cost-management
Cut your Grafana Cloud bill by attributing spend to teams and reducing telemetry volume. Covers FOCUS-compliant billing dashboards, cost-attribution labels in Alloy, Adaptive Metrics (cardinality reduction), Adaptive Logs (drop/sample), Adaptive Traces (tail sampling), usage alerts, and an optimizat
- 0
- Installs
- —
- Rating
- —
- Success rate
- 3
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 f361c00dd8a87e97… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
Grafana Cloud Cost Management
Docs: https://grafana.com/docs/grafana-cloud/cost-management-and-billing/
Reduce metric / log / trace spend with Adaptive signals + cost-attribution labels.
Prerequisites
- A Grafana Cloud stack with Adaptive Metrics / Logs / Traces enabled (visible under Cost Management)
- Alloy (or Grafana Agent) ingesting telemetry, with API key in scope
metrics:write+logs:write(+traces:write) - Admin access to the stack to apply Adaptive recommendations
Common Workflows
1. Attribute cost to a team / service
# 1. Add external labels in Alloy (metrics + logs configs)
prometheus.remote_write "cloud" {
endpoint { url = sys.env("PROMETHEUS_URL") /* ... */ }
external_labels = { team = "platform", project = "checkout-service" }
}
# 2. Reload Alloy
curl -X POST http://localhost:12345/-/reload
# 3. Verify labels arrived in Grafana Cloud
# In Explore, run: count by (team, project) ({__name__=~".+"})
# Then visit Cost Management → group by `team` / `project`
See references/adaptive-signals.md for the full Alloy snippet.
2. Cut metric cardinality with Adaptive Metrics
# 1. Pull recommendations
curl https://<stack>.grafana.net/api/plugins/grafana-adaptive-metrics-app/resources/v1/recommendations \
-H "Authorization: Bearer <token>" | jq '.recommendations | length'
# 2. In the UI: Grafana Cloud → Adaptive Metrics → review rules sorted by series-reduction impact
# 3. Test in "Preview" mode before applying
# 4. Apply (takes effect within 5 min)
# 5. Verify — series count should drop on the affected metrics
# Before applying, capture baseline:
# count({__name__="http_request_duration_seconds_bucket"})
# Wait 10 min after apply, run again — expect 10x+ reduction for high-card metrics.
# Rollback if needed: open the rule in the UI → Disable, or DELETE /v1/rules/<id>.
3. Drop noisy logs in Alloy
# 1. Add a filter stage (see references/adaptive-signals.md for the full block)
loki.process "filter_logs" {
forward_to = [loki.write.cloud.receiver]
stage.drop { expression = ".*GET /health.*" }
}
# 2. Reload Alloy
curl -X POST http://localhost:12345/-/reload
# 3. Verify the filter — health logs should NOT appear in Logs Drilldown
# LogQL check (should return 0):
# sum(rate({app="my-app"} |= "GET /health" [5m]))
# Bytes-ingested should also drop. Compare 24h before/after:
# sum(increase(loki_ingester_chunk_size_bytes_sum[24h])) by (namespace)
4. Set a usage alert before you hit quota
See references/alerts-and-queries.md for ready-to-paste rules (MetricsUsageHigh, LogsIngestionHigh).
Optimization checklist
- Apply Adaptive Metrics recommendations — typically reduces series 40-60%
- Drop health/readiness probe logs in Alloy
- Tail-sample traces to 5-10% + keep errors / slow spans
- Add
team+projectexternal labels to every Alloy config - Set usage alerts at 80% of quota
- Replace expensive ad-hoc queries with recording rules
References
references/adaptive-signals.md— Adaptive Metrics / Logs / Traces config; cost-attribution labelsreferences/alerts-and-queries.md— usage alert rules, cost-finding PromQL, billing-unit table
Resources
Files
3- SKILL.md
72d01cb29f4.5 KB - references/adaptive-signals.md
64f6f3eeed2.1 KB - references/alerts-and-queries.md
9bd100de5f1.3 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from grafana/skills8
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
Manage Grafana Cloud accounts — organizations, stacks, RBAC roles and assignments, SSO/SAML/OAuth/GitHub auth, service accounts for CI/CD, user invites, team membership, and API-driven provisioning. Creates stacks via the Cloud API, mints service-account tokens, applies role assignments, configures
Use when the user asks to "write a validator", "add validation", "implement admission control", "write a mutating webhook", "add a mutation handler", "validate incoming resources", "implement admission logic", "add admission webhooks", "write ingress validation", or asks how to validate or mutate re
Configure Grafana Alerting, Incident Response Management (IRM), and SLOs end-to-end — provisions Grafana-managed and data-source-managed alert rules, contact points (Slack/PagerDuty/email/webhook), notification policies with hierarchical matchers, silences, mute timings, on-call schedules and escala
Build a unified telemetry pipeline with Grafana Alloy — one OpenTelemetry-compatible binary that collects metrics, logs, traces, and profiles and ships to Grafana Cloud / Prometheus / Loki / Tempo / Pyroscope. Covers the Alloy config language (blocks, `sys.env`, component refs), `prometheus.scrape`
Get RED metrics + service maps + frontend RUM + AI/LLM monitoring out of Grafana Cloud — Application Observability (`traces_spanmetrics_*` from OTel traces, p50/p95/p99 latency, exemplar-to-trace, traces-to-logs / profiles), Frontend Observability with the Faro Web SDK (Core Web Vitals, session repl
Use when starting any grafana-app-sdk work — scaffolding a Grafana app, initializing a Grafana App Platform app, picking a deployment mode (standalone operator / grafana/apps / frontend-only), wiring app-specific config, or onboarding to the SDK. Covers `grafana-app-sdk` CLI install, `project init`
Connect AI coding agents (Claude Code, Cursor, VS Code, OpenAI Codex) to Grafana Cloud via the `mcp-grafana` Model Context Protocol server. Installs the server with `go install`, generates a Grafana service-account token, wires `~/.claude/settings.json` or `~/.cursor/mcp.json` with the `command` + `
Related knowledge skillsscan passed
PostHog logs for Java
Stop hook that blocks Claude from finishing until quality checks pass. Detects rationalization patterns (surface text heuristics), stale learning logs (filesystem mtime), and low disk space. Complements self-audit by mechanically enforcing learning capture habits. Use when Claude should be mechanica