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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
Expand your Sleuth monitoring reach with Datadog
2020-10-30 · via Datadog | The Monitor blog

Sleuth is a deployment tracking tool that gives you a deeper level of insight into your CI/CD workflows by tracking all of your team’s deployment tools from a single dashboard. Sleuth integrates with different components of your deployment pipeline and develops an understanding of your development processes. It can then automatically alert you as to when code is shipping, when manual approvals are needed, and when failures occur. Sleuth is built to work with a wide array of tools from across the code deployment toolkit, reducing the friction that occurs between code bases (like Github and Bitbucket), issue trackers (like Jira and Clubhouse), feature flaggers (like LaunchDarkly), and infrastructure-as-code providers (like Terraform).

We’re excited to announce that Datadog now integrates with Sleuth. Sleuth’s Datadog integration is easy to install, and, once configured, allows Sleuth users to tie Datadog’s rich metrics to the various sources of change that Sleuth monitors. From code and issue-tracking to feature flags and more, Sleuth can use your Datadog metrics to provide greater context around a feature or release from first concept to development, testing, and push to production

Full visibility into your Sleuth workflow

Sleuth organizes your deployments into projects, which collect and organize key data from your code sources and their associated staging environments. This data consists of metrics and errors. Metrics that Sleuth pulls from your deployment infrastructure might include the average response times of your APIs or the percentage of notifications sent within a five-minute SLA, while errors might include the rate of service errors sampled from across every active deployment in the project. Sleuth then uses this data to make smart inferences about which changes caused a chosen data fluctuation.

When you install the Datadog integration in Sleuth, Sleuth immediately begins pulling in the Datadog metrics of your choosing and attaching them to their related deployments. This gives you a big-picture perspective of your project’s health as measured across past and current deployments and allows Sleuth to detect unusual activity within your deployments and the tools you use to automate them. And, because Datadog integrates with many of the same services that Sleuth does, as well as more than 1,000 other technologies, you can enrich your Sleuth with monitoring data from across your deployment stack.

Visualize key Datadog performance metrics

Sleuth’s Datadog integration gives you a comprehensive view of your projects’ performance and lets you analyze incoming service level indicator (SLI) data across all of your Sleuth-integrated services from a single location. The default release window contains widgets that display key metrics like the average time elapsed for PR approval and the rate of errors within your deployment testing environments. You can easily customize the window to include visualizations of metrics from Datadog or other parts of your stack that you want to track, giving you insight into the metric variances, both large and small, that affect your deployments.

Sleuth's Datadog integration lets you visualize the deployment metrics it pulls from your Datadog monitoring infrastructure.

Elevate your Sleuth performance with Datadog

Sleuth’s Datadog integration gives you unparalleled clarity into your deployment workflow and expands Sleuth’s deployment tracking capabilities. And by integrating with over 1,000 other technologies, Datadog helps you detect errors across your entire stack and understand how they might be affecting your deployments. If you’re already a Datadog customer, you can start using the Sleuth integration right now. Otherwise, get started with a free 14-day trial.