Datadog
About Datadog
Datadog provides an observability and security platform. It brings together applications, infrastructure, data, models, and security into one place. The platform uses AI to detect and resolve issues.
Products
- Infrastructure Monitoring: Monitors infrastructure.
- Application Performance Monitoring: Monitors application performance.
- Log Management: Manages logs.
- Cloud Security: Addresses cloud security.
- Database Monitoring: Monitors databases.
- Incident Management: Handles incident management.
Used for
- Migrating to AWS
- Monitoring Kubernetes environments
- Managing cloud security posture
- Monitoring digital experience
- Analyzing and correlating logs
Who it is for
- Financial Services industry
- Manufacturing & Logistics industry
- Healthcare/Life Sciences industry
- Retail/E-Commerce industry
From Datadog's own website, read 2026-10-23. How we build listings.
How to evaluate APM & Distributed Tracing
This is how the CIOPages Research Team evaluates this category. It is not an assessment of Datadog. It comes from our Observability & APM Platforms buyer guide.
22%
Telemetry Coverage & Correlation
Unified metrics, traces, logs, real-user monitoring, synthetics, and continuous profiling; automatic correlation across signals; service maps and topology; Kubernetes, container, serverless, and cloud-provider auto-discovery; breadth and quality of the integration catalog
22%
Cost Control, Cardinality & Data Governance
Pricing-unit transparency and overage terms; high-cardinality handling without runaway cost; ingest-time filtering, sampling, aggregation, and tiered retention; a native or compatible telemetry pipeline; per-team budgets, quotas, and chargeback visibility so spend is governable, not just observable
20%
Distributed Tracing & APM Depth
End-to-end trace correlation across services and async boundaries, tail-based and adaptive sampling, code-level and method-level profiling, error tracking and exception grouping, database and dependency visibility, and frictionless OpenTelemetry trace ingestion alongside any native agent
16%
AIOps, Anomaly Detection & Root Cause
Quality of automated anomaly detection and alert noise reduction, causal versus correlative root-cause analysis, forecasting and capacity signals, and emerging agentic remediation — judged on accuracy during a real incident, not on the marketing name of the AI engine
12%
OpenTelemetry & Openness
Depth of native OTel support (traces, metrics, logs) versus a proprietary agent; whether collection is genuinely vendor-neutral or quietly locks you in; query-language portability; export and data-egress freedom; and how much re-instrumentation a future migration would actually require
8%
Scale, Reliability & Operability
Proven performance at your peak event and series volumes, multi-region availability and platform SLA, data-retention options, RBAC and SSO, and — if self-hosted — the operational burden of running and upgrading the backend at high cardinality
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Quick Facts
www.datadoghq.com Pricing pageCategoryObservability & Monitoring
SubcategoryAPM & Distributed Tracing
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