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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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Troubleshoot faster with the GitLab Source Code integration in Datadog
2026-01-05 · via Datadog | The Monitor blog
Eric Metaj

Eric Metaj

Mark Azer

Mark Azer

Developers and SREs who rely on GitLab to develop their services often face significant friction when troubleshooting errors or fixing issues that degrade code quality. To understand the context of a problem, they resort to tab-hopping between observability tools and GitLab, connecting stack traces, spans, and profiles back to the right files and commits. At the same time, many teams lack the visibility to detect security vulnerabilities, infrastructure misconfigurations, and flaky tests early in the software development lifecycle. This often results in fixes that cost much more than they would have if the issues had been discovered and addressed earlier.

We’re excited to announce the general availability of our GitLab Source Code integration. By connecting your GitLab repositories to Datadog, you can bring rich source code context directly into your observability workflows and shift key insights left into everyday development. This integration unifies your source code with Datadog’s broader developer ecosystem, enhancing Datadog APM, CI Visibility, Code Security, Test Optimization, and other Datadog tools you use to observe modern applications.

In this post, we’ll look at how the GitLab Source Code integration helps you:

When you connect your GitLab.com or self-managed instance to Datadog, the GitLab Source Code integration can immediately analyze your repositories for vulnerabilities, code quality issues, and infrastructure-as-code misconfigurations. Because it connects to your repositories directly, you can start detecting these problems earlier and with less manual effort, without redesigning or instrumenting your CI pipelines.

From there, you can review findings in Datadog with direct links back to the relevant files and lines in GitLab. This gives security, platform, and development teams a shared view of code health across services and environments, helping them quickly understand where issues originate and which repositories and services to prioritize.

A side panel in Datadog Code Security providing information about a potential SQL injection detected in a GitLab respository, with next steps recommended for response and remediation.

Accelerate troubleshooting with code-aware APM

With the GitLab Source Code integration enabled, APM surfaces inline code snippets directly inside Datadog Error Tracking, Live Debugger, Continuous Profiler, and more. When an error or performance issue appears, APM shows GitLab code snippets that match stack traces or hot paths without requiring you to search for filenames and functions in GitLab manually.

With this context provided, APM users no longer need to jump between tools to understand failures. They can follow a trace to a representative error, inspect the associated code right in Datadog, and then jump straight to the corresponding file in GitLab. This helps teams identify root causes faster and move directly from detection to making the right code change.

Automate code reviews and shift left with merge request feedback

The GitLab Source Code integration also helps you catch issues before they reach production by posting actionable comments on GitLab merge requests (MRs). When combined with CI Visibility, Code Security, Code Coverage, and Test Optimization, Datadog can flag risky or insecure changes in the diff and add targeted feedback where it is most useful alongside the code under review.

Updates from Datadog service account in GitLab summarizing vulnerability scans, pipeline errors, and failed tests.

The automated MR comments encourage earlier detection of problems such as flaky tests, regressions in coverage, and new vulnerabilities, while reducing back-and-forth review cycles. Reviewers and authors can determine whether a change is ready to merge based on concrete telemetry data, then follow links from the comments back to detailed test runs or security findings in Datadog when deeper investigation is needed. In certain cases, Datadog can help resolve issues by suggesting fixes that can be applied directly in the MR interface, further reducing the context-switching needed to complete remediation.

Get started with the GitLab Source Code integration

The GitLab Source Code integration brings GitLab repositories into Datadog, giving your teams a single, code-aware view across APM, CI Visibility, Code Security, Test Optimization, and more. By connecting to your GitLab projects, you can analyze code health, debug incidents faster with inline code snippets, and surface automated feedback directly in merge requests so that problems are addressed before they impact users.

To get started, install the GitLab Source Code integration from the Datadog Integrations page and connect your GitLab.com or self-managed instance. For more information, see the GitLab Source Code integration documentation and the Datadog Source Code Integration guide. And if you’re new to Datadog, you can sign up for a 14-day free trial.