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Deployment & Hosting
D

Dynatrace

Dynatrace is an AI-powered observability and application performance monitoring platform for cloud environments. It helps teams monitor, analyze, and optimize software performance, infrastructure, logs, security, and user experience.

Starting atFrom $0.04/hour per 8 GiB host
Visit Dynatrace →
OverviewFeaturesPricingUse CasesLimitationsFAQAlternatives

Overview

Dynatrace is an enterprise observability platform that unifies application performance monitoring, infrastructure monitoring, log management, digital experience monitoring, and application security into a single AI-powered solution, with pricing starting at $0.04/hour for full-stack monitoring on a usage basis. It targets large enterprises and complex cloud-native organizations that need end-to-end visibility across hybrid and multi-cloud environments.

Founded in 2005 in Linz, Austria by Bernd Greifeneder, Sok-Kheng Taing, and Hubert Gerstmayr, Dynatrace (NYSE:DT) is headquartered in Boston, Massachusetts. The platform is built around three proprietary AI engines collectively branded as Davis AI — combining causal, predictive, and generative AI — to automatically detect anomalies, identify root causes, and recommend or automate remediation. Its OneAgent technology auto-discovers and instruments every component in an environment (containers, Kubernetes pods, VMs, databases, services, and end-user sessions) without requiring manual configuration, while the Smartscape topology map continuously visualizes dependencies in real time.

Dynatrace covers a wide functional surface: full-stack APM, distributed tracing (PurePath), real user monitoring (RUM), synthetic monitoring, infrastructure observability, log analytics powered by the Grail data lakehouse, business analytics, runtime application security, and DevSecOps automation through workflows and AutomationEngine. Compared to the other observability tools in our directory such as Datadog, New Relic, Splunk, and Grafana, Dynatrace differentiates itself with its deterministic AI root-cause analysis, single-agent deployment model, and Grail's index-free log querying via the DQL language. It is generally positioned at the higher end of the market — best suited for Fortune 1000 enterprises, regulated industries, and organizations running thousands of services where automation and causal AI deliver clear ROI rather than for small teams or hobbyists who may find the platform expensive and complex relative to lighter-weight alternatives.

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Key Features

Davis AI (causal, predictive, and generative)+

Davis is Dynatrace's three-part AI engine combining causal AI for root-cause analysis, predictive AI for forecasting capacity and seasonal anomalies, and generative AI through Davis CoPilot for natural-language queries. Because Davis leverages the real-time Smartscape topology, it produces deterministic answers — naming the exact failing component and downstream impact — rather than probabilistic correlations. This sharply reduces alert fatigue and accelerates incident resolution.

OneAgent and Smartscape auto-discovery+

A single OneAgent installed per host automatically detects every process, container, service, database, and dependency, then continuously builds the Smartscape topology graph. This eliminates most manual instrumentation work that engineers face with traditional APM or OpenTelemetry-only stacks. New services and infrastructure components appear in monitoring within minutes of deployment.

Grail data lakehouse with DQL+

Grail is Dynatrace's index-free, schema-on-read data lakehouse for logs, metrics, traces, events, and business data, queried via DQL (Dynatrace Query Language). Unlike traditional log management tools that require expensive indexing, Grail lets teams retain massive volumes of data affordably and run complex analytical queries on demand. This enables forensic investigations, security analytics, and business observability without pre-defining schemas.

Application Security and runtime vulnerability analytics+

Dynatrace Application Security uses the same OneAgent to detect vulnerable libraries at runtime and assess real exploit risk based on whether the code path is loaded, exposed, and reachable. This dramatically reduces false-positive CVE noise compared to static scanners. The module also supports runtime attack detection and protection, making it a credible DevSecOps tool alongside its observability stack.

AutomationEngine and Workflows+

AutomationEngine lets teams build event-driven, code-based workflows that connect Dynatrace signals to external systems like ServiceNow, Jira, PagerDuty, Slack, GitHub Actions, and Kubernetes. Workflows can trigger remediation, open incidents, post deployment validations, or enforce SLOs — turning Dynatrace from a passive monitoring tool into an active automation backbone for SRE and platform teams. Built-in templates and a visual editor lower the barrier to entry.

