When choosing between Datadog and Dynatrace, two of the cloud monitoring and observability platforms, understand their strengths, weaknesses, and unique features.
This in-depth comparison will help you make an informed decision based on your business needs and technical requirements.
Datadog vs Dynatrace: Datadog is a cloud monitoring platform that covers infrastructure metrics, APM, logs, and security through a single agent and hundreds of integrations. Dynatrace is an AI-driven observability platform that uses Davis causal AI to automate root-cause analysis across full-stack environments, with agents and an assistant under the Dynatrace Intelligence brand. Choose Datadog for breadth of integrations and flexible, usage-based pricing. Choose Dynatrace for automated root-cause detection in large enterprise environments with complex dependencies.
What Are Datadog and Dynatrace?
Both Datadog and Dynatrace provide end-to-end monitoring solutions designed to offer visibility into your cloud infrastructure, applications, and user experience.
They help businesses detect, diagnose, and resolve issues in real-time, ensuring optimal performance and availability of digital systems.
- Datadog is known for its wide array of integrations, user-friendly interface, and scalability. It’s often favored by DevOps teams for its flexibility, strong cloud-native capabilities, and ease of use.
- Dynatrace stands out with its AI-powered, fully automated monitoring capabilities. Known for advanced analytics and deep insights, it’s a great choice for enterprises looking for automated solutions to handle complex environments.
Last9 
| Aspect | Datadog | Dynatrace |
|---|---|---|
| Core strength | Breadth of integrations (1,000+) | AI-powered root-cause analysis (Davis causal AI) |
| Deployment | SaaS sites only (BYOC covers logs only) | SaaS on AWS, Azure, GCP + Managed (your data centre) |
| APM approach | Datadog Agent + SDKs, Single Step Instrumentation | OneAgent auto-instrumentation |
| Pricing model | Separate meters: hosts, GB, events, metrics, AI | DPS annual commitment, drawn down on an hourly rate card |
| Log management | Integrated, indexed separately | Integrated with Grail data lakehouse |
| Kubernetes | Cluster Agent + node agents | OneAgent operator, auto-discovery |
| Best for | DevOps teams wanting flexibility | Enterprise IT with complex dependencies |
Datadog vs. Dynatrace: Key Features Breakdown
Both platforms provide a range of monitoring tools, but their approaches and features differ significantly.
| Feature | Datadog | Dynatrace |
|---|---|---|
| Monitoring Capabilities | Infrastructure, APM, Logs, Synthetic Monitoring, Security Monitoring | Full-stack monitoring, APM, Real-Time User Monitoring, AI-powered anomaly detection |
| AI & Automation | ML anomaly detection, Bits AI agents billed in AI Credits, MCP server | Davis causal AI, Dynatrace Assist and agents, hosted MCP server |
| User Interface | Intuitive, easy to use, customizable dashboards | Advanced, AI-powered insights, steep learning curve |
| Integrations | 1,000+ integrations across cloud and on-prem systems | About 930 Hub items, strong on Kubernetes and cloud-native, plus mainframe |
| Pricing | Usage-based, flexible but can get expensive at scale | DPS annual spend commitment, drawn down on an hourly rate card |
| Scalability | Highly scalable for cloud-native environments | Excellent scalability for large enterprises, microservices, and complex systems |
1. Monitoring Capabilities
- Datadog provides an extensive set of tools across multiple use cases:
- Infrastructure Monitoring: Full monitoring for hosts, containers, and cloud infrastructure.
- Application Performance Monitoring (APM): Tracks the performance of applications, helping developers understand latency, bottlenecks, and root causes of failures.
- Log Management: Collects, searches, and analyzes logs, offering high visibility into logs and their associated metrics.
- Synthetic Monitoring: Simulates user interactions to monitor the availability and performance of services across the globe.
- Security Monitoring: Provides real-time security analytics to protect infrastructure and applications from potential threats.
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- Dynatrace offers an AI-driven monitoring solution with deep insights:
- Full-Stack Monitoring: Includes infrastructure, APM, and real-time user monitoring (RUM), automatically mapping and tracking dependencies across the environment.
- AI-Powered Anomaly Detection: Dynatrace uses AI (Davis AI) to automatically detect anomalies, reducing the need for manual configuration.
