What does dynatrace do and why would you use it over datadog or new relic?

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Dynatrace is a full-stack observability and application performance monitoring platform, and the answer to why a team might choose it over Datadog or New Relic is rooted in specific architectural choices Dynatrace made that have significant operational implications.

All three platforms — Dynatrace, Datadog, and New Relic — provide application performance monitoring, infrastructure monitoring, log management, and distributed tracing. At the feature inventory level, they're closely competitive. The meaningful differences are in philosophy and in the degree of automatic configuration versus manual instrumentation required to get the platform working.

Dynatrace's most distinctive characteristic is its OneAgent approach and its AI engine, called Davis. OneAgent is a single agent deployed on a host that automatically discovers, instruments, and monitors everything running on that host — the operating system, all processes, all services, all dependencies — without requiring developers to add instrumentation code to individual applications or configure individual monitors for each service. For large environments with hundreds of services and complex microservice architectures, this auto-discovery and auto-instrumentation means the platform can produce a complete application topology map shortly after deployment, without manual configuration of each component. Davis, the AI engine, then continuously analyzes the telemetry from that auto-discovered topology to identify root causes of performance problems, correlating events across the stack automatically.

This automation has a specific value proposition: in organizations where the engineering team running observability is small relative to the scope of the environment being monitored, having the platform configure itself and surface root cause determinations automatically reduces the operational burden considerably. Security teams and SREs who would otherwise spend significant time configuring monitors, defining baselines, and manually correlating logs to traces to find the root cause of an incident find that Dynatrace's automated approach accelerates that work.

Datadog is often chosen when teams value a more composable, modular approach. Datadog's ecosystem of integrations is broader than most competitors, its log management and infrastructure monitoring capabilities are widely considered strong, and its flexibility for building custom dashboards and alerts gives engineering teams fine-grained control over exactly what they monitor and how. Teams that prefer to own their instrumentation and monitoring configuration, and that have the engineering bandwidth to do so, often prefer Datadog's model. It also has strong developer adoption because its APIs and SDKs integrate easily into CI/CD pipelines and development tooling.

New Relic has repositioned itself around a consumption-based pricing model that some organizations find more predictable for their usage patterns, and its entity-based observability model has matured significantly in recent years.

The honest caveat about Dynatrace is that the platform's comprehensiveness comes with complexity at the administrative level and a price point that reflects its enterprise positioning. Organizations with smaller engineering teams and simpler architectures sometimes find the setup period longer than expected before the automation benefits are fully realized. Dynatrace tends to deliver its strongest ROI in large, complex microservice environments where the auto-discovery and AI root cause analysis replace work that would otherwise require substantial manual effort.

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What does dynatrace do and why would you use it over datadog or new relic?