Metrics Monitoring Platform
Collect, store and query time-series metrics from every service.
AdvancedPlatform
Services emit metrics to a central collector, which writes them into a metrics store. Dashboards and alerting query that store for operational visibility, while retention and downsampling policies control cost.
When to use it
- The fleet is large enough that a single service's logs are no longer sufficient to understand system health
- You need alerting on thresholds and trends, not just manual inspection
Trade-offs
- High-cardinality labels and long retention can make storage very expensive very quickly
- Traffic spikes during incidents can overwhelm the collection layer if it is not designed for it
Components used
Managed App ServiceMetrics StoreDashboardsAlerting & On-call
How it works
- Agents collect metrics from every service and ship them to a time-series database optimised for many small appends and range queries.
- Data is downsampled as it ages — full resolution recently, coarser rollups for older windows — because nobody queries per-second data from six months ago.
- An alerting engine evaluates rules continuously against the same store that powers dashboards.
Used in the wild
- Infrastructure and application monitoring.
- SLO tracking and error-budget burn alerts.
- Capacity planning from historical trends.
Good to know
- Cardinality is the thing that kills these systems. Adding a label like user id or request id multiplies the number of stored series into the millions and will exhaust memory long before request volume does.
- Pull-based collection (the server scrapes targets) versus push-based (agents send) is a long-running argument; pull makes target health obvious, push handles short-lived jobs and network boundaries better.
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