Dynatrace
About Dynatrace
Dynatrace uses a data lakehouse to analyze data in context.
Products
- AI Observability: Provides observability for generative AI applications, LLMs, and agents.
- Application Observability: Provides APM, distributed tracing, and profiling for cloud-native and enterprise stacks.
- Application Security: Discovers, prioritizes, and shields against vulnerabilities in real-time.
- Business Observability: Provides customizable analytics for business decisions in real-time.
- Digital Experience: Provides real-user and synthetic monitoring and session replays.
- Infrastructure Observability: Provides observability for multi-cloud environments.
Used for
- Troubleshooting using log data analytics
- Tracking requests across distributed services, APIs, and serverless workloads
- Validating critical user journeys, APIs, and services
- Detecting and remediating vulnerabilities at runtime
- Replaying end user sessions to diagnose production issues
From Dynatrace'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 Dynatrace. 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.dynatrace.com Pricing pageCategoryObservability & Monitoring
SubcategoryAPM & Distributed Tracing
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