Datadog Pricing: All your Questions Answered

When it comes to monitoring and observability tools, Datadog is often one of the first names that comes to mind.

But while Datadog’s features are widely discussed, its pricing often remains a topic of confusion. How much does Datadog cost, and what factors influence your bill?

This guide breaks down Datadog pricing to help you better understand its structure, hidden nuances, and whether it’s the right fit for your needs.

What Is Datadog?

Datadog is a cloud-based monitoring platform designed to provide comprehensive observability for your applications, infrastructure, and logs. It’s widely used by companies operating in cloud-native environments, especially those with distributed systems. 

Datadog offers a range of features such as performance monitoring, security monitoring, and application profiling to keep everything running smoothly across AWS, Azure, or serverless environments. 

With robust dashboards, alerts, and integrations with hundreds of third-party tools, Datadog makes it easy to keep track of all your metrics, log events, and application health in one place.

The Basics of Datadog Pricing

Datadog’s pricing model is modular and usage-based, offering flexible options depending on your needs.

The platform provides several product suites, each with its pricing structure designed to support a wide variety of use cases, including AWS, Azure, and database monitoring.

Here’s a breakdown of its primary categories:

1. Infrastructure Monitoring

Cost: Starts at $15 per host per month (billed annually) or $18 monthly.

Details: This plan covers basic infrastructure monitoring, including metrics, dashboards, alerts, and on-demand updates for servers, databases, and more. Perfect for teams using AWS or Azure and looking to optimize performance in a cloud-native environment.

2. APM (Application Performance Monitoring)

Cost: $31 per host per month (annual) or $36 monthly.

Details: This plan is focused on application performance, tracking traces, and dependencies, and optimizing application health.

It’s ideal for teams using serverless architectures and looking to keep an eye on key performance indicators (KPIs). It also integrates seamlessly with cloud environments like AWS, Azure, and more.

3. Log Management

Cost: $0.10 per indexed log per GB and $1.27 per million ingested events.

Details: Datadog allows you to archive and index logs selectively, giving you control over your log volume and offering cost-saving options.

Whether you're tracking log events across your AWS or Azure environments, this plan helps manage large-scale log data with customizable retention periods at the end of the month.

4. Security Monitoring

Cost:

• Starts at $10 per host per month (Pro plan, billed annually)
• $25 per host per month (Enterprise plan, billed annually)

Details: Datadog's Security Monitoring plan includes runtime security features, threat detection capabilities, and proactive monitoring for cloud environments.

It helps protect against potential security risks, especially for teams operating in serverless systems or handling critical database monitoring.

5. Synthetic Monitoring

Cost:

• Starts at $12 per 1,000 browser test runs per month
• API tests: Starts at $5 per 10,000 test runs per month

Details: Datadog's Synthetic Monitoring helps proactively monitor application uptime and user journeys. It allows teams to simulate real user behavior across cloud environments such as AWS, Azure, or serverless applications. This plan helps optimize performance and troubleshoot issues before they impact customers.

Datadog’s Additional Costs You Should Know About

1. Data Retention Costs

Datadog's default data retention periods are:

• Metrics: 1 day
• Logs and Traces: 15 months (customizable)

Extending these retention periods beyond the standard duration incurs additional fees. Strategic planning of your data retention policies can help manage these costs effectively.

2. Custom Metrics

Datadog offers:

• 100 custom metrics per host for the Pro plan
• 200 custom metrics per host for the Enterprise plan

Beyond these allotments, Datadog charges as low as $1 per 100 custom metrics per month. To control costs, consider consolidating or rehydrating custom metrics whenever possible to reduce billable usage.

3. High-Cardinality Data

Using high-cardinality tags, such as unique user IDs or session IDs, can increase your Datadog bill due to how the platform calculates usage. These tags generate a large volume of data, which can lead to higher charges—especially for users on Pro or Enterprise plans with extensive host-based allocations.

Key Point

Efficient tagging strategies are critical for cost optimization, as each unique combination of a metric and its associated tags counts as a separate billable entity. To reduce costs, consider consolidating tags and limiting high-cardinality data where possible.

Datadog APM Pricing Breakdown

Here’s a breakdown of Datadog APM pricing to help you make more informed decisions when optimizing your costs:

Ingested Traces

• Pricing: Charges are based on the volume of traces ingested.
• Retention: Not all traces are stored long-term. Sampling is used to determine which traces are kept.

Indexed Spans

• Definition: Spans are individual components of a trace, such as database queries or HTTP requests.
• Pricing: Datadog bills separately for indexed spans, which are searchable for analytics.
• Cost: Indexed spans cost $1.70 per million spans per month.

Retention Periods

• Default Retention: Traces and spans are typically kept for 15 days.
• Extended Retention: Longer retention periods come with additional fees.

