GitLab + Datadog
Connect GitLab and Datadog to Ship Faster and Debug Smarter
Stop switching between your CI/CD pipeline and your monitoring dashboards. tray.ai keeps them in sync automatically.
Why integrate GitLab and Datadog?
GitLab runs your development lifecycle. Datadog watches your production systems. When they don't talk to each other, engineers end up doing the translation manually — cross-referencing deployment times with latency graphs, reconstructing timelines during incidents, and opening tickets by hand when an alert fires. That's slow and error-prone, especially when things are already on fire. Connecting GitLab to Datadog through tray.ai closes that loop automatically, so the context you need moves with you instead of waiting in a different browser tab.
Automate & integrate GitLab & Datadog
Use case
Automatic Deployment Markers in Datadog
Every time a GitLab CI/CD pipeline completes a successful deployment, tray.ai sends a deployment event to Datadog, annotating your metrics graphs with release markers tied to the exact commit. On-call engineers and SREs get an instant visual timeline of when code changes hit production relative to any performance shift. No more manually logging deployments or guessing which release introduced a regression.
Use case
GitLab Issue Creation from Datadog Alerts
When Datadog triggers a monitor alert for elevated error rates, latency thresholds, or infrastructure anomalies, tray.ai automatically creates a structured GitLab issue assigned to the appropriate team. The issue arrives pre-populated with alert details, affected service, severity, and a direct link to the Datadog monitor. Engineers can start triaging immediately rather than waiting for someone to manually file the ticket.
Use case
Pipeline Failure Alerts Enriched with Datadog Metrics
When a GitLab CI/CD pipeline fails, tray.ai queries Datadog for relevant infrastructure and application metrics from the failure window and attaches them directly to the failure notification. Engineers reviewing a broken build can immediately see whether the failure coincided with resource exhaustion, a downstream service outage, or abnormal error rates — without opening a second tool to check.
Use case
Automated Rollback Pipelines Triggered by Datadog Monitors
When a Datadog monitor detects critical post-deployment degradation — a sudden spike in 5xx errors, or a drop in core business metrics — tray.ai can automatically trigger a GitLab rollback pipeline to revert to the last known stable release. The workflow captures the triggering alert, logs the rollback event as a GitLab issue for post-mortem purposes, and notifies the responsible squad. What could be a prolonged outage becomes a contained, automated recovery.
Use case
Security Vulnerability Alerts Flowing into GitLab Issues
Datadog's security monitoring can detect runtime threats and anomalous behaviors in production. With tray.ai, those security signals are automatically converted into GitLab security issues, tagged with the appropriate severity label, and assigned to the security or platform engineering team. Security findings from production don't sit in a separate dashboard waiting for someone to notice them — they show up where engineering work actually happens.
Use case
Merge Request Risk Scoring Based on Datadog Service Health
Before a merge request gets approved, tray.ai can query Datadog for the current health of the services that MR touches — checking for open monitors, error rate trends, and recent deployment stability. If the targeted service is already degraded, the workflow posts a warning comment on the GitLab MR, recommends a hold, or automatically applies a do-not-merge label. It's a simple way to avoid stacking a deployment onto a service that's already struggling.
Use case
Sprint and Release Reporting Combining GitLab and Datadog Data
At the close of a sprint or release cycle, tray.ai pulls GitLab data — merge counts, pipeline success rates, deployment frequency — alongside Datadog reliability metrics like MTTR, incident counts, and SLO compliance, then compiles them into a single engineering health report. It's delivered automatically to engineering leadership and posted to a shared team channel. No one has to spend an afternoon pulling numbers from two separate platforms.
Get started with GitLab & Datadog integration today
GitLab & Datadog Challenges
What challenges are there when working with GitLab & Datadog and how will using Tray.ai help?
Challenge
Mapping GitLab Projects to Datadog Services and Tags
GitLab organizes work by projects and groups. Datadog organizes observability data by services, environments, and custom tags. Without a clear mapping between the two, automated workflows risk sending deployment events to the wrong Datadog service, creating issues without the right labels, or missing monitors entirely when routing alerts back to GitLab. Maintaining this mapping by hand breaks every time a team renames a service or restructures their projects.
How Tray.ai Can Help:
tray.ai's workflow builder lets teams define a centralized service mapping lookup table that translates GitLab project identifiers to Datadog service names, environment tags, and team ownership. Every workflow references this mapping dynamically, and updating it in one place propagates the change across all your automations — no editing individual workflows as your organization changes.
Challenge
Handling Datadog Webhook Volume Without Alert Fatigue
In production environments with dozens of monitors, Datadog can fire a high volume of webhooks in short windows — especially during an active incident. Without intelligent filtering, tray.ai workflows could create hundreds of duplicate GitLab issues, making the noise worse than doing it manually.
How Tray.ai Can Help:
tray.ai supports conditional logic and branching at every workflow step, so you can filter Datadog webhook payloads by severity, environment, monitor group, or alert state transition before any downstream action runs. Deduplication logic built on tray.ai's data storage can suppress duplicate issues for the same monitor within a configurable cooldown period.
Challenge
Securing Credentials and API Tokens Across Both Platforms
Connecting GitLab and Datadog means managing sensitive API tokens — GitLab personal access tokens with pipeline trigger permissions, plus Datadog API and application keys. Storing these insecurely, or rotating them without updating integrations, creates both a security risk and a brittleness that can silently break your automations at the worst possible moment.
