Locust vs NeoLoad
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 sourceQuote on request
Quote onlyWhat drives the bill
The metric that actually scales your invoice.
Free and open source. You pay in engineering time and CI minutes.
Enterprise licensing by peak virtual users, negotiated annually.
Free tier
Everything, self-hosted
Free edition limited to a small number of virtual users.
Open source
MIT
Self-hostable
No-code authoring
Languages
Platforms
Fits teams
Adoption
Widely used
OSS community · since 2011
Established
Tricentis · since 2005
Strengths
- 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.
- Handles enterprise protocols such as SAP GUI and Citrix that open-source tools cannot.
- APM correlation makes root-cause analysis much faster than latency graphs alone.
- Both GUI design and as-code authoring, so mixed-skill teams can share a suite.
Limitations
- 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.
- Expensive, with a procurement-scale sales process.
- Overkill for a straightforward web or API workload.
- Proprietary scripts do not port to other tools.
Best for
Python teams who want load scenarios expressed in their own language.
Large enterprises performance-testing SAP, Citrix or Oracle estates.
Pricing last reviewed August 2026 and is indicative only — confirm on each vendor’s own page.