Add-Ons

• Advanced Features: Features like Continuous Profiler or database query monitoring can be added, but they increase costs.

Cost Factors for APM

• High Trace Volume: Applications with heavy traffic or complex architectures will generate more traces, which directly impacts costs.
• Span Indexing: Indexing more spans results in higher costs. It’s important to decide which spans matter for effective monitoring.
• Retention Needs: Retaining data longer increases storage costs. Carefully assess retention needs based on your analysis requirements.
• Add-Ons: Extra tools or integrations for advanced features can quickly be added to your bill.

How to Optimize APM Costs

• Selective Trace Retention: Use intelligent sampling to retain only key traces, such as error-prone or high-latency ones. This minimizes unnecessary storage costs.
• Index Only Valuable Spans: Prioritize indexing the most important spans that provide useful insights. Avoid indexing every span to reduce costs.
• Analyze Retention Requirements: Balance your retention needs with monitoring goals. Only keep data as long as it’s necessary for analysis.
• Monitor What Matters: Focus on the applications or components that impact your business the most.

Datadog Infrastructure Monitoring Pricing Breakdown

Here’s a breakdown of how Datadog charges for infrastructure monitoring, so you can make the most cost-effective decisions for your environment:

Per-Host Pricing

• Pricing: Datadog charges a monthly fee for each host monitored, such as servers, containers, or VMs.
• Dynamic Containers: For containers that scale dynamically, pricing is based on average hourly usage to account for fluctuating workloads.

Data Retention

• Default Retention: Metrics are stored for a default period (e.g., 15 months for custom metrics).
• Extended Retention: Keeping data for a longer period will incur additional fees.

Add-Ons

• Additional Features: Features like APM, custom metrics, and log analytics come with their charges, which can quickly add up.

Discount Tiers

• Volume Discounts: Larger deployments may qualify for volume discounts or custom pricing agreements, but smaller teams might not see the same benefits.

What Drives Infrastructure Monitoring Costs?

• Container Sprawl: As your containerized environment scales, hourly usage tracking can lead to unexpected cost increases.
• High-Cardinality Metrics: Detailed metrics across many dimensions (e.g., tags) can ramp up costs, especially when combined with add-ons like custom metrics.
• Granular Monitoring: More granular monitoring improves visibility but increases data collection frequency, which can drive up resource consumption and costs.

Optimizing Datadog Infrastructure Costs

• Monitor What Matters: Focus on the critical systems in your environment. Avoid cluttering your dashboards with unnecessary metrics that increase complexity and costs.
• Adjust Data Retention: Set shorter retention periods for non-essential metrics to cut down on storage costs.
• Use Tags Strategically: Be mindful of your tagging strategy to prevent generating excessive high-cardinality metrics that can lead to higher costs.
• Monitor Containers Intelligently: Instead of tracking every transient container, use sampling or aggregate metrics to reduce overhead.

Real User Monitoring Pricing Breakdown

Datadog’s Real User Monitoring (RUM) allows you to track actual user behavior on your applications in real-time, providing valuable insights into how users interact with your product. However, pricing is based on event volume.

Here's what you need to know:

How Datadog Prices RUM

• Volume-Based Pricing: Datadog charges based on the number of events processed, which include user interactions like page views, clicks, and errors during sessions. More traffic or highly interactive apps mean more events, raising your costs.
• Retention Costs: Events are typically retained for a default period (e.g., 15 days). If you want to store data for longer, you’ll incur additional charges.
• Add-Ons: Advanced features, such as session replay or in-depth analytics, can be added to your bill.

Factors That Affect RUM Costs

• Application Traffic: More users equate to more events to track, which leads to higher costs.
• Interactivity: Highly dynamic applications, such as single-page apps (SPAs) or apps with complex user interfaces, generate more events, leading to higher costs.
• Retention Periods: Retaining data for longer periods increases storage fees.

Tips to Optimize RUM Costs

• Sample Wisely: Focus on capturing only relevant user sessions through smart sampling. This ensures you're tracking high-priority workflows, geographies, or user segments.
• Track High-Value Metrics: Instead of tracking every single interaction, zero in on critical metrics, such as key conversion funnels or performance bottlenecks.
• Set Retention Limits: For non-essential data, set shorter retention periods to avoid unnecessary storage charges.

Is Datadog RUM Right for You?

Datadog RUM is great for teams with substantial observability budgets that need detailed insights into user behavior.

However, if you're a smaller team focused more on reliability engineering than user analytics, platforms like Last9 might be a better fit, offering more predictable costs and tailored solutions.

Synthetic Monitoring Pricing Breakdown

Synthetic monitoring allows you to simulate virtual users interacting with your application, ensuring smooth operations. However, these tests can add up quickly. Understanding Datadog's pricing model can help you balance coverage with costs.

