Google Cloud Monitoring API
Google Cloud's fully-managed metrics, alerting, and uptime monitoring service for ingesting custom time-series metrics, querying GCP infrastructure metrics, and configuring alert policies via a REST/gRPC API.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Separate IAM roles for metric writers vs readers enables least-privilege agent design. Metrics data does not contain raw log content — lower sensitivity than Cloud Logging. Alert notification channels (email, PagerDuty, Slack) store connection tokens that should be protected with appropriate IAM. HIPAA-eligible with BAA. FedRAMP High authorized.
⚡ Reliability
Best When
You are running GCP workloads and want automatic collection of infrastructure metrics alongside custom application metrics in a single pane — especially when GCP service metrics are already flowing in without any configuration.
Avoid When
You need multi-cloud observability, are standardized on Datadog or Prometheus, or need metric retention beyond 6 weeks without BigQuery export.
Use Cases
- • Agents writing custom application metrics (job throughput, queue depth, error rates) to Cloud Monitoring for unified observability alongside GCP infrastructure metrics
- • Automated alerting pipelines where agents create and manage alert policies programmatically — escalating when SLOs are breached or anomalies are detected
- • Agent-driven SLO management workflows querying service-level indicators and tracking error budget burn rate via the Monitoring API
- • Infrastructure health checks where agents create and monitor uptime checks against HTTP endpoints, TCP ports, or GCP services
- • Dashboard automation — agents querying time-series data to generate custom reports or feed metrics into downstream visualization tools
Not For
- • Log-based analysis — use Cloud Logging for querying log events; Cloud Monitoring is for numeric time-series metrics
- • Long-term metrics retention beyond 6 weeks for custom metrics — export to BigQuery for extended metric history
- • Multi-cloud metrics aggregation requiring a vendor-neutral format — consider Prometheus with remote_write or Datadog for cross-cloud observability
Interface
Authentication
Service accounts with ADC are recommended. IAM roles: roles/monitoring.metricWriter for write-only (least-privilege for agents that only push metrics), roles/monitoring.viewer for read-only queries, roles/monitoring.admin for full access including alert policy management. Workload Identity preferred in GKE.
Pricing
GCP built-in metrics (Compute, Cloud Run, GKE, etc.) are always free — cost only applies to custom metrics and API calls. High-cardinality custom metrics (many unique label value combinations) can drive up cost quickly.
Agent Metadata
Known Gotchas
- ⚠ Custom metric types must be created before data can be written — agents must call metricDescriptors.create first, or use the auto-creation behavior of the write API carefully
- ⚠ Time-series labels have strict cardinality constraints — each unique combination of label values creates a separate time series; high-cardinality labels (user IDs, request IDs) cause rapid cost escalation and quota exhaustion
- ⚠ Metric data cannot be backfilled more than 25 hours in the past — agents that miss a write window cannot retroactively fill gaps in metric history
- ⚠ The MQL (Monitoring Query Language) and the older filter-based query API have different capabilities — MQL is more powerful but not available in all API endpoints
- ⚠ Alert policies with notification channels require channels to be pre-created — agents building full alert automation must manage notification channel lifecycle separately from alert policies
Alternatives
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Scores are editorial opinions as of 2026-03-07.