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Adaptive Metrics

Grafana LabsOfficialPublished by Grafana Labs

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
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From the author

Cut Grafana Cloud Metrics cost by shrinking active-series count with Adaptive Metrics aggregation rules — auto-recommendations from query history, custom exact/regex rules, label-drop config, unused-metric detection, and Alloy remote_write fallback. Use when investigating a high Mimir/Grafana Cloud bill, hunting high-cardinality labels (`pod_uid`, `service_instance_id`, `version`), pre-aggregating counters/gauges, dropping unused metrics, or measuring `grafanacloud_instance_active_series` before/after — even when the user says "reduce cardinality", "too many series", "metrics spend", "active series count is exploding", or "drop the version label" without naming Adaptive Metrics.
— the description by Grafana Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Adaptive Metrics provides a framework for controlling metrics billing in Grafana Cloud by reducing the active series count. It enables users to identify high-cardinality labels and unused metrics, then apply aggregation rules or drop configurations to shrink the data footprint before storage. The process involves reviewing automated recommendations, creating custom exact or regex-based aggregation rules, and verifying the impact on series counts.

What it does

  • Generates recommendations for reducing active series based on query history.
  • Allows creation of custom aggregation rules using exact or regex matching.
  • Identifies unused metrics that can be safely dropped to save costs.
  • Provides methods to drop specific labels that contribute to high cardinality.
  • Supports verification of series reduction impact through query metrics.
  • Offers a fallback configuration for dropping metrics at the remote write stage.

When to use it

  • When investigating high costs in a Grafana Cloud metrics bill.
  • When dealing with exploding active series counts due to high-cardinality labels.
  • When you need to pre-aggregate counters or gauges to optimize storage.
  • When you want to remove unused metrics that are still being sent to the cloud.
  • When you need to verify the effectiveness of cardinality reduction strategies.

Advantages

  • Directly impacts billing by reducing the number of active series.
  • Automated recommendations simplify the identification of optimization targets.
  • Flexible rule creation supports both specific and broad metric patterns.
  • Provides clear verification steps to measure the success of applied rules.

Usage examples

“How can I see which metrics are recommended for aggregation to lower my bill?”
The agent retrieves the recommendation list from the Adaptive Metrics API, showing metric names, current series counts, and estimated reduction percentages.
“I need to drop the version label from my CPU metrics to reduce cardinality.”
The agent guides you through creating a custom aggregation rule that targets the specific metric and drops the requested labels using a sum aggregation type.
“How do I find and remove unused metrics in my Grafana Cloud instance?”
The agent lists unused metrics via the usage analysis endpoint and provides instructions for verifying their lack of use in dashboards or alerts before dropping them.
“How can I check if my aggregation rules are actually working?”
The agent provides PromQL queries to monitor the total active series and the specific count of series dropped by your active aggregation rules.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/grafana/adaptive-metrics/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip adaptive-metrics.zip -d .agents/skills

Documentation by Grafana Labs

Grafana Cloud Adaptive Metrics

Docs: https://grafana.com/docs/grafana-cloud/cost-management-and-billing/reduce-costs/metrics-costs/adaptive-metrics.md

Aggregation rules that pre-shrink high-cardinality metrics before storage — directly reduces active-series billing.

Prerequisites

  • Grafana Cloud Metrics plan (any paid tier)

  • API key with metrics:write (for the Adaptive Metrics API — adaptive-metrics.grafana.net, Bearer auth)

  • For the verification queries: the metrics query endpoint (prometheus-prod-XX.grafana.net) uses HTTP basic auth — <metrics_user> (numeric stack/instance ID) plus a token with metrics:read — not the Bearer key

  • Access to Home → Adaptive Metrics in the Cloud portal

Common Workflows

1. Review + apply auto-recommendations
# 1. Pull the recommendation list (sorted by series-reduction impact)
curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/recommendations" \
  | jq '.recommendations[] | {metric_name, current_series, projected_series, estimated_reduction_percent}'

# 2. Capture the baseline series count for the target metric
#    (metrics query endpoint = basic auth, not the Bearer key)
curl -s -u "<metrics_user>:<metrics_token>" \
  "https://prometheus-prod-XX.grafana.net/api/prom/api/v1/query?query=count({__name__=\"process_cpu_seconds_total\"})" \
  | jq '.data.result[0].value[1]'   # → e.g. "12480"

# 3. Apply the recommendation (or click Apply in the UI)
curl -s -X POST -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/recommendations/<ID>/apply"

# 4. Wait ~5 min. Verify — re-run the count query; expect a large drop.
#    Also check the saving metric:
#      grafanacloud_instance_active_series_dropped_by_aggregation_rules

Rollback — delete the rule:

curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/rules" | jq '.rules[] | {id, metric_name}'
curl -s -X DELETE -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/rules/<RULE_ID>"
# Or in the UI: Rules → row → Disable
2. Hand-write a custom rule
# 1. Sanity-check the metric is not used WITH that label in dashboards/alerts
grep -r 'process_cpu_seconds_total' dashboards/ alerts/ | grep -E 'version|go_version'
# Expect no hits → safe to drop.