How Datadog Prices Synthetic Monitoring

• API Tests: Charged per execution. Running high-frequency tests or monitoring many endpoints can quickly increase costs.
• Browser Tests: Charged per test step. A simulated user journey (e.g., logging in, searching, making a purchase) incurs a charge for each action performed in sequence. These tests are generally more expensive due to their complexity.
• Multi-Location Testing: Running tests from multiple geographic locations increases your coverage but also raises costs.

What Drives Costs Up?

• Test Frequency: Running tests more frequently (e.g., every minute) multiply your costs compared to running them less often.
• Test Complexity: Complex journeys with many steps, such as detailed e-commerce workflows, tend to be pricier.
• Geographic Redundancy: Testing the same workflow from multiple regions adds coverage but increases the bill.

Optimizing Synthetic Monitoring Costs

• Test Smarter, Not Harder: Focus on frequent tests for critical user flows like login or checkout. Run less important tests less often.
• Targeted Alerts: Set smart thresholds to avoid unnecessary testing, running tests only when needed.
• Regional Focus: Limit test locations to regions with the most traffic to optimize coverage and costs.
• Use Real User Monitoring (RUM): Use synthetic tests for key paths and rely on RUM for passive, real-time insights into user behavior.

How Datadog Costs Rise in Different Scenarios

Datadog's pricing model can be tricky to navigate, but these scenarios help illustrate how costs can rise in unexpected ways.

Metrics-Heavy Chaos:

Imagine a SaaS company where the dev team is a little too enthusiastic about custom metrics. They create thousands of them, driving up costs. Here's how it could happen:

• Too many custom metrics: Since Datadog charges per custom metric, unnecessary ones could spike the bill.
• Solution: If they implemented a metric naming convention and periodically cleaned up old metrics, it might help keep costs in check.
• Optimization: Perhaps using a tiered strategy could work—critical metrics go to Datadog, while non-essential ones are logged elsewhere.

Log Overload:

Picture a streaming platform that logs everything, from user activity to server health, resulting in a flood of data in Datadog. This could lead to unexpected costs:

• Excessive log ingestion: Sending all logs (including debug and staging logs) to Datadog could unnecessarily inflate costs.
• Solution: Filtering out low-priority logs, like debug logs from staging, and routing them to cheaper storage might save money.
• Optimization: Retaining only actionable logs in Datadog could reduce storage and ingestion costs significantly.

Dynamic Infrastructure:

Now, imagine a cloud-native startup using Kubernetes, with dynamic scaling during traffic spikes. Here's how costs might rise unexpectedly:

• Scaling leads to higher costs: Datadog charges per host or container, meaning costs could quickly increase during scaling events.
• Solution: Setting alerts when container counts exceed certain thresholds could help predict and manage costs.
• Optimization: By using Datadog’s Container Monitoring, they could analyze which containers truly need monitoring, helping balance scalability and affordability.

Hybrid Monitoring:

A large enterprise migrating from on-prem to the cloud could see its monitoring costs double. Here's how it could play out:

• Hybrid setup: Monitoring both cloud and on-prem systems with Datadog could result in doubled costs.
• Solution: They might focus Datadog’s attention on critical on-prem systems while using existing tools for the rest.
• Optimization: Once the migration is complete, transitioning fully to Datadog with optimized usage might simplify costs.

4 Easy Ways to Optimize Datadog Costs

1. Audit Your Monitors

Regularly review your monitors to ensure you're tracking only relevant metrics. This practice helps reduce unnecessary test runs and prevents overages, which can lead to increased Datadog agent usage and higher bills.

2. Utilize Tagging Wisely

While tags are valuable for filtering and organizing data, avoid overly granular tagging. Overuse of high-cardinality tags, such as unique user IDs, can create billable spikes.

A well-thought-out tagging strategy is essential for cost optimization, especially for enterprise plan users managing a larger number of hosts or data points.

3. Selective Log Indexing

Datadog's log management feature allows you to archive logs without indexing them, helping keep costs down. Consider indexing only critical logs while archiving the rest. This selective indexing ensures cost-effective storage without unnecessary data storage or indexing overhead.

4. Annual Commitment

Opting for annual billing can save you up to 20% compared to monthly payments. This approach is beneficial if you anticipate stable usage patterns or have predictable resource allocation needs, aiding in long-term cloud cost optimization.

3 Best Alternatives to Datadog

If you’re looking for alternatives, here are some tools to consider, including how they stack up and what makes them unique.

1. Prometheus and Grafana

Cost: Free (self-hosted), though operational overhead is significant.