Pricing Plans

Full-Stack Monitoring

From $0.04/hour per 8 GiB host

  • ✓OneAgent auto-instrumentation
  • ✓APM and distributed tracing (PurePath)
  • ✓Infrastructure and Kubernetes monitoring
  • ✓Davis AI causal root-cause analysis
  • ✓Smartscape topology mapping

Infrastructure Monitoring

From $0.01/hour per host

  • ✓Host, container, and process monitoring
  • ✓Network and process metrics
  • ✓Cloud platform integrations (AWS, Azure, GCP)
  • ✓Davis AI anomaly detection
  • ✓Log ingestion (priced separately)

Log Management & Analytics

From $0.0001 per GiB scanned (Grail)

  • ✓Grail data lakehouse storage
  • ✓Index-free log ingestion
  • ✓DQL query language
  • ✓Long-term retention
  • ✓Davis AI on log data

Application Security

From $0.018/hour per 8 GiB host

  • ✓Runtime vulnerability analytics
  • ✓Real exploit-risk prioritization
  • ✓Runtime application protection
  • ✓Attack detection and blocking
  • ✓DevSecOps workflow automation

Digital Experience Monitoring

Priced per DEM unit (RUM + synthetic)

  • ✓Real user monitoring (RUM)
  • ✓Session replay
  • ✓Synthetic browser and HTTP monitors
  • ✓Mobile app monitoring
  • ✓Business analytics on user sessions
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Best Use Cases

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Large enterprises running complex hybrid or multi-cloud stacks (AWS + Azure + on-prem + mainframe) that need a single pane of glass with deterministic root-cause analysis across thousands of services

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Kubernetes-heavy organizations that want automatic discovery and dependency mapping of pods, services, and ingress without manually wiring up Prometheus exporters or sidecars

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Regulated industries (banking, insurance, healthcare, public sector) that require runtime application security, vulnerability prioritization, and audit-grade log retention via Grail

🚀

Site reliability engineering (SRE) teams using SLOs, error budgets, and automated remediation workflows through AutomationEngine to reduce manual toil and MTTR

💡

Digital experience and business observability use cases where product, marketing, and engineering teams need to correlate user sessions, conversion funnels, and revenue data with backend performance

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Migrations from legacy APM tools (AppDynamics, classic New Relic, CA APM) where the goal is consolidating multiple monitoring tools into one unified, AI-driven platform

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Dynatrace doesn't handle well:

  • ⚠Total cost of ownership is high relative to most observability tools in our directory — small teams and individual developers will find it economically impractical
  • ⚠Steep learning curve for the new Grail-based platform, including DQL, AppEngine, and AutomationEngine, requiring meaningful training investment
  • ⚠Custom dashboard and visualization flexibility is more limited than Grafana and other open-source-first tools, especially for advanced data exploration workflows
  • ⚠Self-hosted and air-gapped deployment options (Dynatrace Managed) are available but less commonly used and require dedicated infrastructure operations effort
  • ⚠Consumption-based pricing across multiple SKUs (hosts, GiB ingested, DEM units, queries) can make budgeting and forecasting difficult without careful governance

Pros & Cons

✓ Pros

  • ✓Davis AI provides deterministic, causal root-cause analysis rather than just statistical correlation, reducing alert noise and accelerating MTTR in complex distributed systems
  • ✓Single OneAgent deployment automatically discovers and instruments hosts, containers, services, and dependencies — eliminating most manual instrumentation work that competing tools require
  • ✓Grail data lakehouse stores logs, metrics, traces, and events without indexing, enabling fast DQL queries across petabyte-scale data without pre-aggregation trade-offs
  • ✓Unified platform consolidates APM, infrastructure, logs, RUM, synthetic, and runtime security — reducing the need to license and integrate multiple separate tools
  • ✓Strong support for hybrid and multi-cloud environments including AWS, Azure, GCP, Kubernetes, OpenShift, SAP, and mainframe — making it well-suited to large enterprises with heterogeneous stacks
  • ✓Publicly traded company (NYSE:DT) with 20+ years of operating history and enterprise-grade SLAs, security certifications, and 24/7 support phone lines (+1-844-900-3962 for technical support)