How Tray.ai Can Help:
tray.ai stores all credentials in an encrypted, centralized authentication system that keeps secrets separate from workflow logic. When you rotate an API token, you update one credential record and the change propagates across every workflow that uses it. Role-based access controls ensure only authorized team members can view or modify stored credentials.
Challenge
Keeping Deployment Event Timing Accurate Across Asynchronous Pipelines
Multi-stage GitLab pipelines with parallel jobs can have complex completion timing. Figuring out exactly when a deployment to a specific environment actually finished isn't always obvious — and sending a Datadog deployment event too early, or at the wrong stage, produces misleading annotations that make incident correlation harder rather than easier.
How Tray.ai Can Help:
tray.ai's GitLab connector supports granular event filtering, so teams can trigger workflows on specific pipeline stage completions and environment-scoped deployment events rather than on generic pipeline events. Datadog deployment markers reflect the true moment a specific environment was updated, preserving the accuracy of your post-deployment monitoring windows.
Challenge
Maintaining Integration Reliability During Datadog or GitLab Outages
Both GitLab and Datadog are infrastructure tools your team depends on, and either can experience API degradation or brief outages. Without retry logic and failure handling, integration workflows can silently drop deployment events or fail to create GitLab issues during exactly the moments when that reliability matters most.
How Tray.ai Can Help:
tray.ai includes configurable retry logic, error handling branches, and workflow-level failure alerting so transient API errors don't result in lost events or silent failures. Failed workflow runs are logged with full execution context, and teams can replay or audit them after an API degradation resolves — no deployment event or critical alert gets permanently dropped.
Start using our pre-built GitLab & Datadog templates today
Start from scratch or use one of our pre-built GitLab & Datadog templates to quickly solve your most common use cases.
GitLab & Datadog Templates
Find pre-built GitLab & Datadog solutions for common use cases
Template
GitLab Deployment to Datadog Event Marker
Automatically sends a deployment event to Datadog whenever a GitLab CI/CD pipeline completes a successful production deployment, annotating all relevant metric dashboards with release context including branch name, commit SHA, and deploying user.
Steps:
- Trigger on GitLab pipeline completion with a 'success' status and a production environment tag
- Extract deployment metadata including project name, commit SHA, branch, and pipeline author
- Send a formatted deployment event to Datadog via the Events API with appropriate tags and service labels
Connectors Used: GitLab, Datadog
Template
Datadog Monitor Alert to GitLab Issue
When a Datadog monitor transitions to an ALERT or NO DATA state, this template automatically creates a GitLab issue in the appropriate project, pre-populated with alert details, severity, affected service, and a direct link to the Datadog monitor for fast triage.
Steps:
- Receive Datadog webhook payload when a monitor alert fires or changes state
- Parse alert metadata including monitor name, severity, affected host or service, and alert message
- Create a GitLab issue with structured title, description, severity label, and assignee based on the affected service owner mapping
Connectors Used: Datadog, GitLab
Template
Critical Datadog Alert to GitLab Rollback Pipeline Trigger
Watches Datadog for critical post-deployment alert conditions and, when thresholds are breached within a configurable window after a GitLab deployment, automatically triggers a GitLab rollback pipeline and creates a linked incident issue for post-mortem tracking.
Steps:
- Receive a critical-severity Datadog monitor alert webhook and check if it falls within a post-deployment observation window
- Identify the last successful deployment pipeline in the affected GitLab project and extract the previous stable commit reference
- Trigger a GitLab rollback pipeline against the stable commit and create an incident issue linking the Datadog alert, the failed deployment, and the rollback pipeline
Connectors Used: Datadog, GitLab
Template
GitLab Pipeline Failure Enriched with Datadog Metrics
Enriches GitLab pipeline failure notifications by automatically querying Datadog for infrastructure and application metrics from the failure time window and appending the findings as a comment on the failed pipeline's associated merge request.
Steps:
- Trigger on a GitLab pipeline failure event and capture the failure timestamp, project, and associated merge request
- Query Datadog Metrics API for CPU, memory, error rate, and latency data for the relevant service during the failure window
- Post a formatted Datadog metrics summary as a comment on the GitLab merge request to give developers immediate infrastructure context
Connectors Used: GitLab, Datadog
Template
Datadog Security Signal to GitLab Security Issue
Converts Datadog security monitoring signals into structured GitLab security issues, automatically tagged by severity and assigned to the appropriate team, so production runtime threats are tracked and actioned within the engineering workflow rather than sitting in a separate dashboard.
Steps:
- Receive a Datadog security signal webhook and extract signal type, severity, affected resource, and detection rule name
- Map signal severity to a GitLab issue label and determine the responsible team based on the affected service
- Create a GitLab issue in the security or platform project with full signal context, remediation guidance link, and appropriate assignees
Connectors Used: Datadog, GitLab
Template
Weekly Engineering Health Report from GitLab and Datadog
Pulls GitLab deployment frequency, pipeline success rate, and merge request throughput together with Datadog SLO compliance, incident count, and MTTR into a unified weekly engineering health report delivered to a designated channel or email distribution list.
Steps:
- On a weekly schedule, query GitLab API for pipeline runs, deployment counts, MR merge rates, and mean pipeline duration for the past seven days
- Query Datadog for SLO status, monitor alert counts, and average MTTR across monitored services for the same period
- Compile a structured report combining both datasets and deliver it via the configured notification channel with trend comparisons against the previous week
Connectors Used: GitLab, Datadog