Dynatrace
One observability platform for applications, infrastructure, Kubernetes, logs, security, and AI workloads.
About Dynatrace
Dynatrace brings application, infrastructure, log, digital experience, security, and AI workload data into one observability platform. Engineers can use APM, distributed tracing, profiling, topology, infrastructure monitoring, log analytics, and automated root-cause analysis without moving an incident through several disconnected tools. Full-Stack Monitoring includes Kubernetes platform monitoring and OpenTelemetry metrics and traces. AI Observability follows applications, orchestration frameworks, models, vector databases, and compute resources. The Dynatrace Platform Subscription charges by usage. Published entry rates cover basic host discovery, infrastructure and full-stack monitoring, Kubernetes pods, and live code monitoring. Budgeting still takes work because hosts, memory, pods, containers, retention, and optional capabilities use different cost drivers. Dynatrace makes the most sense for organizations with complex cloud or hybrid estates and teams ready to manage a broad observability system. A smaller environment may find both the scope and the pricing model heavier than a focused monitoring service.
Best for
Best use cases
Investigating a distributed incident across services, hosts, traces, and logs
Monitoring Kubernetes workload health while retaining application-level context
Tracking AI model latency, token cost, invocation errors, and compute utilization
Compare before choosing
Broader operational coverage than a focused host-monitoring service, with more setup scope
Usage-specific billing offers precision but requires more forecasting than a flat seat price
Built-in topology and root-cause analysis can reduce tool switching during incidents
Features
Application troubleshooting
Trace transactions across applications and infrastructure, profile code, and use automated root-cause analysis during incidents.
Cloud infrastructure monitoring
Track host, process, disk, memory, network, Kubernetes, and topology signals across cloud and data-center environments.
AI workload observability
Measure model latency, token usage, cost, errors, orchestration behavior, vector databases, and accelerator utilization.
Pros
- Correlated application, infrastructure, log, and topology data reduces tool switching during incident response.
- Full-Stack Monitoring includes APM, profiling, Kubernetes visibility, and OpenTelemetry metrics and traces.
- Published hourly units and monthly estimates make individual workload rates visible before purchase.
Cons
- Different billing units for hosts, memory, pods, and containers make total-cost forecasting more involved.
- Teams with a small environment may pay for and administer more platform breadth than they need.
- Longer trace retention and adjacent capabilities can add cost beyond the listed entry monitoring rates.
Pricing Plans
Foundation & Discovery
- Basic host health indicators
- Host and process topology detection
- Filesystem and OS service monitoring
- Logs collected and correlated in context
Infrastructure Monitoring
- Everything in Foundation & Discovery
- Process, disk, memory, and network analysis
- Custom metrics for extensibility
Full-Stack Monitoring
- Everything in Infrastructure Monitoring
- APM and automated root-cause analysis
- Code-level profiling
- Kubernetes Platform Monitoring
- OpenTelemetry metrics and traces
- 10 days of trace retention, extendable to 10 years
Kubernetes Platform Monitoring
- Kubernetes metrics and events
- Health views and contextual analysis
- Resources and topology
- Included with Full-Stack Monitoring hosts
Code Monitoring
- Live production troubleshooting and debugging
- Non-breaking breakpoints
- Visual Studio Code and JetBrains integration
* Pricing information is for reference only. Please refer to the latest announcement on Dynatrace's official website.