GitHub + Datadog
Connect GitHub and Datadog to Ship Code Faster with Full Observability
Automate the bridge between your development workflow and production monitoring so your engineering teams always have context when they need it.
Why integrate GitHub and Datadog?
GitHub and Datadog sit at opposite ends of the software delivery lifecycle — where code is written and where it runs. Integrating the two gives engineering teams a direct feedback loop between pull requests, deployments, and production health metrics. Connect GitHub with Datadog on tray.ai and you can cut out manual status updates, speed up incident response, and make every code change traceable to a real system outcome.
Automate & integrate GitHub & Datadog
Use case
Automatic Deployment Event Tracking in Datadog
Every time a pull request is merged or a release is tagged in GitHub, tray.ai automatically sends a deployment event to Datadog, creating a visible marker on your dashboards and APM traces. Engineers can instantly correlate a spike in error rates or latency with the exact commit that introduced the change — no more manually annotating Datadog timelines after each deployment.
Use case
GitHub Issue Creation from Datadog Alerts
When Datadog fires a monitor alert or detects an anomaly, tray.ai automatically opens a GitHub issue in the relevant repository, pre-populated with alert details, affected services, and links back to the Datadog dashboard. Engineering teams don't lose track of operationally-driven bug reports, and every incident has a traceable action item in the development backlog. Issue severity labels map directly from Datadog alert priority levels.
Use case
CI/CD Pipeline Gating Based on Datadog SLOs
Before a GitHub Actions workflow proceeds to a production deployment step, tray.ai checks the current status of Datadog SLOs and service health monitors. If error budgets are burning too fast or a critical monitor is alerting, the pipeline pauses automatically or a hold is placed on the release — stopping you from piling instability on top of an already-struggling system. Teams define custom thresholds and exception workflows entirely in tray.ai without touching their CI/CD pipelines directly.
Use case
Incident Post-Mortem Automation
When a Datadog incident is resolved, tray.ai automatically compiles a post-mortem draft by pulling the incident timeline from Datadog and matching it against the GitHub commit log, PR descriptions, and recent deployments for affected services. The draft lands as a GitHub issue or wiki page so engineering leads can review and publish it without starting from a blank document — cutting post-incident analysis from hours to minutes.
Use case
Security Vulnerability Monitoring and Alert Routing
When GitHub's Dependabot or code scanning tools flag a critical vulnerability in a repository, tray.ai automatically creates a Datadog event and tags the affected services so on-call engineers can assess live exposure in production. At the same time, the vulnerability data gets enriched with Datadog service catalog information to identify which production environments are running the affected dependency — a direct line between code-level security findings and operational risk.
Use case
Release Notes and Changelog Generation for Datadog Dashboards
After each GitHub release is published, tray.ai extracts the release notes and associated PR metadata and pushes a formatted changelog entry as a Datadog event or notebook annotation. Product and engineering leaders can view a rolling changelog directly within their Datadog dashboards, overlaid on metrics, without switching context to GitHub. It's particularly useful for teams running multiple services where tracking what changed and when matters for capacity planning and stakeholder reporting.
Use case
On-Call Escalation Enrichment with GitHub Blame and PR Context
When Datadog pages an on-call engineer for a critical alert, tray.ai automatically enriches the incident notification with GitHub blame data, the most recent PR merged to the affected service, and the original PR author's contact details. The responder gets immediate developer context without digging through git history under pressure. The enriched notification can be delivered via PagerDuty, Slack, or email, all orchestrated through tray.ai.
Get started with GitHub & Datadog integration today
GitHub & Datadog Challenges
What challenges are there when working with GitHub & Datadog and how will using Tray.ai help?
Challenge
Keeping Deployment Markers in Sync Across Both Platforms
Engineering teams often rely on manual processes or fragile shell scripts to send deployment events to Datadog after a GitHub release, which leaves gaps in the timeline that make incident correlation unreliable. When scripts fail silently, Datadog dashboards show no deployment marker, and engineers investigating a production issue have no way to know if a code change was the root cause.
How Tray.ai Can Help:
tray.ai provides a reliable, event-driven trigger on GitHub release and push events that sends structured deployment data to Datadog every time, without manual intervention. Built-in error handling and retry logic mean deployment events are never silently dropped, and tray.ai's run history gives teams a complete audit log of every event sent.
Challenge
Mapping GitHub Repositories to Datadog Services at Scale
Large engineering organizations manage dozens or hundreds of GitHub repositories, each potentially mapping to one or more Datadog services with different naming conventions. Building and maintaining this mapping manually is error-prone, and without it, automated workflows either create issues in the wrong repository or tag the wrong Datadog service with deployment events.
How Tray.ai Can Help:
tray.ai supports dynamic lookup tables and data transformation steps that let teams maintain a centralized repository-to-service mapping that all workflows reference. When naming conventions change or new services are added, you update the mapping in one place and every connected workflow picks it up immediately — no code changes required.
