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
Datadog Pricing Calculator
Datadog Pricing Calculator
Input your infrastructure, log volume, and APM usage to calculate your estimated monthly Datadog bill.
1. AWS integration: Enabling CloudWatch pulls in every AWS metric by default — thousands of custom metrics you never explicitly enabled.
2. OpenTelemetry: All OTel metrics count as custom metrics in Datadog, including standard OTel semantic conventions.
3. High-cardinality tags: Tagging a metric by user ID (1M users) = 1M custom metrics from one metric name. Avoid unbounded tag values.
| Cost component | Datadog | CubeAPM | Why |
|---|---|---|---|
| Infrastructure Monitoring › | $0 | $0 | |
| Infra Hosts & Containers | $0 | $0 | |
| Database Monitoring | $0 | $0 | |
| Custom Metrics | $0 | unlimited | |
| APM | $0 | $0 | |
| Log Management › | $0 | $0 | |
| Log Ingestion | $0 | $0 | |
| Log Indexing | $0 | $0 | |
| RUM | $0 | — | |
| Synthetic Monitoring | $0 | — | |
| Cloud Platform Cost › | $0 | ~$0 | |
| Data Egress | $0 | $0 | |
| Cloud Infra (self-hosted) | — | ~$0 | |
| Total / Month | $0 | $0 | |
| — |
Runs entirely inside your own AWS / GCP / Azure, or physical infrastructure — your data never leaves your environment. No per-host fees, no egress costs, no separate APM license.
See how much you'd save with CubeAPM
No per-host fees. No data-out charges. Flat per-GB rate with APM, logs, and metrics all included — running inside your own cloud account.
Datadog pricing sources
- Infrastructure Pro: $18/host/mo PAYG ($15 annual); Enterprise: $27/$23 · datadoghq.com/pricing
- APM: $36/host/mo PAYG ($31 annual) — includes 150 GB ingested spans + 1M indexed spans per host
- Log Ingestion: $0.1/GB; Log Indexing: $1.7/M events at 15-day retention
- Custom Metrics: $0.05/metric/mo overage; first 100/Pro host included free
- Synthetics: API $5/10K runs; Browser $12/1K runs
- Database Monitoring: $84/db host/mo PAYG ($70 annual)
- Host billing: 99th-percentile high-watermark of hourly host counts per month
Cloud & conversion assumptions
- Cloud data-out: ~$0.1/GB internet egress (AWS/GCP/Azure list rate)
- Log events per GB: 1,000,000 default (dropdown above) — varies by app verbosity
- CubeAPM infra: ~$0.02/GB estimated compute + storage paid to your cloud provider
- APM trace volume: ~45 GB/host/month (empirical real-world average; Uptrace 2026 benchmark: 40–50 GB/host across all team sizes)
Common billing surprises (not calculated here)
- AWS CloudWatch integration: Enabling this pulls in every CloudWatch metric by default — can add thousands of custom metrics automatically
- OpenTelemetry: All OTel metrics count as Datadog custom metrics, including standard semantic conventions
- Agent misconfiguration: Agent deployed per container (not per node) bills each container as a separate host — can 10× your bill on a K8s cluster
- Dev/staging environments: Billed at full rate; no free tier for non-production hosts
- Log Rehydration: Pulling archived logs back costs ~$1.27/million events — unexpected for compliance teams
- Fargate task pricing: Infrastructure Fargate = $1.00/task/mo PAYG; APM Fargate = $2.90/task/mo PAYG (separate from host billing)
- Serverless Lambda: APM for Lambda = $5/1M invocations PAYG
Not included in this estimate
- Volume discounts or enterprise negotiated rates (can significantly reduce Datadog costs)
- Cloud Network Monitoring, Cloud Cost Management, CI Visibility
- Security products: Cloud SIEM, CSM Pro/Enterprise, App & API Protection
- Log Rehydration ($1.27/M re-hydrated events)
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Ready to cut this bill?
CubeAPM is a full-stack observability platform that runs inside your own cloud account.
No per-host fees. No separate APM license. No egress charges. One flat rate per GB — logs, traces,
and metrics — with unlimited users and alerting included.
Based on this estimate, switching could save your team significantly per year.
|
Book a Cost Teardown
Our engineers will review your actual Datadog invoice, identify every avoidable cost, and show you a
like-for-like CubeAPM setup — live, on a 30-minute call.
cubeapm.com
|
To give feedback or report any discrepancy, reach out to [email protected]
Why use this Datadog pricing calculator?
Datadog pricing scales across multiple dimensions at the same time. Small configuration changes such as increasing log verbosity, enabling deeper tracing, adding Kubernetes workloads, or extending retention can significantly affect monthly spend.
This calculator helps you:
- Estimate Datadog costs before committing to a plan or scaling workloads
- Identify which usage factors drive spend the most (hosts, logs, APM, containers, or RUM)
- Model scenarios for small, mid-sized, and large teams
- Compare Datadog’s usage-based pricing with more predictable alternatives using the same assumptions
The goal is not to predict an exact bill, but to provide a realistic cost range based on how Datadog pricing works in practice.