- Real-Time Diagnostics: Automatic root-cause analysis, which helps identify issues in real-time across distributed systems.
- Cloud Automation: Fully integrates with Kubernetes, Docker, and cloud-native environments, offering cloud-native monitoring out-of-the-box.
2. AI & Automation
- Datadog offers machine learning features for anomaly detection, alerting, and forecasting, which take more manual configuration. Bits AI adds investigation, chat, and code fixes, and Datadog runs an MCP server for any MCP client. Every Bits agent draws on AI Credits.
- Dynatrace leans on AI-powered automation. Davis causal AI detects performance problems and their root causes without manual thresholds, which helps most in large, complex systems. Under the Dynatrace Intelligence brand, Dynatrace Assist (formerly Davis CoPilot) answers questions and generates DQL, and named agents such as the SRE and Kubernetes troubleshooting agents run investigations. A hosted remote MCP server exposes read-only tools to coding agents. The AI has no separate licence, but the queries it runs consume DPS, and it is not available on Dynatrace Managed.
- Last9, for comparison, has no per-query or per-investigation AI charge, and its agent runs on your own model provider or inference endpoint.
3. User Experience
- Datadog is known for its intuitive, user-friendly interface. It provides a highly customizable dashboard that lets users create and track their metrics easily. There is a learning curve, but setup and day-to-day use are straightforward.
- Dynatrace, although offering a wealth of advanced features, has a more complex interface. Its AI-powered insights make up for this complexity, but it requires a higher level of expertise to fully use its capabilities.
Last9 
4. Integrations
- Datadog supports over 1,000 integrations, allowing it to monitor a wide range of platforms and services. From cloud services to on-prem applications, Datadog is highly flexible.
- Dynatrace focuses heavily on cloud-native environments and microservices, offering deep integrations with Kubernetes, Docker, and cloud providers like AWS, Azure, and Google Cloud. The Dynatrace Hub lists about 930 items. It also covers legacy estates: OneAgent for z/OS traces CICS and IMS transactions at code level on the mainframe.
5. Pricing
Both Datadog and Dynatrace are on the costlier end of the spectrum.
- Datadog follows a usage-based pricing model that can become expensive as you scale. It bills separate meters for hosts, ingested GB, indexed events, custom metrics, and AI credits. Each custom metric tag combination above 100 per host is billed, so new label values add to the bill.
While it offers a free trial, the costs can add up, making it more suitable for organizations that are willing to pay a premium for flexibility and scalability.
- Dynatrace sells the Dynatrace Platform Subscription (DPS): an annual, platform-level spend commitment drawn down on an hourly rate card, with unlimited users. For example, Full-Stack Monitoring is $0.01 per memory-GiB-hour, and logs are $0.20 per GiB ingested, plus retention and query charges. Older contracts may still use classic licensing (host units and Davis Data Units), which DPS replaces.
The commitment means a larger spend upfront. Automation can cut time spent on manual configuration, but plan for the licensing model itself to take effort: it is one of the most common complaints about Dynatrace.
Last9 takes a different approach: one licence, with the infrastructure on your own cloud bill and no charge per label or metric name.
These tools are designed for larger organizations or teams willing to pay a hefty price for powerful monitoring and automation features.
6. Scalability
- Datadog is well-suited for cloud-native environments and works effectively with small to medium-sized teams. It scales easily with cloud deployments, but as you add more systems and increase log volume, pricing can increase quickly.
- Dynatrace is built with scalability in mind, particularly for large enterprise environments. Its AI and automation make it an excellent choice for organizations with complex, large-scale infrastructures, and it can handle thousands of microservices with ease.
7. Performance & Reliability
- Datadog excels in environments where flexibility and ease of integration are key. However, its performance might degrade as you scale, especially when dealing with large amounts of log data or complex systems.
- Dynatrace is built for performance at scale. Its AI-powered automation and real-time diagnostics ensure minimal downtime and high reliability in enterprise-grade environments.
8. Support and Documentation
Datadog
Strengths:
- Full Guides: Datadog’s documentation provides a thorough, step-by-step approach, helping users set up and configure their tools easily.
