Prometheus v3.11.0, released on 2026-04-02, is a minor update focusing on enhanced service discovery, advanced PromQL capabilities, and experimental TSDB improvements. This release offers valuable updates for DevOps and QA teams tracking monitoring tool evolution. For the official source, refer to the Prometheus v3.11.0 release notes.

Key Changes:

  • Service Discovery Enhancements: AWS Service Discovery now supports Elasticache and RDS roles. Azure SD gains support for Azure Workload Identity authentication. Kubernetes SD introduces node role selectors for pod roles and new pod-based labels (__meta_kubernetes_pod_deployment_name, __meta_kubernetes_pod_cronjob_name, __meta_kubernetes_pod_job_name) for better target identification. A new prometheus_sd_last_update_timestamp_seconds metric helps track SD update frequency.
  • PromQL Updates: New </ and >/ operators are added for trimming observations from native histograms. An experimental histogram_quantiles variadic function allows computing multiple quantiles simultaneously, offering more detailed performance analysis.
  • TSDB Innovations: Several experimental features are introduced, including storage.tsdb.retention.percentage to configure disk usage limits, fast-startup for quicker restarts, st-storage for ingesting start timestamps, and xor2-encoding for optimized float sample chunk encoding.
  • Deprecations & Changes: Hetzner SD users should update deprecated labels like __meta_hetzner_datacenter and __meta_hetzner_hcloud_datacenter_location to their new counterparts. Promtool’s debug output now redirects to stderr, preventing interference with stdout.
  • Performance & Bug Fixes: Performance improvements target PromQL joins, native histogram aggregations, remote write WAL watching, and TSDB label value intersections. Numerous bug fixes address issues in AWS SD, Agent memory leaks, alerting state, Kubernetes SD target duplication, and OTLP error handling.

Impact for QA Teams: QA engineers benefit from improved visibility into test environments through expanded cloud service discovery. The new PromQL features enable more precise analysis of performance metrics, especially with native histograms, aiding in identifying regressions or performance bottlenecks. A more stable and performant Prometheus instance, thanks to various bug fixes and optimizations, ensures reliable monitoring data for testing efforts. For more on integrating Prometheus, see our article on Grafana Prometheus Monitoring.