# Grafana + Datadog integration > Automate metric flows, alert routing, and dashboard sync between Grafana and Datadog — no glue code required. **Canonical page:** https://tray.ai/connectors/grafana-datadog-integrations/ **Grafana connector:** https://tray.ai/connectors/grafana-integrations/ **Grafana documentation:** https://tray.ai/documentation/connectors/service/grafana **Datadog connector:** https://tray.ai/connectors/datadog-integrations/ **Datadog documentation:** https://tray.ai/documentation/connectors/service/datadog ## Overview Grafana and Datadog are two of the most widely adopted observability platforms in modern engineering organizations. Grafana is built for flexible, open-source visualization across diverse data sources; Datadog handles infrastructure monitoring, APM, and log management in a single SaaS platform. Teams that rely on both tools often end up with duplicated alerting rules, siloed dashboards, and manual reconciliation of incident data across systems. Integrating Grafana and Datadog through tray.ai keeps both platforms in sync and eliminates the toil of managing them independently. Engineering and SRE teams frequently run Grafana alongside Datadog because each platform does something different. Grafana has highly customizable dashboards that pull from Prometheus, InfluxDB, Loki, and dozens of other sources. Datadog is where you go for infrastructure health, distributed tracing, and anomaly detection. Without an integration layer, on-call engineers have to manually cross-reference alerts firing in Datadog with correlated visualizations in Grafana — and that slows down mean time to resolution (MTTR). Connecting the two platforms via tray.ai lets teams automatically propagate alerts, sync annotation events, route incidents to the right dashboards, and maintain consistent monitoring coverage across both tools. ## Use cases ### Bidirectional Alert Synchronization When a Datadog monitor triggers an alert, tray.ai automatically creates a corresponding Grafana annotation on the relevant dashboard panels, giving engineers immediate visual context around the incident timeline. Resolved alerts in Datadog are reflected in Grafana in real time, so both platforms stay aligned without manual updates. - No more manually cross-referencing Datadog alerts with Grafana dashboards during an incident - Cut mean time to resolution by surfacing correlated visualizations alongside active alerts - Maintain a consistent annotation history across both platforms for post-incident reviews ### Automated Incident Annotation from Datadog Deployments Every time Datadog detects a deployment event or a CI/CD pipeline completes, tray.ai pushes an annotation into the relevant Grafana dashboards, marking exactly when code changes shipped. SRE teams can immediately see whether metric degradations line up with recent releases. - Automatically correlate deployment events with performance regressions in Grafana - Cut investigative time by contextualizing metric spikes against deployment timelines - Build an audit trail of deployments that persists in Grafana dashboards for future reference ### Datadog Metric Export to Grafana Data Sources tray.ai can periodically pull metric data from the Datadog Metrics API and push it into external data stores — such as InfluxDB or PostgreSQL — that Grafana is already querying, making Datadog metrics available in unified Grafana dashboards alongside data from other sources. This is especially useful for teams that want a single consolidated view across their entire infrastructure. - Consolidate Datadog and non-Datadog metrics into a single Grafana dashboard without duplicating instrumentation - Preserve Datadog metric history in a data store you own and control - Correlate Datadog infrastructure metrics with application metrics from other tools in one place ### On-Call Escalation Enrichment When a Grafana alert fires, tray.ai queries Datadog for related APM traces, log events, and infrastructure health signals, then packages that context into a notification delivered to PagerDuty, Slack, or your incident management tool of choice. Engineers arrive at an incident already knowing what happened, rather than scrambling to gather it from multiple platforms. - Pre-populate incident notifications with Datadog context so first-response investigation starts faster - Improve alert fidelity by combining Grafana threshold alerts with Datadog anomaly detection signals - Reduce alert fatigue by correlating signals from both platforms before escalating to on-call teams ### SLA and Uptime Reporting Automation tray.ai pulls SLO and uptime data from Datadog and automatically generates or updates reporting dashboards in Grafana, giving leadership and engineering teams consistent, always-current reliability metrics. Scheduled workflows refresh reports daily, weekly, or monthly without anyone touching them manually. - Deliver accurate SLA reports to stakeholders without manually exporting data from Datadog - Keep Grafana executive dashboards current with live Datadog SLO status - Take the reporting burden off SRE teams by automating the data pipeline between both tools ### Cross-Platform Dashboard Provisioning When new services are onboarded and dashboards are created in Datadog, tray.ai triggers automatic provisioning of corresponding Grafana dashboards using predefined templates, so monitoring parity across both platforms is there from day one. No more situations where a new service shows up in Datadog but is missing from Grafana entirely. - Every new service gets dashboard coverage in both Grafana and Datadog from the moment it's deployed - Enforce organizational dashboard standards by driving Grafana provisioning from Datadog service catalog events - Reduce manual configuration work for platform engineering teams during service onboarding ### Shared Downtime and Maintenance Window Management When a maintenance window is scheduled