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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Monitor Google Compute Engine performance with Datadog
Jean-Mathieu Saponaro · 2016-09-22 · via Datadog | The Monitor blog
Jean-Mathieu Saponaro

Jean-Mathieu Saponaro

Google Compute Engine (GCE), part of the suite of services offered on the Google Cloud Platform (GCP), provides you on-demand and easily scalable virtual machines. Launched in 2013, it has become a serious alternative to AWS EC2 and has been adopted by major companies such as Spotify, which decided to move all its infrastructure to GCP in early 2016.

As the core of your cloud infrastructure, virtual machine instances need to be closely monitored in order to spot hiccups and bottlenecks, to enable rapid investigation of any issue, and to know when to scale up.

Datadog collects the Google Compute Engine performance metrics you need, and is an easy way to monitor your VM instances’ activity, health, and performance. Datadog provides highly targeted alerts, and can correlate what’s happening in GCE with metrics and events from the rest of your infrastructure.

Google Compute Engine default dashboard in Datadog
Google Compute Engine performance - default dashboard
Google Compute Engine default dashboard in Datadog

The Compute Engine performance metrics you need

Google Compute Engine’s performance metrics are collected by the Datadog integration so you can properly monitor your GCE instances with:

  • Status checks to see if your instances are down or running properly

  • Metrics tracking the percentage of allocated CPU currently in use on the instance. Note that some instance types allow bursting above 100% usage.

  • Throughput metrics to give you insights on:

    • I/O: number of read and write operations as well as volume of data read from or written to disk

    • Network: volume of data received and sent over the network

    • Saturation: number of throttled read and write operations as well as the corresponding amount of data being throttled

In our docs, you will find a list of all the metrics collected from GCE, with a brief description of each.

GCE CPU utilization

Datadog automatically tags your GCE metrics with their associated instance type, host name, availability zone, and more. Tags are automatically formatted as key:value pairs, such as `region:us-central1`, which allows you to aggregate your incoming metrics from different instances. For example, you can monitor the average CPU utilization by region and determine if additional machines need to be spun up to meet localized demand.

Any network tags and labels assigned to your Google Compute Engine instances will also appear as tags in Datadog.

You can use tags to slice and dice your metrics, and to filter and group your hosts for a comprehensive view of your infrastructure.

And all the power of Datadog

You can of course use all the features Datadog offers, from advanced alerting mechanisms to host maps and outlier detection, to properly monitor GCE and effectively investigate any performance issue.

Host map showing GCE instances running by zone
Compute Engine performance hostmap
Host map showing GCE instances running by zone

Host maps provide you a bird’s-eye view of all your Google Compute Engine instances so you can check their health at a glance. You can then filter and group them using any of your labels and tags.

Start monitoring GCE in a few seconds

If you are already a Datadog user, you can start monitoring GCE as part of our integration with Google Cloud Platform. Otherwise you can sign up for a free trial and immediately start monitoring your Google Compute Engine instances alongside the rest of your infrastructure, applications, and services.