standard Next.js API routes. For browser clients calling gRPC services through Next.js, use Connect-RPC which transcodes standard HTTP/1.1 requests to gRPC, avoiding the browser compatibility issue.

Does SpeedMVPs use gRPC in AI product architectures?+

We use gRPC when the architecture specifically benefits from it: for products integrating with self-hosted model serving layers that expose gRPC inference APIs, or for high-throughput microservices communication where the performance difference is measurable. For most AI MVPs using external LLM APIs and Next.js, we use REST or tRPC as it is simpler, faster to build, and sufficient for the scale. Get a free consultation at speedmvps.co.uk

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