- User-Friendly: The clear structure and well-organized topics make it easy for users to find what they need quickly, from basic integrations to advanced configurations.
- Active Community Forum: The community forum is a helpful resource for troubleshooting, with real-time answers and tips from other users.
Weaknesses:
- Complexity for Beginners: While thorough, some sections may be overwhelming for beginners, especially when it comes to troubleshooting advanced issues.
- Occasional Gaps: Some documentation, especially for newer features, might be sparse or require users to rely on community discussions rather than official resources.
Dynatrace
Strengths:
- Detailed Enterprise-Level Resources: Dynatrace’s documentation is thorough, with a strong focus on complex environments, which is ideal for large teams and enterprise-scale operations.
- In-depth Troubleshooting: The troubleshooting guides are highly detailed, covering a wide range of potential issues that users may encounter in large, complex infrastructures.
- Training and Learning Resources: The platform offers rich online training materials, making it easier for teams to level up their skills and knowledge.
Weaknesses:
- Enterprise-Focused: For smaller teams or individual users, the documentation can sometimes feel too focused on enterprise-scale needs, making it less relatable for non-complex setups.
- Learning Curve: The advanced features and integrations come with a steep learning curve, which might be a hurdle for those unfamiliar with enterprise-grade monitoring tools.
Top 5 Datadog and Dynatrace Alternatives
We cover the top five below. For the wider field, our full guide to Datadog alternatives compares nine tools by use case, pricing, and migration path.
1. Last9
Last9 is an OTLP-native observability platform compatible with Prometheus and OpenTelemetry, built to manage high-cardinality metrics, logs, and traces at scale without runaway cost.
Key Features:
- Unified Telemetry: View metrics, logs, and traces together in one place for correlation and monitoring in one view.
- Real-Time Dynamic Metrics: Generate scoped metrics in real-time and manage cardinality using Streaming Aggregations, enabling efficient data analysis.
- Runs in Your Own Cloud: Last9 runs single-tenant in your own cloud account (AWS or GCP), in any region, and Last9 manages it. It is available on the AWS and GCP marketplaces.
- AI and MCP: One assistant in the app, in Slack, and in your IDE through MCP. The production agent, Last9, runs on your model provider or inference endpoint, and customer data is never used for training.
Capabilities:
- Smart Alerting: Set granular alerts and use anomaly detection for high cardinality data, integrated with GitOps and IaC workflows for automated alert management.
- Control Plane: Drop, remap, redact, forward, and aggregate metrics, logs, and traces at ingest, from the UI. Every rule is previewed against live data before you save it.
- High Cardinality Without Sampling: High-cardinality labels are kept with no sampling, with 20M series per metric per day by default.
- Instant Insights: Access one-click dashboards and alerts that deliver operational recommendations to improve system performance quickly.
Pricing:
- One Licence: The infrastructure runs on your own cloud bill, at your negotiated rates. There is no per-query or per-investigation AI charge, and no charge per label or metric name.
- Open-Source Advantage: Being OTel-native, Last9 reduces long-term costs associated with vendor lock-in.
For a side-by-side view, see Last9 vs Dynatrace and Last9 vs Datadog.
2. New Relic
Key Features:
- Full-Stack Observability: Provides detailed insights into infrastructure, applications, and digital customer experiences.
- Automatic Instrumentation: Simplifies setup with automatic instrumentation for key technologies.
- AI-Powered Anomaly Detection: Uses machine learning to detect performance issues in real-time.
Capabilities:
- Real-Time Monitoring: High-definition monitoring of cloud environments, applications, and infrastructure.
- Integration with AWS: Works well with AWS services like EC2, Lambda, and S3 for smooth observability.
- Open Integrations: Supports integration with open-source tools like Prometheus and Grafana.
Pricing:
- Usage-Based Pricing: Pricing scales based on usage and can become costly as teams scale.
- Free Tier: Provides a limited free tier with basic monitoring features.
Last9 has been crucial for us. We’ve been able to find interesting bugs, that were not possible for us with New Relic. – Shekhar Patil, Founder & CEO, Tacitbase
3. Splunk Observability Cloud
Key Features:
- Full Monitoring: Supports log management, APM, and infrastructure monitoring in a unified platform.
