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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 - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Introducing Metrics from Logs and Log Rehydration™
2019-07-17 · via Datadog | The Monitor blog

As your application grows in size and complexity, it becomes increasingly difficult to manage the number of logs it generates and the cost of ingesting, processing, and analyzing them. Organizations often have little control over fluctuations in the volume of logs generated—and the resulting costs of collecting them—so they are forced to limit the number of logs generated by their applications, or to pre-filter logs before sending them to their log management platform. Teams create gaps in log coverage when they have to choose which logs to keep or throw away, making it impossible to react quickly to changes in their logs.

Stream all of your logs, keep only what you need

Datadog’s approach to log management, which we call Logging without Limits™, enables you to send all your logs to Datadog simply and cost-effectively. You can observe a real-time stream of all of your processed logs, and decide on the fly which logs are valuable enough to retain in Datadog for further analysis. All your logs, whether you retain them in Datadog or not, can be archived in your own cloud storage to maintain a complete history of your operations for any technical, security, or business audit.

We are excited to announce two additions to Datadog’s Logging without Limits™ feature set, which provide even more flexibility in how you monitor and analyze your log data:

Generate metrics from logs

List of metrics generated from logs

Data sources such as servers, containers, and cloud services all generate a high volume of logs. Those raw logs often don’t provide a lot of value individually. For example, a typical web access log usually won’t give you any insight into the health of your web server. But, in aggregate, access logs are high-value because they show trends in key indicators like request latency and server status.

With Datadog, you can now build aggregated views of your log data by creating metrics from any of your ingested logs, regardless of the source platform, language, or tool. Instead of retaining and sifting through a large number of logs, you can create a single metric to track the trends those logs reflect. Datadog retains that metric at full granularity for 15 months.

To create a new metric, navigate to the “Generate Metrics” tab on the Log Configuration page of your account. Filter the log data using any attribute or tag group that exists in your processed logs, then group by any dimension such as host, availability zone, or service.

Generate a new metric from your logs

Once you name and save your metric, you will quickly be able to view it in Datadog dashboards and graphs, and set up sophisticated alerts to detect abnormal trends. For example, you can use the user agent data in your web access logs to create a metric that captures browser usage trends for your application. You can also apply analytics such as forecasts and anomaly detection to your generated metrics.

Creating metrics from logs reduces the costs of indexing and retaining your logs, so you can automatically archive the underlying logs in cloud storage such as Amazon S3 or Azure blobs. And, if you notice abnormal activity in a metric, you can seamlessly reload related logs from an archive for further troubleshooting.

Reload logs from your archives

List of logs pulled from archives

Datadog’s Log Rehydration™ enables you to not only archive all of your logs in your cloud storage solution of choice, but also retrieve a subset of logs from an archive on demand. You can quickly access archived data at any time for investigating incidents or conducting technical, security, or business audits.

To reload logs from an archive, navigate to the Logs Configuration page in your account and click on the “Reload from Archives” tab. Click on the “New Historical View” button, select the archive and time period for the logs you need to access, and query the logs using free-text search or attributes such as service, source, or status.

Rehydrate your logs and pull critical logs from archives

Datadog will automatically retrieve your logs based on the criteria you set for the historical view and display them in the Log Explorer so you can quickly view log details or pivot from a log to related traces, just as you would with new logs.

List of metrics generated from logs

The ability to rehydrate your logs supports better retention practices, further reducing costs. For example, you can reduce the number of logs you index and retain, or decrease retention periods for your indexed logs, because you can always seamlessly retrieve all the logs you need from an archive.

No limits to logging

Log Rehydration™ and Metrics from Logs are now generally available. Together with the rest of the Logging without Limits™ feature set, these new features provide you a cost-effective way for capturing all of your log data and dynamically retaining the logs that you deem most important. If you are already using Datadog, you can learn more about Logging without Limits™ in our documentation. Or you can sign up for a free 14-day trial to start monitoring your logs, metrics, and traces today.