Challenge
Avoiding Alert Fatigue from Bidirectional Automation
When GitHub and Datadog are connected bidirectionally without deduplication logic, feedback loops are easy to create — a Datadog alert creates a GitHub issue, which triggers a webhook, which creates another event in Datadog, adding more noise for already-stretched on-call teams. Simple point-to-point integrations rarely account for these edge cases.
How Tray.ai Can Help:
tray.ai lets teams build conditional logic, deduplication checks, and state-aware branching directly into integration workflows. Before creating a GitHub issue from a Datadog alert, a workflow can check whether an open issue for that alert already exists and skip creation if it does, keeping both systems clean.
Challenge
Handling GitHub API Rate Limits During High-Frequency Deployments
Teams that deploy frequently — especially those running feature flag releases or canary deployments — may trigger GitHub webhooks in rapid succession. Integrations that make a GitHub API call for every single event can quickly exhaust rate limits, causing workflows to fail and leaving Datadog without deployment context at precisely the moments it's most needed.
How Tray.ai Can Help:
tray.ai has built-in rate limit awareness and queues and batches API calls to GitHub intelligently, so even during high-frequency deployment windows, events are processed reliably without exceeding API quotas. Teams can configure throttling and batching behavior within tray.ai's workflow settings without writing custom rate-limit management code.
Challenge
Maintaining Workflow Reliability Across API Version Changes
Both GitHub and Datadog regularly update their APIs, deprecate endpoints, and introduce new authentication requirements. Teams that have built custom scripts or lightweight webhook relays to connect the two platforms end up constantly patching integrations when breaking changes arrive, pulling engineering time away from product work.
How Tray.ai Can Help:
tray.ai maintains and updates its GitHub and Datadog connectors as APIs evolve, so teams using tray.ai-built integrations are insulated from upstream breaking changes. When GitHub or Datadog releases a new API version, tray.ai updates the connector layer so workflows keep running without engineering teams having to modify or redeploy their automation.
Start using our pre-built GitHub & Datadog templates today
Start from scratch or use one of our pre-built GitHub & Datadog templates to quickly solve your most common use cases.
GitHub & Datadog Templates
Find pre-built GitHub & Datadog solutions for common use cases
Template
GitHub Release to Datadog Deployment Event
Automatically creates a Datadog deployment event and timeline marker every time a new GitHub release is published, capturing the release tag, author, and linked repository for instant dashboard correlation.
Steps:
- Trigger on GitHub release published event via webhook
- Extract release tag, commit SHA, author, and repository metadata
- Create a Datadog event with deployment type, service tags, and a deep link back to the GitHub release
Connectors Used: GitHub, Datadog
Template
Datadog Monitor Alert to GitHub Issue
Watches for Datadog monitor alerts at or above a configurable priority threshold and automatically creates a labeled GitHub issue in the target repository with full alert context, reducing response time and ensuring operational issues enter the engineering backlog.
Steps:
- Trigger on Datadog monitor alert webhook for P1 or P2 priority events
- Parse alert name, affected service, metric values, and Datadog dashboard URL
- Create a GitHub issue with pre-filled title, body, severity label, and assignee based on a service-to-team mapping
Connectors Used: Datadog, GitHub
Template
GitHub PR Merge to Datadog Service Metric Tag Update
When a pull request targeting a monitored service is merged, this template automatically updates the relevant Datadog service's tags to reflect the new version, enabling precise metric filtering and alerting based on software version.
Steps:
- Trigger on GitHub pull request merged event for configured repositories
- Extract target branch, repository name, and latest semantic version tag
- Update the corresponding Datadog service definition tags via the Datadog API
Connectors Used: GitHub, Datadog
Template
Datadog Incident Resolved to GitHub Post-Mortem Issue
Automatically drafts a structured post-mortem GitHub issue when a Datadog incident is marked as resolved, pulling the incident timeline, alert history, and matching it with recent GitHub deployments to the affected service.
Steps:
- Trigger on Datadog incident status change to resolved
- Retrieve incident timeline, affected services, and alert history from Datadog API
- Query GitHub for recent merges and releases to affected service repositories and create a post-mortem issue with combined timeline
Connectors Used: Datadog, GitHub
Template
GitHub Dependabot Alert to Datadog Security Event
Sends GitHub Dependabot vulnerability alerts to Datadog as tagged security events whenever a critical or high-severity dependency vulnerability is detected, so security risk is tracked alongside operational health.
Steps:
- Trigger on GitHub Dependabot alert created or updated webhook for critical or high severity
- Map affected GitHub repository to the corresponding Datadog service using a lookup table
- Create a Datadog event tagged with service name, CVE ID, severity, and a link to the Dependabot advisory
Connectors Used: GitHub, Datadog
Template
Datadog SLO Breach to GitHub Deployment Hold Comment
Monitors Datadog SLO status and automatically posts a hold warning comment on any open GitHub pull request targeting a deployment branch when an SLO breach is detected, preventing teams from merging during active instability.
Steps:
- Poll Datadog SLO status on a configurable schedule or trigger on SLO budget burn alert
- Identify open GitHub pull requests targeting the production or main branch of affected service repositories
- Post a standardized hold comment on each open PR with SLO details and a link to the Datadog dashboard
Connectors Used: Datadog, GitHub