Why Datadog's pricing becomes complex at scale
Datadog’s pricing is not based on a single consumption unit. Instead, costs accumulate across multiple products, host types, usage limits, and overage rules, which makes budget forecasting increasingly difficult as environments grow.
Multiple billing units across products:
Datadog charges separately for different capabilities- infrastructure monitoring, logs, APM, custom metrics, digital experience monitoring, database and containers, and serverless workloads.
Each product has its own billing units, default limits, and overages. As teams enable more features over time, costs compound across independent pricing dimensions, rather than scaling predictably with usage.
Coupled APM and Infra pricing
Datadog pricing introduces implicit coupling between products. APM hosts must also be licensed as Infrastructure Monitoring hosts; this increases baseline costs.
For example, if you subscribe to 10 APM hosts, those same 10 hosts must also be subscribed under Pro or Enterprise Infrastructure Monitoring. This means APM cannot be purchased in isolation, and infrastructure costs scale automatically alongside APM adoption, even if infrastructure metrics are already covered elsewhere.
Short search window
Ingested traces are available for live search and analytics for only 15 minutes by default. Beyond that, teams need to rely on indexing the spans. Although per APM host 1 million indexed spans are included with 15-day retention, to index more spans or to retain those indexed spans beyond 15 days, there are significant overage charges.
APM ingestion limits
APM pricing includes per-host limits on span ingestion and retention.
- Span ingestion: 150 GB per APM host per month by default
- Span retention: 1 million indexed spans per APM host per month
Once these limits are exceeded, additional span ingestion is billed separately, at $0.10 per GB per month and span retention at $2.55 per million spans per month with 15-day retention.
In high-throughput or microservices-heavy systems, these limits are often reached quickly with service interactions, making APM costs sensitive to traffic patterns and instrumentation depth.
Hourly metering of on-demand APM
Datadog charges on-demand APM usage hourly, including both ingested and indexed span. The usage is not averaged across the month. This means even short spikes in traffic can push usage above hourly limits and trigger overage charges.
Overage due to custom metric growth
Custom metrics frequently grow due to high-cardinality tags, Kubernetes labels and dynamic dimensions, application-level metrics added incrementally over time. This gradual growth distributed across teams and custom metrics drive cost rapidly.
But Datadog has a default allowance for custom metrics per host (100-200 per host allocated). Beyond included allotments for spans and custom metrics, overages are billed per unit (e.g., per million indexed spans, per 100 custom metrics), adding unpredictability.
Separate charges for log ingestion, indexing, and forwarding
Datadog’s Log Management pricing introduces multiple independent pricing units:
- Ingestion: $0.10/GB ingested or scanned per month
- Standard indexing: $1.70/million log events per month ($2.55/million on-demand) with 15-days retention.
- Log forwarding: $0.25/GB outbound per destination per month
Using different pricing units for ingestion, indexing, retention, and forwarding makes accurate forecasting difficult without detailed modeling of application behavior and retention requirements.
Complex digital experience monitoring billing
Datadog’s Digital Experience Monitoring, including Real User Monitoring (RUM), Synthetics, and Error Tracking are billed independently from core telemetry products like hosts, logs, and APM, using distinct usage-based units that scale with user traffic and test volume.
- RUM: $0.15 per 1,000 sessions per month, and sessions replay at $2.5 per 1000 sessions per month
- Synthetic Monitoring: $5 per 10,000 API test runs, $12 per 1,000 browser test runs per month, and $50 per 100 test runs for mobile app testing.
- Error tracking: $25/month, $36 on-demand for 50K errors
As session counts grow and more frequent or geographically distributed tests are added, these costs can scale quickly and unpredictably, especially for high-traffic applications or comprehensive uptime coverage.
Expensive premium support
For organizations that require stronger SLAs, Datadog’s Premier Support is priced at 8% of monthly Datadog spend, with a minimum fee of $2,000 per month.
Because Premier Support scales as a percentage of total usage, support costs increase automatically as telemetry volume and product adoption grow. In larger environments, support becomes a variable cost tied directly to platform spend rather than a predictable fixed expense.
Why this matters for cost estimation
Because each of these limits and pricing units applies independently, Datadog costs often increase from multiple directions at once:
- More traffic increases log and trace volume.
- More services increase host, container, and APM usage
- Longer retention increases indexing and storage costs
- Additional features activate new pricing tiers
This is why Datadog pricing can feel predictable at small scale, but increasingly difficult to forecast as observability coverage expands.
The calculator above is designed to surface these cost drivers clearly, helping teams anticipate spend before costs become difficult to control.
How Datadog pricing really scales in production
Datadog pricing is usage-based, meaning costs scale with telemetry generation rather than infrastructure alone. In real-world environments, spend is typically driven by:
- Log ingestion volume per day
- Container count and churn in Kubernetes
- Trace volume and indexing decisions
- Retention duration
- Custom metrics and tag cardinality
- Additional products enabled over time
Many teams experience a widening gap between expected spend and actual invoices once observability expands beyond initial use cases.
