Benchmarks

Caddy Snake embeds Python directly inside Caddy, eliminating the overhead of a separate upstream process where the design allows. The harness compares Flask (WSGI), FastAPI (ASGI), and ESGI (gevent) on a minimal JSON GET /hello endpoint. The table below matches the committed benchmarks/results.json, produced on Scaleway POP2-2C-8G (linux/amd64) via benchmarks/scaleway_bench.sh.

Test configurations

Configuration	Description
Flask + Gunicorn + Caddy	Gunicorn (1 worker, 4 threads) behind Caddy reverse proxy
Flask + Caddy Snake	Flask served directly by Caddy via caddy-snake (process worker)
FastAPI + Uvicorn + Caddy	Uvicorn (1 worker) behind Caddy reverse proxy
FastAPI + Caddy Snake	FastAPI served directly by Caddy via caddy-snake (process worker)
ESGI (gevent) + Caddy reverse proxy	The same caddysnake.py gevent ESGI StreamServer used by workers, listening on a Unix domain socket with Caddy proxying HTTP to that socket
ESGI + Caddy Snake	The ESGI app served via module_esgi / runtime gevent inside the plugin

All configurations return the same JSON body: {"message":"Hello, World!"}.

Results

Configuration	Requests/sec	Avg Latency (ms)	P99 Latency (ms)
Flask + Gunicorn + Caddy	1,759	56.68	72.67
Flask + Caddy Snake	2,905	34.40	53.24
FastAPI + Uvicorn + Caddy	3,382	29.54	233.59
FastAPI + Caddy Snake	4,854	20.57	38.98
ESGI (gevent) + Caddy reverse proxy	5,011	19.92	50.45
ESGI + Caddy Snake	5,146	19.42	45.04

On this hardware, Flask and FastAPI see a clear gain from embedding; the ESGI pair is closer because both rows already use the same gevent gateway code path (reverse proxy vs embedded Go hop).

Methodology

• Tool: hey
• Concurrency: 100 connections
• Duration: 10 seconds per test run
• Warmup: 200 requests at 10 concurrency before each configuration
• Repeats: 10 runs per configuration, averages reported
• Platform (published numbers): Scaleway POP2-2C-8G, linux/amd64
• Python: 3.13 (inside Docker Ubuntu 22.04 image from benchmarks/Dockerfile)
• Go: 1.26
• Workers: Caddy Snake uses one process worker in the benchmark Caddyfiles; Gunicorn uses 1 worker with 4 threads; Uvicorn uses 1 worker; the standalone ESGI line uses one caddysnake.py ESGI server process

Reproduce

Public numbers (Scaleway POP2-2C-8G)

See benchmarks/scaleway_bench.sh (Scaleway CLI + SSH). Example:

BENCH_RSYNC_LOCAL=1 BENCH_SSH_IDENTITY="$HOME/.ssh/id_ed25519_scw" ./benchmarks/scaleway_bench.sh

Any machine (Docker)

docker build -t caddy-snake-bench -f benchmarks/Dockerfile .
docker run --rm -v $(pwd)/benchmarks:/workspace/benchmarks caddy-snake-bench

Results go to benchmarks/results.json and benchmarks/benchmark_chart.{png,svg}. Copy the SVG into the docs site when publishing updated figures:

cp benchmarks/benchmark_chart.svg docs/static/img/benchmark_chart.svg

What the benchmark does

1. Builds Caddy with the caddy-snake plugin from source
2. Creates a Python 3.13 virtual environment with Flask, FastAPI, Gunicorn, Uvicorn, uvloop, and gevent (for ESGI)
3. For each configuration: start servers, wait for GET /hello, warm up, then benchmark with hey (100 concurrent, 10 s) for 10 runs; record requests/sec, average latency, and P99
4. Writes results.json and generates the comparison chart