A common operational surprise in containerized environments is the sudden inflation of Sysdig Monitor costs, primarily driven by unanticipated data ingestion volume. Having analyzed several team deployments, I've found the issue rarely stems from core application metrics, but from auxiliary data sources and overly permissive collection policies.
The primary cost levers are:
* **Metric Cardinality:** Each unique combination of metric name and key-value label pair counts as a distinct time series. A single metric with dynamic labels (like `container_id`, `request_id`) can spawn thousands of series.
* **Sampling Rate:** The default 30-second interval may be unnecessarily frequent for many business-level metrics.
* **Scope of Collection:** By default, agents (like the Sysdig agent or Falco sidecar) may collect all exposed Prometheus metrics from every pod, including those from third-party helm charts that emit high-cardinality internal data.
Begin by identifying the source of the ingestion. Use Sysdig's **Metrics** explorer with a breakdown query. Focus on the `sysdig_agent_metrics_samples_total` metric, segmented by `kubernetes.namespace.name` or `kubernetes.deployment.name`.
```promql
avg(avg_over_time(sysdig_agent_metrics_samples_total[5m]))
```
This will highlight which namespaces or deployments are contributing the most samples. The next step is to implement granular filtering in the agent configuration. For the Sysdig agent, you can refine collection in the `dragent.yaml`:
```yaml
prometheus:
enabled: true
log_errors: true
max_metrics: 2000
max_metrics_per_process: 400
metrics_for_patterns: []
patterns:
- include: '{__name__=~"container_.*"}'
- exclude: '{__name__=~"container_tasks_.*"}'
```
The key is moving from a broad include to explicit `patterns` that `include` only what you need and `exclude` problematic metrics. For Kubernetes, consider using the `CustomMetrics` resource to target specific pods and services, rather than cluster-wide scraping.
Finally, review the sampling frequency. For stable infrastructure metrics, increasing the interval to 60s or 120s can reduce volume significantly without impacting observability. Implement these changes in a staged manner, monitoring the `sysdig_agent_metrics_samples_total` trend in your dashboard to validate the reduction.
--crusader
Commit early, deploy often, but always rollback-ready.