in Datadog to suppress alerts during planned downtime, tray.ai automatically creates a matching silenced alert period or annotation in Grafana, preventing false-positive noise in both platforms at once. When the window closes, both systems resume normal alerting together. - Prevent duplicate alert noise during planned maintenance by keeping both platforms synchronized - Stop scheduling maintenance windows twice in two separate systems - Maintain a shared maintenance event history in both Grafana and Datadog for compliance and auditing ## Templates ### Datadog Alert to Grafana Annotation Automatically creates a Grafana annotation on a specified dashboard whenever a Datadog monitor transitions to an alert or resolved state, providing instant visual context on time-series panels. Connectors used: Datadog, Grafana ### Grafana Alert to Datadog Event When a Grafana alert rule fires or resolves, this template pushes a corresponding event into the Datadog Events stream, giving you full cross-platform visibility in the Datadog event timeline and triggering any Datadog workflows downstream. Connectors used: Grafana, Datadog ### Datadog SLO Sync to Grafana Dashboard On a scheduled cadence, this template retrieves current SLO status and budget burn data from Datadog and updates a designated Grafana dashboard's data source or annotations to reflect up-to-date reliability metrics for stakeholder reporting. Connectors used: Datadog, Grafana ### New Datadog Monitor to Grafana Dashboard Provisioning When a new Datadog monitor is created for a service, this template checks whether a corresponding Grafana dashboard exists and, if not, provisions one from a predefined template — so monitoring parity across both platforms is automatic. Connectors used: Datadog, Grafana ### Shared Maintenance Window Synchronization Automatically mirrors a Datadog downtime schedule into a Grafana alert silence or annotation, so both platforms suppress noise and record planned maintenance events in parallel without requiring manual configuration in each tool. Connectors used: Datadog, Grafana ### Enriched Incident Notification from Grafana Alert with Datadog Context When a Grafana alert fires, this template queries Datadog for correlated APM traces and infrastructure events within the same time window, then delivers an enriched incident summary to Slack or PagerDuty so on-call engineers have full context immediately. Connectors used: Grafana, Datadog ## Challenges Tray.ai solves ### Keeping Alert States Consistent Across Both Platforms Datadog and Grafana each maintain their own alerting state machines, and when an alert resolves in one platform it doesn't automatically update the other. The result is stale alert banners, mismatched annotation histories, and on-call engineers receiving conflicting signals from two systems that should be telling the same story. **How Tray.ai helps:** tray.ai listens to webhook events from both Grafana and Datadog and propagates state changes bidirectionally in real time. Configurable conditional logic ensures that only meaningful state transitions trigger cross-platform updates, preventing feedback loops while keeping both systems accurate. ### API Authentication and Token Management Grafana uses API keys or service account tokens scoped to specific organizations, while Datadog relies on application keys paired with API keys. Managing these credentials securely across automated workflows — especially in multi-environment setups with separate staging and production instances — is a real operational headache. **How Tray.ai helps:** tray.ai stores authentication tokens for both Grafana and Datadog in an encrypted credential store, with support for environment-specific configurations. Credentials are never exposed in workflow logs, making multi-environment integrations straightforward to manage safely. ### Data Format Mismatch Between Grafana and Datadog APIs Grafana's annotation and alerting APIs use a different schema than Datadog's Events and Monitors APIs. Field names, timestamp formats, severity enumerations, and tag structures all differ between the two platforms. Writing transformation logic by hand to bridge these differences is error-prone and breaks quietly. **How Tray.ai helps:** tray.ai's built-in data mapping tools let teams visually define how fields from Datadog payloads map to Grafana API schemas — and vice versa — without writing custom code. JSONPath expressions, conditional branching, and format conversion helpers handle translating between the two platforms' data models. ### Rate Limiting and API Quota Management Both Datadog and Grafana enforce API rate limits that can become a real constraint when workflows need to sync large volumes of annotations, events, or metric data in near-real-time. Unmanaged integrations can exhaust API quotas, causing failed syncs and data gaps in dashboards. **How Tray.ai helps:** tray.ai automatically retries failed Grafana and Datadog API calls with exponential backoff, and supports configurable throttling at the workflow level to stay within rate limits. Transient quota errors don't cause silent data loss — operations queue for retry instead. ### Multi-Instance and Multi-Organization Complexity Large engineering organizations often run multiple Grafana instances — one per team or environment — alongside one or more Datadog accounts. Routing data to the right Grafana instance or Datadog account based on service ownership, environment, or team requires conditional logic that's painful to maintain in custom scripts. **How Tray.ai helps:** tray.ai workflows support dynamic connector configuration, so a single workflow can route data to different Grafana instances or Datadog accounts based on metadata in the triggering event — service tags, environment labels, team identifiers. No separate scripts, no duplicate workflows for each environment. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/