Your on-call phone goes off at 2 a.m. A workflow in production stalled, but the metrics dashboard shows nothing. Is it a latency spike, a stuck worker, or bad luck? That’s the exact kind of pain Prometheus and Temporal can solve together—if you wire them up right.
Prometheus is the observability backbone for modern infrastructure, built to scrape metrics and make problems visible before users notice them. Temporal, on the other hand, is a workflow orchestration engine that brings state, retries, and determinism to distributed apps. Put them together and you get something rare in distributed systems: confidence.
When you integrate Prometheus with Temporal, every workflow execution, queue latency, and activity error becomes a time-stamped metric you can query, alert on, or trend. Temporal’s metrics endpoint exposes workflow and task data in a Prometheus-friendly way. Prometheus pulls those metrics at intervals and stores them for analysis. Grafana or any dashboarding tool can then visualize it, giving you fast feedback on how workflows behave under load.
To wire it up, you point Prometheus to Temporal’s metrics endpoint, often running on port 9090 or similar, and configure the scrape job. Set job_name to “temporal” so you can cut through noise quickly. Each namespace in Temporal produces its own metric set. That means you can slice operational performance per team or service without touching the codebase.
If you see drops in worker throughput, check the temporal_activity_task_schedule_to_start_latency metric. High retry counts? The temporal_workflow_task_retries_total counter will tell you which workflow type is the problem child. Build alerts with thresholds rather than single events, because retries are part of Temporal’s normal control loop.