✗ Cons

  • ✗Pricing is widely regarded as among the highest in the observability category, with consumption-based costs that can become unpredictable as data volumes scale
  • ✗Steep learning curve — DQL, Grail, AutomationEngine, and the new app-based platform require significant onboarding investment compared to simpler dashboarding tools
  • ✗Dashboarding and visualization customization is less flexible than open-source-friendly alternatives like Grafana, with users sometimes constrained to Dynatrace's UI conventions
  • ✗Smaller teams and startups often find the platform overkill for their needs and difficult to justify versus lighter-weight SaaS APM tools
  • ✗Migration from the classic Dynatrace experience to the new Grail-based platform has introduced friction for long-time customers retraining on new query languages and apps

Frequently Asked Questions

How does Dynatrace's pricing work and is it really enterprise-only?+

Dynatrace uses consumption-based pricing across multiple SKUs, including Full-Stack Monitoring (starting around $0.04/hour per 8 GiB host), Infrastructure Monitoring (around $0.01/hour per host), Log Management & Analytics (priced per GiB ingested and queried), Application Security, and Digital Experience Monitoring. While there is a 15-day free trial and self-service signup, the platform's commercial model and feature depth are clearly aimed at mid-market and enterprise buyers. Smaller teams typically find the total cost of ownership higher than lighter SaaS APM alternatives, especially once log ingestion and retention are factored in.

What makes Davis AI different from AI features in other observability tools?+

Davis AI is Dynatrace's deterministic causal AI engine that uses the real-time Smartscape topology to trace cause-and-effect relationships rather than relying purely on statistical correlation. This means when an incident occurs, Davis identifies the actual root-cause component (a failing pod, a slow database, a third-party API) along with all impacted entities, instead of surfacing dozens of correlated alerts. Dynatrace has expanded Davis with predictive AI for capacity forecasting and a generative AI copilot (Davis CoPilot) that lets engineers ask natural-language questions and auto-generate DQL queries, dashboards, and workflows.

How does Dynatrace compare to Datadog and New Relic?+

Compared to the other major observability tools in our directory, Dynatrace is generally seen as the most automated and AI-driven of the three, with the strongest auto-discovery via OneAgent and the most mature causal root-cause analysis. Datadog typically wins on breadth of integrations (700+) and dashboard flexibility, while New Relic is often more cost-predictable thanks to its per-user pricing model. Dynatrace tends to be the preferred choice for large enterprises with complex Kubernetes, mainframe, or SAP environments where automation reduces operational toil, while Datadog and New Relic are often picked by faster-moving teams that prioritize developer ergonomics or budget predictability.

What environments and technologies does Dynatrace support?+

Dynatrace supports a very wide range of environments including AWS, Azure, Google Cloud, IBM Cloud, OpenShift, Kubernetes, VMware, on-premises servers, mainframes (z/OS), and SAP systems. The OneAgent supports major languages including Java, .NET, Node.js, Python, Go, PHP, and Ruby, and integrates with OpenTelemetry for vendor-neutral instrumentation. It also offers prebuilt content for hundreds of technologies through Dynatrace Hub, including databases, message queues, CI/CD platforms, and ITSM tools like ServiceNow, Jira, and PagerDuty.

Is Dynatrace a good fit for application security and DevSecOps?+

Yes — Dynatrace Application Security adds runtime vulnerability analytics, runtime application protection, and attack detection on top of the same OneAgent used for observability. Because it observes applications at runtime, it can prioritize CVEs based on whether the vulnerable library is actually loaded and exposed to the public internet, dramatically reducing false positives compared to static SAST tools. Combined with AutomationEngine workflows, security teams can automate remediation tasks like ticket creation, deployment blocks, or runtime mitigations, making Dynatrace a credible DevSecOps platform alongside its observability function.
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What's New in 2026

Dynatrace continues to expand its AI capabilities with Davis CoPilot, a generative AI assistant that lets users query data, build dashboards, and create AutomationEngine workflows in natural language. The platform's homepage messaging in 2026 centers on 'Observability built for the age of AI,' emphasizing observability for AI-driven applications and LLM workloads, as well as deeper integration of causal, predictive, and generative AI under the unified Davis brand. The company has also continued migrating customers from the classic experience to the Grail-based platform with new apps, AppEngine for custom apps, and expanded business observability use cases.

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Datadog

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Quick Info

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Website

www.dynatrace.com/
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