Grafana Labs has published a practical approach for monitoring Cypress test suites by converting test results into Prometheus metrics and sending them to Grafana Cloud. The approach allows teams to track test failures, execution times, and flaky tests across multiple runs instead of relying solely on terminal output or CI logs from individual executions.
The approach uses Cypress lifecycle hooks to capture test results, a Prometheus Pushgateway to temporarily hold metrics from the short-lived test jobs, and Grafana Alloy to scrape those metrics and forward them to Grafana Cloud. The resulting data can then be visualised and used for alerting, providing a longer-term view of test-suite behaviour.
Cypress already exposes useful information through its plugin lifecycle. The before:run hook can establish a common identifier for an entire test-suite execution, while after:spec provides the results from each specification, including pass and failure counts, test states, and durations. Grafana's example converts this information into a small set of Prometheus metrics covering individual tests, specifications, and complete runs.
This allows teams to answer questions that are difficult to answer from a single CI execution. A dashboard can show whether a particular specification is becoming slower, whether a test has started failing intermittently, or whether overall suite performance is deteriorating. GitHub Actions run identifiers can also be attached to the metrics, allowing a metric change to be traced back to the CI execution that produced it.
The Pushgateway is important because Cypress executions are short-lived jobs. A conventional Prometheus scrape may never reach a test process before it exits, so the results are pushed to an intermediary that can subsequently be scraped by Alloy. Grafana recommends treating this telemetry as a side effect of the test rather than part of the test outcome: failure to publish metrics should not cause an otherwise successful test run to fail.
The approach reflects a broader shift in how engineering teams treat quality data. Test results are often stored in CI systems or test-management platforms primarily for reporting, while production telemetry is treated as operational data. Exporting test execution metrics into the same observability environment creates an opportunity to examine software quality alongside application and infrastructure behaviour.
This can be particularly useful for identifying trends rather than individual failures. A test that occasionally fails may look like an isolated problem in CI. Still, persistent metrics can reveal that its failure rate is increasing, that execution time is gradually worsening, or that failures correlate with particular specifications or deployment periods.
The approach is also deliberately based on existing open-source components rather than requiring a proprietary Cypress monitoring mechanism: Cypress → Prometheus metrics → Pushgateway → Grafana Alloy → Grafana Cloud. Grafana's wider observability platform supports Prometheus-compatible metrics alongside logs, traces, and other OpenTelemetry-based telemetry.
This does not replace dedicated test-management or CI reporting tools. Platforms such as Cypress Cloud, Allure, Xray, and CI systems such as GitHub Actions provide richer test-specific views, historical execution information, or integration with broader development workflows. The Grafana approach is different: it treats test results as time-series operational data, making them available alongside the telemetry engineers already use to understand system behaviour.
That distinction could help teams move beyond simple pass/fail reporting. Instead of asking only whether the latest build passed, engineering teams can begin tracking indicators such as test execution duration, failure rates, flaky-test frequency, and suite-level performance as part of their broader engineering health metrics.
The larger implication is that automated testing increasingly produces useful telemetry in its own right. As test suites become larger and more distributed, retaining and correlating that information over time could help teams identify degradation earlier and make test reliability and performance observable characteristics of the engineering system, rather than metrics that disappear when a CI job finishes