- Cloud-Native Integrations: Easily integrates with popular cloud providers and services.
- AI-Driven Insights: Utilizes machine learning to provide proactive alerts and diagnose problems quickly.
Capabilities:
- Real-Time Data Ingestion: Fast data collection for real-time monitoring of cloud-native applications.
- Advanced Visualizations: Highly customizable visualizations for a more user-centric experience.
- Anomaly Detection: Automatically identifies outliers in data to alert teams on potential issues.
Pricing:
- Pay-as-You-Go: Uses a pay-per-data-ingested model, which can become expensive with large-scale deployments.
- Enterprise Tier: Offers an enterprise-tier pricing structure with more advanced features.
4. SignalFx (Acquired by Splunk)
Key Features:
- Cloud-Native Monitoring: Primarily designed for monitoring modern cloud infrastructures and microservices.
- Real-Time Analytics: Focuses on providing real-time monitoring data for faster decision-making.
- Distributed Tracing: Integrated tracing for full-stack observability.
Capabilities:
- Dynamic Alerting: Sends alerts based on anomaly detection and real-time performance insights.
- Custom Metrics: Ability to create custom metrics for better visibility into app and system performance.
- Extensive Integrations: Integrates with AWS, Kubernetes, and more.
Pricing:
- Scalable Pricing: Pricing is based on data volume and usage, which may be cost-effective for smaller teams but can become costly as usage increases.
- Flexible Plans: Offers flexible plans for teams at different stages of scaling.
5. AppDynamics (Cisco)
Key Features:
- End-to-End Monitoring: Covers application performance monitoring, business transaction monitoring, and infrastructure monitoring.
- Real-Time Diagnostics: Provides real-time diagnostic insights into code-level performance.
- Business Insights: Tracks business metrics alongside technical performance data.
Capabilities:
- Automatic Detection: Automatically detects application performance issues and provides deep root cause analysis.
- Cloud and On-Prem Monitoring: Flexible deployment options, supporting both cloud-native and hybrid environments.
- AI-Powered Insights: Uses machine learning to predict and prevent potential outages and performance degradation.
Pricing:
- Subscription-Based: Pricing is tiered, based on usage, and tends to be expensive for larger infrastructures.
- Enterprise Focused: Primarily designed for larger enterprises, making it more suitable for large-scale teams.
Practical Challenges in Integrating and Scaling Datadog and Dynatrace
Datadog Challenges
- Complex Configuration: Setting up custom metrics and fine-tuning integrations can be time-consuming, especially for large, multi-cloud environments.
- Cost Management: As you scale, the cost of log management and monitoring services can rise significantly, making it difficult to predict long-term expenses.
- Data Overload: With extensive integrations, Datadog can flood users with a massive volume of data, making it hard to extract actionable insights without a proper filtering strategy.
Dynatrace Challenges
- Steep Learning Curve: The initial setup and configuration can be difficult for teams unfamiliar with its advanced features, and DQL is the only query language (there is no PromQL).
- Resource Consumption: Dynatrace’s deep monitoring capabilities can put a strain on system resources, particularly when monitoring large numbers of microservices or cloud-native applications.
- Enterprise Complexity: For businesses with rapidly changing infrastructure, managing and configuring Dynatrace’s extensive features can be complex, requiring ongoing adjustments to ensure full visibility and performance optimization.
Migrating from Datadog to Dynatrace (or vice versa)
Migrating between Datadog and Dynatrace can be challenging, but it’s manageable with the right approach. Here’s a simplified overview:
- Assess Your Current Setup: Identify key metrics, integrations, and dashboards you’re using on Datadog or Dynatrace.
- Set Up Parallel Monitoring: Run both tools side by side for a brief period to ensure no data is missed during the transition.
Here’s a step-by-step guide to sending metrics and traces from the Datadog Agent to Last9.
- Recreate Key Metrics and Dashboards:
- Datadog to Dynatrace: Map your custom metrics and logs to Dynatrace’s full-stack monitoring and AI-powered insights.
- Dynatrace to Datadog: Recreate custom dashboards and integrations in Datadog’s interface.
