Amazon Managed Grafana is a fully managed service for the open-source Grafana visualization platform. It provides enterprise-ready Grafana workspaces with built-in authentication (AWS IAM Identity Center, SAML), automatic scaling, and native integration with AWS data sources. For AI projects, Managed Grafana serves as the operational dashboard layer for monitoring model performance, data pipeline health, and infrastructure metrics.

Official documentation: https://docs.aws.amazon.com/grafana/ Pricing: https://aws.amazon.com/grafana/pricing/ Service quotas: https://docs.aws.amazon.com/grafana/latest/userguide/AMG-limits.html

Foundations first

Before the specifics, the plain-English idea. A dashboard is a single screen that gathers numbers from many systems and draws them as charts, tables, and gauges so a person can understand the state of those systems at a glance. Grafana is the most widely used open-source tool for building such dashboards. It does not store data itself: it queries other systems (a metrics database, a logs store, a cloud monitoring service) and renders the answers. This habit of watching a running system through its metrics, logs, and traces is called observability, and it is the difference between guessing why something broke and seeing it.

“Managed” means AWS runs the Grafana servers for you. You do not install, patch, scale, or back up anything. You get a web URL (a workspace), you sign in, and you build dashboards. The trade-off is the usual managed-service one: less control over the underlying machine in exchange for far less operational work.

If you are new to the surrounding ideas, start with these:

  • Observability - understanding a system’s internal state from its outputs (metrics, logs, traces). See /glossary/observability/.
  • Grafana - the open-source dashboard and visualization tool this service hosts. See /glossary/grafana/ and the self-managed /tools/grafana/.
  • Prometheus - the de facto open-source metrics database and query language. See /glossary/prometheus/.
  • Authentication and authorization - proving who a user is, then deciding what they may do. See /glossary/authentication-and-authorization/.
  • SLA, SLO, SLI - the language of reliability targets that dashboards and alerts track. See /glossary/sla-slo-sli/.

Core Concepts

Workspace - An isolated Grafana instance with its own URL, user management, and configuration. You choose the Grafana version when you create a workspace, and AWS scales it automatically based on usage. As of April 2026, new workspaces can be created with Grafana version 12.4, and AWS supports an in-place upgrade from version 10.4 to 12.4 from the console, SDK, or CLI (announced May 2026). Workspaces support Grafana plugins, alerting, and standard Grafana features. Version 12.4 adds the Scenes-powered rendering engine, queryless Drilldown apps for Prometheus, Loki, Tempo, and Pyroscope, and an updated Amazon CloudWatch plugin.

Data Sources - Connections to backend data stores. Managed Grafana has native support for CloudWatch, Timestream, OpenSearch, Prometheus, X-Ray, and many third-party sources. Data source credentials are managed through IAM roles, eliminating the need to store database passwords in Grafana configuration.

Dashboards - Collections of panels (charts, tables, gauges, maps) that visualize data from one or more data sources. Dashboards support variables, templating, and time range controls for interactive exploration.

Authentication and Access Control

Managed Grafana integrates with AWS IAM Identity Center (formerly SSO) or any SAML 2.0 identity provider. Users are assigned roles: Viewer (read-only dashboard access), Editor (create and modify dashboards), or Admin (workspace configuration). This maps well to enterprise teams where data scientists need Editor access and business stakeholders need Viewer access.

Service-managed permissions allow the workspace to automatically discover and connect to AWS data sources in your account without manual credential configuration.

AI/ML Monitoring Dashboards

The primary value of Managed Grafana for AI projects is operational monitoring. Common dashboard patterns include:

Model performance tracking - Visualize prediction accuracy, latency percentiles, and throughput over time. Connect to CloudWatch metrics emitted by SageMaker endpoints or custom metrics from your inference pipeline. Set alerts when accuracy drops below a threshold or latency exceeds SLA targets.

Data pipeline health - Monitor Glue job success rates, Step Functions execution status, and data freshness. A single dashboard showing pipeline status across all stages (extraction, transformation, model training, deployment) provides situational awareness that prevents issues from cascading.

Cost monitoring - Track AWS spend across AI services using CloudWatch billing metrics. Visualize SageMaker training costs, Bedrock API call volumes, and infrastructure costs on a single dashboard. This visibility is critical for projects where inference costs can grow unpredictably.

Drift detection - Plot feature distributions and prediction distributions over time. Statistical shifts become visible as distribution charts change shape, enabling early detection of model drift before performance degradation becomes significant. See /glossary/drift-detection/, /glossary/model-drift/, and /glossary/data-drift/ for the underlying concepts.

Alerting

Managed Grafana supports Grafana Alerting with notification channels including SNS, Slack, PagerDuty, and email. Alerts can be configured with multiple conditions, evaluation intervals, and notification policies that route alerts to different channels based on severity.

For AI operations, configure alerts for: model endpoint errors exceeding a threshold, training job failures, data pipeline delays beyond acceptable windows, and cost anomalies.

Prometheus Integration

For teams running containerized ML workloads on EKS (/glossary/kubernetes/), Managed Grafana pairs with Amazon Managed Service for Prometheus for a complete monitoring stack. Prometheus (/glossary/prometheus/) scrapes metrics from ML containers (GPU utilization, batch processing rates, queue depths), and Grafana visualizes them. This combination provides Kubernetes-native monitoring without managing Prometheus or Grafana infrastructure. Version 12.4 adds queryless Drilldown apps, which let you explore Prometheus metrics and Loki logs by pointing and clicking rather than writing PromQL or LogQL by hand.

Pricing

Managed Grafana charges per active user per month, with pricing varying by license tier. An active user is anyone who logged in or made an API request at least once during the monthly billing cycle. As of June 2026, an Editor or Administrator license is 9 USD per active user per month and a Viewer license is 5 USD per active user per month. Enterprise plugins that unlock certain third-party data sources cost an additional 45 USD per active user per month. There is no charge for the workspace itself or for running data source queries, and AWS offers a 90-day free trial with up to five free users per account. Always confirm current figures on the official pricing page, since prices and region availability change.

This per-user pricing model means costs scale with team size rather than data volume, making it predictable for budgeting. Contrast this with self-managed Grafana (/tools/grafana/), which is free to run but shifts the cost to the infrastructure and operations you maintain yourself.

Best practices

For AWS workloads, AWS publishes prescriptive guidance in the Well-Architected Framework. The Operational Excellence pillar’s “Implement observability” guidance describes how to design metrics, logs, and traces so issues are caught early and responses are effective, which is exactly what a Managed Grafana dashboard layer supports. See Implement observability (Operational Excellence Pillar) and the wiki’s /glossary/observability/, /glossary/site-reliability-engineering/, and /glossary/aiops/ entries.

Practical tips: prefer service-managed permissions and IAM Identity Center over storing static credentials, scope users to Viewer unless they need to edit, define alerts against /glossary/sla-slo-sli/ targets rather than raw thresholds, and version-control dashboards as JSON so they can be reviewed and redeployed.

Sources