# Setting Up Google Cloud Monitoring and Observability: A Complete Guide > Set up Google Cloud monitoring and observability using Cloud Logging, Cloud Monitoring, Managed Prometheus, and Distributed Tracing with SKILL modules. Deploy a production-ready stack. - Repository: [Google/skills](https://github.com/google/skills) - Tags: how-to-guide - Published: 2026-08-13 --- **You can deploy a production-grade observability stack on Google Cloud by enabling Cloud Logging, Cloud Monitoring, Managed Prometheus, and Distributed Tracing through the reusable SKILL modules in the `google/skills` repository.** Setting up Google Cloud monitoring and observability involves configuring a unified pipeline that spans log ingestion, metrics collection, and distributed tracing. The `google/skills` repository codifies these configurations into modular SKILL files—located in paths like [`skills/cloud/gke-observability/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/gke-observability/SKILL.md)—that provide copy-paste-ready commands and Terraform resources for GKE clusters, VPC networking, and SLO-based alerting. ## Core Architecture and Components Google Cloud’s observability stack consists of six integrated layers that feed into a single pipeline controllable via variables like `enable_monitoring`. **Cloud Logging** (`logging.googleapis.com`) serves as the centralized ingestion point for system component logs, workload logs, and audit logs, offering a queryable **Logging Query Language (LQL)** interface. **Cloud Monitoring** (`monitoring.googleapis.com`) collects system-component, control-plane, and custom metrics, exposing them to Metrics Explorer, Grafana, and **Managed Prometheus** for PromQL queries without self-managed infrastructure. **Cloud Trace and Cloud Profiler** provide OpenTelemetry-enabled agents for distributed tracing and continuous CPU/memory profiling. For network visibility, **VPC Flow Logs**, Firewall Logs, and Cloud NAT Logs capture packet-level telemetry via BigQuery and Cloud Monitoring MCPs. Finally, **SLO Alerting** uses PromQL-driven alert policies in Cloud Monitoring to automate burn-rate-aware breach detection, as configured in [`skills/cloud/google-cloud-slo-alert-configuration/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-slo-alert-configuration/SKILL.md). ## Step-by-Step Implementation ### Enable Required APIs Before configuring specific services, activate the foundational APIs using `gcloud`: ```bash gcloud services enable \ logging.googleapis.com \ monitoring.googleapis.com \ container.googleapis.com \ file.googleapis.com \ --quiet ``` ### Configure GKE Cluster Monitoring Adopt the "golden-path" defaults to expose the full suite of metrics, logs, and tracing. Update your GKE cluster with comprehensive monitoring components and enable Managed Prometheus: ```bash # Enable full suite of monitoring components gcloud container clusters update --region \ --monitoring=SYSTEM,API_SERVER,SCHEDULER,CONTROLLER_MANAGER,STORAGE,POD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,CADVISOR,KUBELET,DCGM \ --quiet # Enable Managed Prometheus gcloud container clusters update --region \ --enable-managed-prometheus \ --quiet # Enable Dataplane V2 flow metrics gcloud container clusters update --region \ --enable-dataplane-v2-flow-observability \ --quiet ``` Refer to [`skills/cloud/gke-observability/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/gke-observability/SKILL.md) for the complete command reference and golden-path defaults. ### Query Logs with Logging Query Language Once Cloud Logging is active, use LQL to extract specific events. To fetch OOM-Killed events from Kubernetes: ```text resource.type="k8s_event" AND jsonPayload.reason="OOMKilling" ``` Execute queries via `gcloud logging read` to filter by namespace: ```bash gcloud logging read 'resource.type="k8s_container" AND resource.labels.namespace_name=""' \ --project \ --limit 100 \ --quiet ``` ### Optimize Costs with System-Only Monitoring For non-production environments, reduce costs by limiting metric collection to system-only telemetry: ```bash gcloud container clusters update --region \ --monitoring=SYSTEM \ --quiet ``` Cost considerations and toggle strategies are documented in the GKE Observability SKILL. ### Implement SLO-Based Alerting with Terraform Create burn-rate-aware alerts using PromQL queries. The following Terraform snippet, consistent with [`skills/cloud/google-cloud-slo-alert-configuration/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-slo-alert-configuration/SKILL.md), creates an API-server latency alert: ```hcl resource "google_monitoring_alert_policy" "api_latency" { display_name = "API Server Latency P99 > 5s" combiner = "OR" conditions { display_name = "apiserver_request_duration_seconds P99" condition_prometheus_query_language { duration = "60s" evaluation_missing_data = "DEFAULT" query = "apiserver_request_duration_seconds{quantile=\"0.99\"} > 