Scaling gRPC in Production: Load Balancing, Backpressure and Queueing — Part 1

Blog about gPRC experiments to scale in production

Introduction

gRPC is widely used for high-performance service-to-service communication, but production behavior depends on more than Protocol Buffers and HTTP/2. This Part 1 experimentally explores how load balancing, concurrency, capacity limits, resilience, and asynchronous queueing affect a gRPC architecture, progressing from Envoy and Kubernetes to client-side round-robin and RabbitMQ. The experiments provide a practical view of how gRPC systems behave as workloads increase and exceed available processing capacity. Part 2 will extend this into AI and agent-to-agent communication, exploring where gRPC can be effective for distributed agents and comparing it with communication mechanisms such as WebSocket, SSE, and other streaming approaches.

Q1) Can multiple gRPC requests share the same HTTP/2 connection? What benefit does Envoy provide in a gRPC architecture? Can a load balancer distribute those multiplexed gRPC requests across multiple backend servers? Will all three gRPC servers actually be utilized? How does throughput change as concurrency increases? What happens when concurrency exceeds the available worker capacity? Does HTTP/2 multiplexing itself guarantee load balancing across backend servers?