Vegeta vs Locust
Feature checklists rarely decide anything. What decides it is the pricing metric, who has to write the tests, and which limitation you can live with.
Entry price
Free and open source
Open sourceFree and open source
Open sourceWhat drives the bill
The metric that actually scales your invoice.
Free and open source. You pay in engineering time and CI minutes.
Free and open source. You pay in engineering time and CI minutes.
Free tier
Everything, self-hosted
Everything, self-hosted
Open source
MIT
MIT
Self-hostable
No-code authoring
Languages
Platforms
Fits teams
Adoption
Established
OSS community · since 2013
Widely used
OSS community · since 2011
Strengths
- Constant-rate model avoids the coordinated-omission problem that distorts VU-based tools.
- Single static binary, trivial to run anywhere.
- Composable output for scripting and pipelines.
- Test scenarios are plain Python, so any logic is expressible without workarounds.
- Distributed mode is conceptually simple and works well on Kubernetes.
- Live web UI is useful during an exploratory ramp-up.
Limitations
- No scenarios or session state — one target list, one rate.
- No GUI or built-in reporting beyond plots.
- Go library usage required for anything sophisticated.
- Python's per-user overhead is higher than k6 or Gatling; big loads need more machines.
- Reporting is basic — plan on exporting to Grafana.
- HTTP-centric; other protocols require writing your own client.
Best for
Backend engineers who want rigorous constant-rate measurements of an endpoint.
Python teams who want load scenarios expressed in their own language.
All of these integrate with: Prometheus, Docker, GitHub Actions.
Pricing last reviewed August 2026 and is indicative only — confirm on each vendor’s own page.