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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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Introducing Live Container monitoring
2017-09-28 · via Datadog | The Monitor blog

To make your dynamic container infrastructure more observable, Datadog is introducing a powerful new Live Container view that gives you insight into the status and performance of all your containers in real time.

Docker adoption is exploding; according to our recent survey, almost a fifth of our users run Docker-ized workloads, representing a growth of 40 percent over the previous year. Containers allow you to run services in isolation, with high fault tolerance, and with better provisioning of resources. However, because of short lifetimes and hardware-agnostic behavior, they pose new challenges in infrastructure monitoring.

Taking inspiration from bedrock tools like htop and ctop, Live Containers gives you complete coverage of your container infrastructure, in a continuously updated table with resource metrics at two-second resolution and faceted search. Coupled with Datadog’s integrations with Docker, Kubernetes, ECS, and other container technologies, plus our built-in tagging of dynamic components, this new Live Container view provides a detailed overview of your containers’ health, resource consumption, and deployment in real time.

We’re going to need a bigger barge

Our research shows that Docker hosts run seven containers each, on average. And the churn rate of these containers is nine times faster than VMs. On top of that, the average Docker user quintuples their container usage between their first and ninth month after adoption. That’s … a lot to monitor:

Datadog's Live Container view provides a searchable, sortable interface for all your running containers.
Monitoring thousands of containers
Datadog's Live Container view provides a searchable, sortable interface for all your running containers.

Further complicating the picture, through many popular orchestrators, Docker users are generally able to abstract their operations from the virtual or physical machines that containers run on. A container lives and dies wherever there are compute resources available to schedule it.

Datadog’s tagging gives you the ability to cut through this complexity and focus on the layer you want to see. Use the Live Container view to filter to a specific Docker image, or pivot the table by Kubernetes service and namespace, or even host, and drill down from there. Integrations with popular orchestrators automatically detect and populate the facet explorer with integration-specific metadata and pull in container-level tags, so you can easily explore your container infrastructure on the fly.

In the above example, we group by host and by deployment, a tag that is automatically included from our Kubernetes integration.

Providing context

The table is an instantaneous look into your container ecosystem. If you need more extensive recent context we provide summary graphs that chart resource metrics for your containers over a longer timespan. In addition, each container can be inspected to see the behavior of an individual in the context of the group. This allows you to quickly identify your most resource-intensive containers or those that are behaving anomalously.

Summary graphs provide metrics over a longer timeframe.
Summary graphs for recent context
Summary graphs provide metrics over a longer timeframe.

In the above example, you can see that CPU utilization can be highly volatile. Because of this, all real-time resource metrics on the Live Containers page are graphed at two-second resolution. That way, important spikes aren’t smeared out by longer-timespan averages.

Visibility into provisioning

All resource metrics in the Live Container view are reported in relation to their provisioned limits. Not only does this make it easy to see which components are nearing resource saturation, but it allows you to quickly see which containers might be overprovisioned so you can take steps to better allocate resources.

Getting started

Whether you are just dipping your toe in, planning a major migration, or running full production workloads on containers, Datadog’s new Live Container view will help you understand your ecosystem, manage your provisioning, and debug effectively. To get started, all you need to do is update your Agent to version 5.17.2 or higher. Data collection for Live Containers is enabled by default, and you will find the new container view in your “Infrastructure” menu in the Datadog nav bar.

Live Container view menu item

If you’re not already a customer, you can start a 14-day free trial to get deeper visibility into your container infrastructure today.