Application Performance Monitoring
Fast & Cost-Effective APM with AI-based sampling. Runs On-Prem with no traces data sent out of your cloud.
Easy log management with fast search & data privacy at the core. No log data leaves your cloud. Ever.
Track hosts, databases, containers, and cloud services in real time — with unlimited retention and full data control.
Track every user journey in real time. Get instant visibility into performance bottlenecks with 4X faster, on-prem RUM.
Prevent downtime before it happens with proactive, scripted tests. Simulate user journeys and detect issues before customers are impacted.
Catch every error in real time. Aggregate, analyze, and resolve issues 4× faster with CubeAPM.
Track Kubernetes clusters, nodes, and pods with real-time performance insights
Application Performance Monitoring
Fast & Cost-Effective APM with AI-based sampling. Runs On-Prem with no traces data sent out of your cloud.
Easy log management with fast search & data privacy at the core. No log data leaves your cloud. Ever.
Track hosts, databases, containers, and cloud services in real time — with unlimited retention and full data control.
Track every user journey in real time. Get instant visibility into performance bottlenecks with 4X faster, on-prem RUM.
Prevent downtime before it happens with proactive, scripted tests. Simulate user journeys and detect issues before customers are impacted.
Catch every error in real time. Aggregate, analyze, and resolve issues 4× faster with CubeAPM.
Track Kubernetes clusters, nodes, and pods with real-time performance insights
Dynatrace Pricing Calculator
Estimate your Dynatrace bill —
Enter your hosts, data volume, and usage to get an instant monthly estimate — and see how it compares to CubeAPM's flat-rate pricing.
Configure your Dynatrace usage
Dynatrace cost breakdown
| Line item | Dynatrace | CubeAPM | Note |
|---|---|---|---|
| Total / month | $0 | $0 |
How Dynatrace compares to CubeAPM
What's included with CubeAPM
See how much you'd save with CubeAPM
No per-host fees. No data-out charges. Flat per-GB rate with APM, logs, and infrastructure all included — running inside your own cloud account.
Frequently asked questions about Dynatrace pricing
Pricing last verified March 2026 from Dynatrace's public rate card. Enterprise contracts may differ. This calculator provides estimates only.
To give feedback or report any discrepancy, reach out to [email protected]
Model your Dynatrace spend using the calculator
This calculator helps you translate real usage assumptions into estimated Dynatrace costs. By adjusting inputs such as infrastructure scale, telemetry volume, and feature usage, teams can see how pricing evolves across different growth scenarios.
It is especially useful for:
- Planning observability budgets before scaling production workloads
- Evaluating the cost impact of Kubernetes and microservices adoption
- Supporting FinOps reviews and tooling comparisons
- Avoiding cost surprises after expanding monitoring coverage
Why Dynatrace Pricing Becomes Complex at Scale
Dynatrace’s pricing model is transparent at the unit level, but complexity increases as environments grow because costs are distributed across multiple independent usage dimensions.
Unlike flat host-based or pure ingestion-based models, Dynatrace pricing is calculated across:
- Memory-GiB hours (Full-Stack monitoring)
- Host hours (Infrastructure monitoring)
- Pod hours (Kubernetes monitoring)
- Log ingestion volume (per GiB)
- Log retention (per GiB-day)
- Log query scan volume (per GiB scanned)
- RUM sessions (per session)
- Synthetic checks (per action)
At a small scale, these units are easy to estimate. At enterprise scale, multiple dimensions grow simultaneously.
Here are the reasons why Dynatrace’s pricing becomes complex at scale:
1. Multiple Billing Metrics Grow at the Same Time
Unlike simple per-host or per-GB models, Dynatrace breaks pricing into separate billable units, each with its own published rate:
Pricing Unit | Billing Metric | List Price |
Full-Stack Monitoring | per memory-GiB-hour | $0.01 |
Infrastructure Monitoring | per host-hour | $0.04 |
Kubernetes Platform Monitoring | per pod-hour | $0.002 |
Log Ingest & Process | per GiB ingested | $0.20 |
Log Retention | per GiB-day | $0.0007 |
Log Query Scan | per GiB scanned | $0.0035 |
RUM (Real User Monitoring) | per session | $0.00225 |
RUM with Session Replay | per session | $0.0045 |
Synthetic Monitoring | per action | $0.0045 |
As organizations scale:
- More hosts: higher memory-GiB hours
- More containers: higher pod hours
- More traffic: higher RUM session counts
- More services: more logs
- More investigations: more log scans
Each of these units can contribute separately to your monthly bill.
2. Monitoring Infrastructure and Memory Together Drives Compound Growth
Full-Stack vs Infrastructure Monitoring:
Full-Stack monitoring is priced at $0.01 per GiB-hour of memory. This means:
- Larger instance sizes cost more
- Autoscaling increases monitored memory hours
- Temporary scaling during traffic spikes increases billable units
If a team doubles memory allocation for performance reasons, observability cost doubles proportionally for those resources. This can make budgeting more complex in cloud-native environments where infrastructure changes dynamically.