What typically causes Datadog bills to spike
Most unexpected Datadog cost increases come from a small set of recurring patterns:
- Unfiltered or verbose log ingestion
- High container churn in Kubernetes
- Excessive custom metrics with high tag cardinality
- Long retention without archiving
- Autoscaling without observability usage caps
This calculator helps teams identify these patterns early and plan accordingly.
How teams manage Datadog costs
Engineering and FinOps teams usually manage Datadog spend by tightening controls around how telemetry is generated, ingested, and retained. Common approaches include:
- Filtering and sampling logs before ingestion to reduce volume while preserving signal
- Reducing unnecessary tags in custom metrics to avoid high-cardinality cost growth
- Limiting the scope of container monitoring, especially for short-lived or non-critical workloads
- Shortening log retention periods once incident analysis or compliance reviews are complete
- Forecasting costs before enabling new Datadog features, such as RUM, synthetics, or additional APM services
These measures help teams keep observability costs aligned with actual operational needs as systems scale.
How to Reduce Your Datadog Bill
Reducing a Datadog bill usually means controlling how telemetry is ingested, indexed, and retained. Most teams don’t reduce costs by reducing visibility but by focusing observability on high-value signals. You can use the pricing calculator above to see how each adjustment affects your estimated monthly cost.
Filter and sample logs before ingestion
Logs are often the largest Datadog cost driver. Teams commonly:
- Don’t ship verbose or debug logs in production
- Avoid sending logs in production to Datadog
- Sample down non-critical logs
- Filter logs at the agent or pipeline level
- Retain only a small part of logs for standard indexing.
- Keep shorter retention periods. If you need to retain logs for longer duration for compliance or other such purpose which might need little or no querying, then better to forward logs to S3 Archives at no cost and retain logs there for longer duration.
Try this in the calculator:
Reduce daily log ingestion (GB/day) by 10–30% and observe how ingestion and indexing costs change.
Control APM trace ingestion and indexing
APM costs increase when trace volume grows unchecked. Teams reduce spend by:
- Lowering sampling for low-value endpoints
- Indexing only critical spans and error paths
- Avoiding excessive instrumentation on high-throughput operations
Try this in the calculator:
Lower trace ingestion volume or indexed spans and see how quickly APM overage costs decline.
Reduce custom metric cardinality
Custom metrics especially in Kubernetes environments can grow quietly due to tags and labels. Teams often:
- Remove high-cardinality tags
- Limit custom metrics to reliability-critical signals
- Audit metrics generated by auto-instrumentation
Try this in the calculator:
Decrease the number of custom metrics per host and review the impact on infrastructure monitoring costs.
Scope Kubernetes and container monitoring
Short-lived or non-critical containers still count toward usage. Many teams reduce costs by monitoring only production namespaces or critical workloads.
Try this in the calculator:
Adjust the number of monitored containers or hosts to model how Kubernetes churn affects monthly spend.
Optimize retention before scaling
Retention choices directly affect indexing and storage costs. Common approaches include:
- Shortening retention for high-volume logs
- Archiving older data
- Retaining full fidelity only for security or compliance needs
Try this in the calculator:
Change retention assumptions and compare short-term savings versus long-term cost growth.
When cost optimization becomes ongoing work
While these steps help reduce Datadog bills, they require continuous tuning, audits, and engineering effort as systems evolve. At larger scale, some teams find that managing cost controls inside a usage-based SaaS model becomes an ongoing operational task.
This is often when teams start evaluating observability alternative platforms such as CubeAPM where cost boundaries are enforced structurally, rather than through constant optimization.
Datadog Pricing vs CubeAPM: A Structural Difference
The key difference between Datadog and CubeAPM is architecture, not features.
Datadog operates as a fully managed SaaS platform, where pricing scales with telemetry usage logs, traces, metrics, sessions, and retention. CubeAPM runs inside customer-controlled infrastructure (BYOC or on-prem), giving teams direct control over data flow, retention, and cost boundaries.
For a detailed breakdown of features and pricing, see CubeAPM vs Datadog.
This leads to fundamental differences:
| Area | Datadog | CubeAPM |
|---|---|---|
| Pricing Model | Hosts + ingestion + retention based | Ingestion-based only (no host, user, or retention fees) |
| Cost predictability | Varies with usage | Stable at scale |
| Log retention/ indexing | Paid, vendor-controlled | No retention fees (infra cost only) |
| Sampling flexibility | Limited | High (95%+ effective sampling) |
| Data residency | SaaS | On-prem/customer-controlled |
| Support | Free + Paid | Free |
How CubeAPM changes the cost equation
With CubeAPM, observability costs are driven by:
- The infrastructure you provision
- Your storage and retention choices
- Your sampling strategy
Cost growth is intentional and visible, aligned with infrastructure planning rather than opaque usage-based billing. For teams operating at scale, this makes observability spend easier to forecast and justify.
Datadog remains a strong fit for teams that prefer a fully managed SaaS experience and are comfortable with usage-based pricing in exchange for operational simplicity.