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Limits & Quotas

xScaler enforces per-tenant limits to ensure a stable, consistent experience for all users.

Limits table​

LimitValue
Max active series per tenantPlan-dependent
Max labels per series30
Max label name length1,024 characters
Max label value length2,048 characters
Max metric name length1,024 characters
Max samples per remote_write request2,000
Query timeout2 minutes

:::info Plan-dependent limits Limits marked "Plan-dependent" vary by your subscription tier. Log in to the xScaler dashboard and navigate to Settings → Limits to see the exact values for your account. :::

Max samples per request​

The ingest endpoint accepts a maximum of 2,000 samples per request. This is reflected in the queue_config.max_samples_per_send setting in both Prometheus and Grafana Alloy configurations. The default value in those configs is already set to 2000 — do not raise it above this limit.

Query timeout​

All queries time out after 2 minutes. If your query hits this limit:

  1. Narrow the time range.
  2. Increase the step value to reduce the number of data points evaluated.
  3. Add label selectors to reduce the number of series scanned.
  4. Break long aggregations into recording rules (see Rules & Alerts).

Finding high-cardinality metrics​

Unexpectedly high active series counts are usually caused by labels with unbounded values (user IDs, request IDs, session tokens). Use this query to identify the top offenders:

curl "https://euw1-01.m.xscalerlabs.com/prometheus/api/v1/query" \
  -H "Authorization: Bearer <token>" \
  -H "X-Scope-OrgID: <tenant-id>" \
  --data-urlencode 'query=topk(10, count by (__name__)({__name__=~".+"}))'

If a metric has a disproportionately high series count, review which labels are attached to it and remove any that contain unbounded values.

Reducing ingest volume​

If you are approaching your series limit, consider:

  • Drop unused metrics at the source — use write_relabel_configs in Prometheus or Alloy to drop metrics before they leave the scrape host (see Prometheus remote_write).
  • Aggregate before shipping — use recording rules to pre-aggregate high-cardinality metrics and ship only the aggregated series.
  • Review label cardinality — replace high-cardinality label values (e.g. pod IDs) with coarser ones (e.g. deployment name).