Example: A server with 32 GiB memory running continuously for 30 days:
- 32 × 24 × 30 = 23,040 memory-GiB-hours
- 23,040 × $0.01 = $230.40/month
If you have 50 such hosts:
- 50 × $230.40 = $11,520/month
Infrastructure monitoring alone, at $0.04 per host-hour, is cheaper but not equivalent.
Example:
- 50 hosts × 720 host-hours × $0.04
- 50 × 720 × $0.04 = $1,440/month
Full-Stack adds depth (traces, code-level insight, distributed tracing) but multiplies cost when host memory size is large or when many hosts are monitored.
3. Log Costs Have Three Independent Variables
Dynatrace log pricing has three separate charges:
- Ingest cost (per GiB)
- Retention cost (per GiB-day)
- Query scan cost (per GiB scanned)
At scale, log volume is usually the fastest-growing telemetry signal. As teams increase:
- Log verbosity
- Retention duration
- Investigative querying
Costs can shift significantly month to month. Choosing the alternative “retain with included queries” model changes the cost structure again, introducing a different retention multiplier.
Example Calculation
Log Ingestion:
- $0.20 per GiB ingested
- If you ingest 5 TB/month:
- 5,000 GiB × $0.20 = $1,000/month
Log Retention:
- $0.0007 per GiB-day
- If 5 TB are retained for 30 days:
- 5,000 × 30 = 150,000 GiB-days
- 150,000 × $0.0007 = $105/month
Log Query Scan:
- $0.0035 per GiB scanned
- If you scan 1 TB of log data in ad-hoc queries:
- 1,000 GiB × $0.0035 = $3.50
These costs can vary independently, even if ingest stays the same, heavy querying or longer retention will increase bills.
4. Digital Experience Pricing Scales with Traffic
User experience monitoring costs scale with the number of sessions:
Metric | Price |
RUM sessions | $0.00225 per session |
RUM + Session Replay | $0.0045 per session |
Synthetic checks | $0.0045 per check |
High-growth SaaS platforms or consumer applications can see:
- Millions of sessions per month
- Seasonal traffic spikes
- Marketing-driven traffic surges
These directly translate into session-based billing increases. Unlike infrastructure monitoring, this cost scales with user adoption, not infrastructure footprint.
Example:
- 2 million RUM sessions/month:
- 2,000,000 × $0.00225 = $4,500
- 2 million Session Replay sessions:
- 2,000,000 × $0.0045 = $9,000
Digital experience costs can rival infrastructure costs in high-traffic environments.
5. Annual Commitment Adds Another Planning Layer
Dynatrace pricing is based on an annual platform subscription commitment:
- You agree to a total annual spend
- All usage draws from this commitment
- If usage exceeds the commitment, you pay the same published rate
- Volume discounts may reduce effective per-unit costs
This ensures no surprise “overage rate hikes,” but it also requires accurate forecasting across multiple billable dimensions. A misestimate of memory, logs, or sessions can deplete your commitment early in the year.
6. Cloud-Native Elasticity Increases Variability
Because many cloud environments are elastic (auto-scaling, burst traffic, ephemeral workloads), usage in one month can differ significantly from the next across:
- Memory-GiB hours
- Pod hours
- Sessions
- Log volume
For example, a traffic spike:
- Doubles RUM sessions: doubles RUM cost
- Adds log churn: increases log ingest
- Drives extra alerts or deeper investigation: more log queries
Cost drivers can cascade.
7. Kubernetes Monitoring Adds a Parallel Cost Dimension
In cloud-native environments, pod counts fluctuate frequently due to autoscaling, deployments, and short-lived workloads. Dynatrace charges for Kubernetes monitoring based on pod-hours, meaning every running pod contributes directly to cost.
Pricing: $0.002 per pod-hour
Example: 500 pods running continuously:
500 × 24 × 30
= 360,000 pod-hours
360,000 × $0.002
= $720/month
If scaling temporarily increases pods to 800 for half the month, the monthly cost rises to:
468,000 pod-hours × $0.002
= $936/month
Even temporary scaling events increase observable cost.
8. Forecasting Requires Modeling Multiple Cost Drivers
Dynatrace pricing scales across independent metrics: memory-GiB hours, log ingestion, RUM sessions, and pod-hours. As systems grow, these expand simultaneously.
