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Comparison and benchmarks

pypiron sustains 8,288 installs/s on 2 vCPU — more than 100× the next server in the benchmark.

Max sustained install throughput Max sustained install throughput

Rank Server Config Installs/s
1 pypiron S3 + presigned redirect (Rust) 8,288
2 devpi devpi + nginx 78
3 bandersnatch full static mirror via nginx 77
4 pypiserver gunicorn + cached-dir 69
5 pypicloud (archived) S3 + DynamoDB (uwsgi) 42
6 proxpi flask caching proxy 32

The others stream every wheel through the server. pypiron hands wheel downloads to object storage, leaving the node to serve index and metadata requests.

In a separate index-only benchmark, one 8-vCPU machine served 202,069 requests/s with a p99 of 2.62 ms.

Each server ran its own documented production topology on the same 2-vCPU AWS box, serving the same frozen set of real wheels under identical client load — the rigs and raw results are published in the repo. Beyond speed: how pypiron is tested.

Choose by job

  • Private packages and a PyPI cache behind one URL — pypiron. One binary, no database, local disk or S3/GCS/Azure.
  • A staging → release pipeline — push to a test index, run the suite, promote — devpi. That workflow is devpi's core, and pypiron does not do it: pypiron vs devpi.
  • A dead-simple private index on one box, no cache — pypiserver: a directory of packages, maintained for over a decade. pypiron vs pypiserver.
  • A caching proxy and nothing else — proxpi, a tiny Flask proxy. pypiron's proxy does the same and hosts private packages too.
  • A byte-complete mirror of all of PyPI — bandersnatch, purpose-built for that job. pypiron mirrors a filtered subset.
  • One governed platform for every artifact type in the org — Artifactory or Nexus: pypiron vs Artifactory.
  • Fully managed, no servers at all — AWS CodeArtifact, if you are on AWS and would rather pay per request than run anything yourself.

Still on pypicloud? It was archived in August 2023. Follow the migration guide.

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