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Can you stress-test distributed infrastructure and still see what is happening inside it?

Engineered for distributed load. Built for operational visibility.

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Can you generate distributed load and observe your infrastructure while it is happening?

Frequent releases. Distributed systems. Complex integrations. In today’s fast-paced environments, testing can’t slow you down – but it can’t fall short either.

Here’s how QALIPSIS helps DevOps teams deliver reliable, high-performing software at scale.

Can your load generator keep up with the infrastructure it is supposed to stress?

  • A single-machine load generator saturates its own network interface before it stresses the system under test. Running from one location also hides zone-level routing and latency differences that only appear when traffic arrives from multiple origins simultaneously — exactly the condition that causes degradation during a global traffic event.
  • Deploy factories across the zones you control, assign each a factory.zone, and trigger campaigns via the REST API with a per-scenario zone percentage split. The head orchestrates execution and collects results; factories inject load independently. Enable step-level monitoring on the steps that represent your critical service boundaries and export meters and events to your telemetry backend for live analysis during the run.
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What was your system doing at minute twelve of the load test?

  • Most load tools produce an internal report you read when the run is over. But the questions that matter operationally — at what point did the queue start lagging, which zone saw latency diverge first, did the connection pool saturate before or after the error rate climbed — require time-series data correlated with your infrastructure metrics, in the same tool your team is already watching while the test runs.
  • Enable monitoring per step — monitoring { events = true }, monitoring { meters = true }, or monitoring { all() } — only where signal is needed, keeping data volume intentional. Export to Graphite, Elasticsearch, Kafka, TimescaleDB/PostgreSQL, or InfluxDB. Add factory.tags to carry environment, team, or project metadata through to every exported data point, so test runs are distinguishable in shared dashboards alongside production signals.
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How often does your infrastructure load test actually run?

  • Infrastructure load tests that require manual setup and manual interpretation are not tests — they are experiments. They run infrequently, produce results no one has time to compare to the previous run, and get skipped entirely under release pressure. The value comes from running them repeatedly and automatically against a consistent baseline, exactly the way other pipeline stages work.
  • Trigger campaigns programmatically via the REST API (POST /campaigns) with parameterised payloads — minion counts, zone distributions, execution profiles — and retrieve results via GET /campaigns/{campaign-key} to wire them into your pipeline logic. Schedule recurring campaigns (POST /campaigns/schedule) with time zone-aware recurrence for nightly or pre-release baseline runs. For Gradle-based pipelines, the Gradle plugin provides qalipsisRunAllScenarios or a custom RunQalipsis task with JUnit report export alongside.
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QALIPSIS: Elevating software quality for DevOps teams

With QALIPSIS, your team can:

Detect bottlenecks

Database compatibility

Automate validation in your CI/CD

Release with confidence every

What you need to know

Boost software quality. Streamline DevOps workflows.

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