5" } } alert_strategy { auto_close = "86400s" } # Notification channels omitted; configure as needed } ``` ### Enable Distributed Tracing Integrate OpenTelemetry exporters into your application code to send traces to Cloud Trace. For Go applications: ```go import ( "go.opentelemetry.io/otel" "go.opentelemetry.io/otel/exporters/trace/googlecloud" ) func initTracer() { exporter, _ := googlecloud.NewExporter() tp := otel.NewTracerProvider(otel.WithSyncer(exporter)) otel.SetTracerProvider(tp) } ``` Deployment patterns for other languages are available in the GKE Observability SKILL under the distributed tracing section. ## Practical Implementation Examples **Full Monitoring Enablement**: Use the `gcloud container clusters update` commands detailed in the implementation steps above to activate the complete metrics suite. **VPC Flow Logs Cost Estimation**: Estimate log ingestion costs programmatically via the Monitoring API endpoint referenced in [`skills/cloud/google-cloud-networking-observability/references/vpc-flow-logs-cost-estimation.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-networking-observability/references/vpc-flow-logs-cost-estimation.md): ```bash curl "https://monitoring.googleapis.com/v3/projects/${PROJECT_ID}/timeSeries?filter=metric.type%3D%22networking.googleapis.com/vpc_flow/predicted_max_vpc_flow_logs_count%22&interval.startTime=${START}&interval.endTime=${END}" ``` **Managed Grafana Dashboard Deployment**: Deploy dashboards using JSON configurations: ```bash gcloud monitoring dashboards create \ --project \ --config-from-file=grafana-dashboard.json ``` ## Key Source Files and References - **[`skills/cloud/gke-observability/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/gke-observability/SKILL.md)**: Blueprint for logging, monitoring, Prometheus, and tracing configuration. - **[`skills/cloud/google-cloud-networking-observability/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-networking-observability/SKILL.md)**: Guides VPC Flow Logs, firewall logs, NAT logs, and network cost estimation. - **[`skills/cloud/google-cloud-slo-alert-configuration/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-slo-alert-configuration/SKILL.md)**: Wizard for PromQL-based SLO alert policies. - **[`skills/cloud/google-cloud-recipe-foundation-builder/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-recipe-foundation-builder/SKILL.md)**: Sets up centralized logging and monitoring scopes across organizations. - **[`skills/cloud/google-cloud-slo-alert-configuration/references/service_metrics.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-slo-alert-configuration/references/service_metrics.md)**: Canonical list of GKE metrics for PromQL alerts. ## Summary Setting up Google Cloud monitoring and observability using the `google/skills` repository provides: - **One-command activation** of production-grade monitoring via `gcloud` cluster updates. - **Native PromQL and LQL support** for querying metrics and logs with documented examples. - **Cost-control mechanisms** to toggle between full and system-only metric collection. - **Terraform-based SLO alerting** aligned with Google SRE best practices. - **Integrated distributed tracing** for microservice performance debugging. These modules deliver a repeatable, secure, and documented foundation for observing GKE workloads and Google Cloud networking environments. ## Frequently Asked Questions ### What APIs must be enabled before setting up Google Cloud monitoring and observability? You must enable `logging.googleapis.com`, `monitoring.googleapis.com`, and `container.googleapis.com` using `gcloud services enable`. The `google/skills` repository typically requires `file.googleapis.com` as well for comprehensive GKE observability. ### How do I reduce monitoring costs for non-production GKE clusters? Limit metric collection to system-only telemetry by updating your cluster with `--monitoring=SYSTEM` instead of the full component list. This eliminates workload-specific metrics while preserving node health visibility, significantly reducing ingestion costs. ### What is the difference between Cloud Monitoring and Managed Prometheus? **Cloud Monitoring** is Google Cloud’s native metrics service that collects system and custom metrics through the Cloud Monitoring API. **Managed Prometheus** is a Google-hosted Prometheus-compatible service that accepts native PromQL queries and stores time-series data without requiring you to operate Prometheus servers, as configured via `--enable-managed-prometheus`. ### Where can I find the canonical PromQL queries for SLO alerts? Reference [`skills/cloud/google-cloud-slo-alert-configuration/references/service_metrics.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-slo-alert-configuration/references/service_metrics.md) within the repository. This file lists the standard GKE and Cloud metrics—such as `apiserver_request_duration_seconds`—used in burn-rate-aware alert policies.