Small Payload Latency: Why gRPC Beats REST in Microservices

When building microservices that handle frequent, small requests—think authentication checks, user profile lookups, or real-time notifications—the choice between gRPC and REST can make a substantial difference in your latency percentiles. Our empirical study provides concrete data to inform this decision.

For the StdSize payload scenario (approximately 0.76 KB, representing typical web API requests), we observed a clear and statistically significant ranking among the four implementations. The combination of Go with gRPC emerged as the leader, followed by Java with gRPC, then Java with REST, and finally Go with REST.

Looking at the median response times, (Go, gRPC) achieved 0.71ms with a remarkably low standard deviation of 0.06ms. (Java, gRPC) came in at 1.19ms with 0.17ms deviation. The REST implementations showed (Java, REST) at 3.21ms with 0.23ms deviation, and (Go, REST) at 3.23ms with 0.28ms deviation.

Why does gRPC excel in this scenario? Several technical factors contribute to its advantage. First, HTTP/2 multiplexing allows multiple requests to share a single TCP connection without head-of-line blocking. Second, header compression (HPACK) significantly reduces overhead on repeated requests. Third, Protocol Buffers provide a compact binary format that's both smaller and faster to parse than JSON. Fourth, the persistent connection model eliminates the overhead of repeated TCP handshakes.

The variability aspect deserves special attention. Our boxplot analysis showed that gRPC implementations not only had lower medians but also tighter interquartile ranges. This consistency is crucial for systems with strict SLA requirements—when you need to guarantee that 99% of requests complete within a certain time, lower variance matters as much as lower average latency.

Practical implications are clear: if you're building chatty microservices with short payloads and strict p99 latency SLAs, adopting gRPC can provide substantial benefits. Remarkably, our factorial analysis showed that you can achieve most of these gains simply by switching communication protocols—without the significant investment of rewriting your services in a different programming language.

The Wilcoxon Rank-Sum test confirmed that all pairwise differences were statistically significant (p-value < 2.2e-16), meaning these aren't just random fluctuations but genuine performance differences that will manifest consistently in production environments.