Throughput per Megabyte: A Pilot Benchmark of Language-Stack Efficiency for Self-Hosted HTTP Services on a Raspberry Pi 5

Cloud-centric web benchmarks miss constraints that matter for self-hosted services on ARM64 single-board computers, especially idle RAM footprint and energy per request. We ran a pilot benchmark on one Raspberry Pi 5, measuring equivalent SQLite-backed CRUD APIs implemented in Go 1.26/net/http, Rust 1.95/Axum, Python 3.13/FastAPI+Granian, Node.js 24/Fastify, and .NET 10 Native AOT across N = 50 randomized runs per stack and endpoint, with a separate concurrency sweep and memory time-series collection. Rust achieved the highest weighted throughput-to-RAM ratio (310.25req/s/MB, 2.8×Go) because its weighted peak RSS was 7.36MB, while .NET, Node.js, Go, and Rust formed a raw-throughput cluster with overlapping bootstrap CIs (2,125–2,461req/s) and Python remained below that cluster at 969req/s. The concurrency sweep on GET /items/:id showed divergent scaling: .NET reached 26,209req/s at c = 240, Go and Rust plateaued near c = 120, Node.js saturated near 9,700req/s, and Python stayed below 2,300req/s; memory snapshots showed Rust holding a flat 7.8MB RSS while Node.js grew by 117MB without visible GC drops. For RAM-constrained self-hosted CRUD-over-SQLite deployments on this Pi 5 unit, the data support Rust as the most resource-efficient measured stack, .NET as the strongest high-concurrency stack, and Go as a practical option where ecosystem breadth matters; replication is needed before extending these findings to other devices or storage backends.