# 2. Create the rule
curl -s -X POST -H "Authorization: Bearer <KEY>" -H "Content-Type: application/json" \
  "https://adaptive-metrics.grafana.net/api/v1/rules" \
  -d '{"rules":[{"metric_name":"process_cpu_seconds_total","match_type":"MATCH_TYPE_EXACT",
                 "drop_labels":["version","go_version"],
                 "aggregations":[{"type":"AGGREGATION_TYPE_SUM"}]}]}'

# 3. Verify — same count() query as above; series count should drop within 5 min.

Full payloads (regex match, aggregation types, all caveats): references/api.md.

3. Drop unused metrics entirely
# 1. List unused metrics
curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/usage-analysis?filter=unused" | \
  jq '.metrics[] | {metric_name, series_count, last_queried}'

# 2. Confirm not referenced in dashboards / alerts / recording rules
grep -r '<METRIC_NAME>' dashboards/ alerts/ recording-rules/

# 3. Add a write_relabel_config drop in Alloy (full block in references/api.md)
#    Reload Alloy: curl -X POST http://localhost:12345/-/reload

# 4. Verify — the metric should no longer appear in series counts after ~10 min
curl -s -u "<metrics_user>:<metrics_token>" \
  'https://prometheus-prod-XX.grafana.net/api/prom/api/v1/label/__name__/values' | jq '.data | index("<METRIC_NAME>")'  # → null

Measure the impact

# Total active series (billed unit)
grafanacloud_instance_active_series

# Series specifically dropped by Adaptive Metrics rules
grafanacloud_instance_active_series_dropped_by_aggregation_rules

Rules take effect within ~5 minutes; full billing impact appears within an hour. The original high-cardinality samples keep flowing but the dropped labels no longer count toward billing.

Resources

Files

references/api.md

Adaptive Metrics API + rule formats

Base URL: https://adaptive-metrics.grafana.net/api/v1 Auth: Authorization: Bearer <grafana-cloud-API-key> (scope metrics:write)

List recommendations

curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/recommendations" | \
  jq '.recommendations[] | {metric_name, current_series, projected_series, estimated_reduction_percent}'

Apply / disable a recommendation

# Apply by ID
curl -s -X POST -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/recommendations/<ID>/apply"

# Roll back — list rules, then DELETE the offending rule
curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/rules" | jq
curl -s -X DELETE -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/rules/<RULE_ID>"

Create a custom rule (exact match)

curl -s -X POST -H "Authorization: Bearer <KEY>" -H "Content-Type: application/json" \
  "https://adaptive-metrics.grafana.net/api/v1/rules" \
  -d '{
    "rules": [{
      "metric_name": "process_cpu_seconds_total",
      "match_type":  "MATCH_TYPE_EXACT",
      "drop_labels": ["version", "go_version"],
      "aggregations":[{"type":"AGGREGATION_TYPE_SUM"}]
    }]
  }'

Create a regex-match rule (family)

curl -s -X POST -H "Authorization: Bearer <KEY>" -H "Content-Type: application/json" \
  "https://adaptive-metrics.grafana.net/api/v1/rules" \
  -d '{
    "rules": [{
      "metric_name": "go_.*",
      "match_type":  "MATCH_TYPE_REGEX",
      "drop_labels": ["go_version", "version", "service_instance_id"],
      "aggregations":[{"type":"AGGREGATION_TYPE_SUM"}]
    }]
  }'

Aggregation types

TypeUse case
sumCounters, request counts, byte totals
maxGauges where you want the worst-case
minGauges where you want the best-case
avgRate metrics / averages

Counters MUST use sum — averaging counters produces incorrect rates.

Unused metrics — usage analysis

curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-metrics.grafana.net/api/v1/usage-analysis?filter=unused" | \
  jq '.metrics[] | {metric_name, series_count, last_queried}'

Before dropping unused metrics entirely:

  1. Confirm not used in any dashboard (search dashboard JSON by metric name).

  2. Confirm not used in any alert or recording rule.

  3. Check with the owning team — could be an SLO input.

Alloy fallback — drop at remote_write

prometheus.remote_write "grafana_cloud" {
  endpoint {
    url = "https://prometheus-prod-XX.grafana.net/api/prom/push"
    write_relabel_config {
      source_labels = ["__name__"]
      regex         = "unused_metric_name|another_unused_metric"
      action        = "drop"
    }
  }
}

Common labels safe to drop globally

  • version, app_version, go_version — rarely queried in PromQL

  • service_instance_id, pod_uid, container_id — ultra-high cardinality

  • git_commit, build_date — static, inflate series for no query value

Adaptive Logs (companion)

curl -s -H "Authorization: Bearer <KEY>" \
  "https://adaptive-logs.grafana.net/api/v1/recommendations" | \
  jq '.recommendations[] | {stream_selector, estimated_reduction_percent}'

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access, Needs an API key

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Grafana Labs
Publisher
Grafana Labs (Official)
Source
grafana/skills · skills/grafana-cloud/adaptive-metrics · commit 8fb72a5
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download adaptive-metrics.zip