Corma directly integrates with Datadog for automated user provisioning and Identity Access Management (IAM) as a service
Datadog is a cloud-based observability and monitoring platform used by engineering, DevOps, and security teams to track application performance, infrastructure health, logs, metrics, and security signals in real time. It helps organizations detect incidents faster, troubleshoot system issues, and maintain reliability across complex cloud environments.
As Datadog becomes a central source of operational visibility, access to dashboards, logs, and alerts is often shared across engineering and security teams. This makes it a high-impact system where strong identity governance is essential.
Multiple pricing dimensions
Costs come from hosts, containers, logs, traces, and add-ons—not just users.
Usage-based billing creates unpredictability
Log ingestion, metrics, and traces can spike costs suddenly.
Overlapping modules increase spend
Teams enable APM, logs, RUM, and security tools without centralized control.
No visibility into cost per team or service
Engineering teams cannot easily attribute costs to specific apps or environments.
Over-provisioned monitoring
Unused hosts, excessive log retention, and redundant metrics inflate costs.
Unified usage visibility
Track hosts, logs, APM, and all Datadog modules in one dashboard.
Service-level cost attribution
Map spend to applications, teams, or environments.
Log and metric optimization
Identify excessive ingestion, redundant logs, or unnecessary retention.
Idle resource detection
Find inactive hosts or services still generating cost.
Module usage optimization
Ensure only required Datadog features are enabled.
Datadog uses a usage-based model, charging for hosts, logs (GB ingested), APM, and additional monitoring features.
No. Pricing is primarily based on infrastructure usage, data ingestion, and enabled modules, not seats.
Log ingestion volume, number of hosts, APM usage, and data retention policies.
Because it charges across multiple dimensions (hosts + logs + traces + add-ons), which scale quickly in large environments.
Yes. It helps optimize log volume, remove unused resources, and control module usage across teams.
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