Details:
Prometheus is the gold standard for open-source time-series monitoring, often paired with Grafana for visualizations. Together, they form a robust monitoring solution for infrastructure and applications. You get customization at its best—tailored queries, alerts, and dashboards.

• Pros:
  • Fully customizable with self-hosting options.
  • No licensing costs—great for smaller teams with technical resources.
• Cons:
  • Operational overhead—requires hosting, scaling, and management.
  • Learning curve and upkeep are needed if self-hosting.

2. Last9

Cost: Transparent, events-based pricing tailored for SRE and reliability-focused use cases, providing observability at scale.

Details:
Last9, trusted by industry leaders like Disney+ Hotstar, Games24x7, CleverTap, and Replit, is a Telemetry Data Platform that optimizes cloud-native monitoring by balancing performance, cost, and user experience.

Our platform easily integrates with OpenTelemetry, Prometheus, and more to unify metrics, logs, and traces—efficiently managing high-cardinality data.

Through our Control Plane's smart alerting and real-time metrics, we empower engineering teams with deeper insights into their observability and operational intelligence needs.

Pros:

• Clear pricing model and lightweight setup.
• Focuses on high-cardinality data and unified observability.
• Keeps costs in check while providing valuable insights.

Cons:

• May not suit teams seeking open-source solutions.

3. Elastic Observability

Cost: Starts at $16 per host per month (cloud-hosted).

Details:
Elastic Observability is part of the Elastic Stack (formerly ELK Stack)—well-suited for log monitoring, metrics collection, and APM. Its standout feature is a search-driven approach to observability. Logs, traces, and metrics are indexed into a centralized platform, enabling powerful searches and dashboards.

• Pros:
  • Powerful search and dashboarding capabilities.
  • Best for teams familiar with the Elastic ecosystem.
• Cons:
  • Costs can creep up depending on data volume and retention needs.
  • Less intuitive for those new to Elasticsearch.

The Takeaway:

Your choice of monitoring tool depends on your team’s priorities:

• Customization on a budget? Prometheus and Grafana.
• SRE-focused insights with minimal noise? Last9.
• Search-heavy log analysis and observability? Elastic Observability.

FAQs

Is Datadog free or paid?
Datadog offers both free and paid plans. The free plan covers basic monitoring features, while the paid plans unlock advanced tools like APM, security monitoring, and log management.

What is the weakness of Datadog?
Datadog's pricing structure can get expensive for large teams or enterprises, especially with high volumes of logs, custom metrics, or high-cardinality data. Its complexity might also be challenging for smaller teams.

How much is Datadog vs. Prometheus?
Datadog starts at $15 per host per month for infrastructure monitoring, while Prometheus is open-source and free but requires more setup and management.

How Much Does Datadog Cost?
Pricing varies by service:

• Infrastructure monitoring: Starts at $15 per host/month (annual).
• APM: Starts at $31 per host/month.
• Log management: $0.10 per GB for indexed logs. Additional costs can come from custom metrics, high-cardinality data, and data retention.

What is allotted with Serverless APM?
Serverless APM in Datadog monitors serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions), tracking execution times, errors, and bottlenecks.

How to calculate Datadog cost?
Cost depends on usage, including infrastructure monitoring, APM, log management, and security monitoring. Consider the number of hosts, data ingested, custom metrics, and retention periods to optimize costs.

What is the span retention period?
Retention refers to how long Datadog keeps your metrics, logs, and traces. Standard retention varies by service, with options to extend for an additional fee.

What counts as a billable DSM host?
A billable DSM host is any server or instance where you’ve enabled Datadog’s security monitoring. These hosts are part of your billable allocation, with costs depending on the number of hosts and security features required.

Can I switch to the APM Pro or APM Enterprise?
Yes, you can upgrade to Datadog’s APM Pro or Enterprise plans for more advanced features like extended trace retention and increased scalability.

What pricing plans does Datadog offer?
Datadog provides several pricing plans based on your needs, ranging from free for basic features to enterprise-level plans with advanced capabilities like custom metrics and extended retention.

What factors influence Datadog's pricing structure?
Pricing depends on factors like the number of hosts, log and trace volume, use of custom metrics, data retention needs, and specific services (APM, SIEM, etc.).

What are the pricing tiers for Datadog services?

• Basic/Free: Limited features for getting started.
• Pro: Full-featured monitoring for small to medium teams.
• Enterprise: Designed for large organizations with advanced features, custom configurations, and extended retention.

What factors influence the cost of using Datadog?
Costs are influenced by factors like the number of hosts, data volume, service levels (e.g., APM, SIEM), and data retention. Optimizing usage and managing cloud costs can help control expenses.

Does Datadog rehydrate logs?
Yes, Datadog can rehydrate archived logs for analysis. This feature is paid, and costs depend on the data volume and retention policies.