- Test & Validate: Ensure the new platform is collecting the same data and providing accurate alerts. Test critical integrations and services to confirm everything is monitored correctly.
- Train Your Team: Provide basic training for the new platform, focusing on essential features like dashboards and alert configurations.
- Decommission the Old Tool: Once the new platform is fully functional, remove any agents or configurations related to the previous tool.
Datadog vs Dynatrace: Which Should You Choose?
Choose Datadog if: you want broad coverage across infrastructure, APM, logs, and security, a large integration catalog, and per-host pricing you can plan around. See our Datadog pricing guide for the numbers.
Choose Dynatrace if: you run a large, complex estate and want automated discovery and AI root-cause analysis with less manual setup, and can commit to an annual DPS spend. If New Relic is also in the mix, see New Relic vs Datadog.
Choose Last9 if: you want OpenTelemetry-native metrics, logs, and traces with high cardinality kept, PromQL and LogQL, and data that stays in your own cloud account. Last9 has no mainframe tracing and does not run in your own data centre, so Dynatrace fits better there.
Where Last9 Fits
Datadog is known for its flexibility, ease of use, and broad integrations, making it ideal for teams seeking a versatile tool, though it can get pricey as you scale. Dynatrace stands out with its AI-driven automation and scalability, making it a great choice for large enterprises with complex environments.
But if you’re looking to avoid the high costs of traditional observability tools and want to get your logs, metrics, and traces all in one platform, Last9 might be just what you need.
While managed solutions like Datadog and New Relic are convenient, they can be costly and prone to vendor lock-in.
Last9 is a telemetry data warehouse built to scale, handling logs, traces, metrics, and events, plus a control plane that lets you shape that data at ingest. It runs in your own cloud account, and it was named a Gartner® Cool Vendor in AI for SRE and Observability (2025). If Dynatrace is the tool you are weighing it against, our Last9 vs Dynatrace comparison covers AI, pricing, and deployment in detail.
Schedule a demo to explore how Last9 can simplify your observability setup.
FAQs
What’s the main difference between Datadog and Dynatrace?
Datadog offers flexibility with a wide range of integrations and ease of use, while Dynatrace focuses on AI-powered automation and scalability, making it ideal for large enterprises.
Which tool is better for small teams?
Datadog is often preferred by smaller teams due to its user-friendly interface, broad integrations, and flexibility. It’s easier to get started with and scales as you grow.
How does Datadog’s pricing compare to Dynatrace?
Datadog bills separate meters for hosts, ingested GB, indexed events, custom metrics, and AI credits, so costs can climb as logs and hosts grow. Dynatrace sells the Dynatrace Platform Subscription (DPS), an annual spend commitment drawn down on an hourly rate card, which means a larger commitment upfront.
Which tool is better for large enterprises?
Dynatrace is designed for large-scale, complex environments with advanced AI-driven automation and deep insights. It’s a better fit for large enterprises dealing with microservices and cloud-native applications.
Can I use both Datadog and Dynatrace together?
While it’s possible to use both tools, running them side by side can lead to data duplication and increased complexity. It’s better to choose one based on your business needs.
How easy is it to switch from Datadog to Dynatrace?
Migrating from Datadog to Dynatrace involves setting up new integrations, recreating dashboards, and ensuring your metrics are captured correctly. Both platforms offer different setups, so it’s key to carefully plan the transition.
Which tool has more integrations?
Datadog lists 1,000+ integrations. The Dynatrace Hub lists about 930 items, with a strong focus on cloud-native environments such as Kubernetes, plus mainframe coverage through OneAgent for z/OS.
Is Dynatrace’s AI worth the complexity?
For large organizations with complex environments, Davis root-cause analysis and the Dynatrace Intelligence agents can save real investigation time. The AI has no separate licence, but the queries it runs consume DPS, and it is available on Dynatrace SaaS only, not on Managed.
Can I integrate Last9 with Datadog or Dynatrace?
Last9 ingests OpenTelemetry, Prometheus, and dual-shipped Datadog Agent data, so a Datadog setup can send to Last9 without re-instrumenting. For Dynatrace there is no importer, and OneAgent does not export OTLP, so services move to OpenTelemetry, and a Last9 engineer runs that migration with your team.
