Locust specialists for your dedicated team.

Where Locust fits in a long-term product team.

Because a Locust scenario is just Python, behaviour that is awkward elsewhere becomes ordinary. Users who branch based on a response, maintain session state across a long journey, or follow a distribution drawn from production data are all simply code, using whatever libraries are already available.

For teams already writing Python, this removes the adoption cost entirely: the test is written in their language, reviewed by the same people and run on the same tooling. That matters more for whether performance testing happens at all than any feature comparison.

What your dedicated team does with Locust.

Load testing gets skipped because it is a separate skill in a separate tool that someone has to find time for. Removing the language barrier removes a real part of that friction.

The rest is making it routine, a delivery-process question the testers on your team handle through outsource qa testing.

Common Locust use cases.

• Journeys with real branching Conditional user behaviour expressed directly, without fighting a configuration format.
• Reusing existing Python utilities Authentication and data generation shared with the application's own tooling.
• Scaling out across workers Distributed runs coordinated from one controller when a single machine is not enough.

Adding Locust skills to your team.

Locust is usually chosen when the team's existing language makes it the easiest option, so performance testing becomes part of normal work. Capacity for it is agreed as part of how the monthly fee is built.

You can add Locust capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.