Example environment:
- 100 Full-Stack hosts (32 GiB each)
- 10 TB logs/month
- 2 million RUM sessions
- 500 pods
Monthly Cost Breakdown
- Full-Stack Monitoring
100 × (32 × 24 × 30 × $0.01)
= $23,040 - Log Ingestion
10,000 GiB × $0.20
= $2,000 - RUM
2,000,000 × $0.00225
= $4,500 - Kubernetes
360,000 pod-hours × $0.002
= $720
Estimated baseline: ~$30,260/month
The complexity comes from the fact that each metric scales independently; infrastructure growth, traffic growth, and deployment growth all increase cost along different axes.
How to Understand and Optimize Dynatrace Costs
Dynatrace pricing is straightforward in structure but demanding in practice. You are essentially paying for usage. Memory hours, host hours, pod hours, log ingestion, retention, and user sessions all contribute separately to your total spend.
If costs feel high, the issue is usage behavior, rarely pricing.
1. Think in Resources, Not Features
Dynatrace charges you for what that feature consumes. When you increase host memory, add pods, expand traffic, or retain more logs, your bill grows because your system grows. That is predictable.
Before optimizing cost, identify which resource dimension is expanding. Is it:
- Memory?
- Logs?
- Sessions?
- Pods?
Each one has a different driver and requires a different solution.
2. Be Intentional About Monitoring Depth
Full-stack monitoring is valuable, but not everything needs deep instrumentation. In mature environments:
- Critical services get full-stack visibility.
- Low-risk internal services may run on infrastructure monitoring.
- Non-production environments are instrumented selectively.
The mistake I see most often is defaulting to maximum depth everywhere. That is not observability maturity. That is lack of governance.
3. Treat Logging as an Engineering Discipline
Log costs grow quietly. Over time teams add debug statements, duplicate attributes, and long retention periods. No single change is dramatic. The accumulation is. Controlling log cost means:
- Reviewing high-volume services regularly.
- Removing unnecessary verbosity.
- Setting retention based on operational need, not habit.
4. Understand the Impact of Elastic Infrastructure
Cloud-native systems scale up and down constantly. When pods double during peak hours, monitoring usage doubles as well. That is how usage-based pricing works.
The optimization lever is not reducing visibility. It is validating scaling policies and avoiding over-provisioned memory or unnecessary burst capacity. Monitoring should scale with real demand, not configuration drift.
5. Plan Commitments Based on Real Growth
Dynatrace operates on an annual commitment model. That commitment should reflect realistic expectations.
If your traffic is growing 30% per year, your observability footprint likely is too. Forecast memory growth, log growth, and session growth before committing.
Underestimating leads to budget pressure later. Overestimating locks in unnecessary spend. Treat commitment planning as part of capacity planning.
6. Review Usage Regularly
Optimization is not a quarterly panic exercise. Look at:
- Which workloads consume the most memory hours?
- Which services generate the most logs?
- Which applications drive the most sessions?
When teams look at usage consistently, cost remains predictable. When they ignore it, it creeps.
Dynatrace pricing mirrors your system. If your architecture is disciplined and intentional, your costs will be too. If monitoring expands without ownership, spend will follow. And that’s an observability governance problem.
How CubeAPM approaches pricing differently
CubeAPM is built for teams that need predictable observability costs as systems scale. CubeAPM simplifies pricing around data volume and gives teams direct control over how telemetry is collected, sampled, and retained. It doesn’t not users based on the number of seats, features, or longer retention periods.
Transparent, data-volume-based pricing
The pricing ($0.15/GB) is tied directly to the amount of telemetry you ingest, without hidden multipliers, bundled feature units, or per-user licensing.
Intelligent sampling to reduce low-value data
CubeAPM prioritizes high-signal telemetry such as errors, high-latency requests, and anomalies while aggressively sampling repetitive, low-value traffic to optimize cost without losing diagnostic depth.
Full control over ingestion and unlimited retention
Teams decide what data to keep, how long to retain it, and where it is stored, instead of relying on fixed SaaS tiers or opaque limits.
Self-hosted/BYOC deployment
Observability data stays within your infrastructure or cloud account, avoiding vendor-imposed throttling and enabling more predictable cost behavior during traffic spikes or incidents.
Cost predictability at scale
By combining controlled ingestion with targeted sampling, CubeAPM helps teams forecast observability more accurately as workloads, traffic, and telemetry volumes grow.
Dynatrace vs CubeAPM Cost Comparison at Scale
*All pricing comparisons are calculated using standardized Small/Medium/Large team profiles defined in our internal benchmarking sheet, based on fixed log, metrics, trace, and retention assumptions. Actual pricing may vary by usage, region, and plan structure. Please confirm current pricing with each vendor.
| *Approx. cost for teams (size) | Dynatrace | CubeAPM |
|---|---|---|
| Small (~40) | $7,740 | $2,080 |
| Mid-Sized (~125) | $21,850 | $7,200 |
| Large (~250) | $46,000 | $15,200 |
Check out Dynatrace alternatives based on pricing, features, deployment, and more.