Datadog vs. Splunk: a side-by-side comparison for 2025

Datadog and Splunk are both tools used to collect, store, and analyze log data. Both are popular among businesses of all sizes, but they have some distinct differences.

Datadog is a monitoring and analytics platform that provides real-time visibility into the performance of applications, infrastructure, and services. It also includes features for alerting, dashboard, and data visualization. On the other hand, Splunk is a software platform that provides insights and intelligence from machine-generated data such as logs, events, and time-series data.

This article will compare the two products in detail and will evaluate factors such as real-time data analysis, scalability, cost, and ease of use to determine which tool is more suitable for your business.

Features overview

✕ - does not support

✓ - partial support

✓✓ - full support

1. Deployment options - Splunk

Datadog is only available a SaaS solution, where the software is hosted and managed in the cloud while Splunk provides several deployment options including on-premise, cloud, and hybrid deployment options. Additionally, Splunk offers the ability to deploy to a virtual machine or containers.

Splunk wins this round for having more deployment options.

2. Data sources - Splunk

Datadog supports a wide variety of data sources. Some examples include:

• Application and server logs: from various sources such as files, syslogs, and cloud providers.
• Metrics: from various sources such as host-level metrics, cloud provider metrics, and application performance metrics.
• Traces: from various sources such as distributed tracing, Application Performance Management (APM) and mobile apps, in order to provide end-to-end visibility into the performance of your applications.
• Infrastructure: from infrastructure and cloud providers such as AWS, GCP, Azure, and Kubernetes.

These data sources can be collected and processed using the Datadog Agent, which is a small software package that can be installed on servers and applications.

Splunk, on the other hand, can also collect and process data from many sources such as:

• Logs: from various sources such as files, syslogs, and cloud providers.
• Metrics: from various sources such as host-level metrics, cloud provider metrics, and application performance metrics.
• Traces: Splunk can collect and process traces, in order to provide end-to-end visibility into the performance of your applications.
• Network data: from network devices such as routers, switches, and firewalls.
• Security data: from sources such as intrusion detection systems (IDS), security information and event management (SIEM) systems, and network security devices.

These data sources can be collected and processed using Splunk forwarders, which are small software packages that can be installed on servers and applications. The forwarders send the data to a Splunk indexer, which processes and stores the data. Note that Splunk can work with structured, semi-structured, and unstructured data making it the more flexible of the two.

3. Data visualization - Datadog

Both Datadog and Splunk have options for visualizing data, but they differ in their focus and capabilities.

Datadog provides a range of chart types, including line charts, bar charts, area charts, scatter plots, and heatmaps, that you can use to visualize data. You can also group charts to create dashboards, giving you an overview of your entire infrastructure. You can customize the layout, add widgets, and configure alerts to be notified of changes in the data. The alerts can be sent via email, SMS, or other methods, and you can customize the thresholds and conditions that trigger the alerts.

Splunk is similar when it comes to these basic functionalities. It also offers the user the ability to create charts, graphs, maps, dashboards, as well as alerts.

The main difference between Datadog and Splunk in terms of visualization is that Datadog offers a variety of pre-built dashboards and visualizations for real-time monitoring, while Splunk doesn't. But it does have a range of visualization options and allows you to create custom visualizations.

Datadog wins this round for having pre-built dashboards and visualizations.

4. Real-time monitoring and alerting - Datadog

Both Datadog and Splunk provide a comprehensive set of features for real-time monitoring, including data collection, alerting, dashboards, and anomaly detection. These features allow you to monitor the performance and availability of your infrastructure, applications, and services in real time and quickly identify and resolve issues as they arise.

Datadog provides real-time visibility into the performance of your systems, with the ability to set up alerts and notifications. Splunk is more geared towards analyzing and searching through large volumes of data, but it does have some real-time capabilities.

5. Search capabilities - Splunk

Both Datadog and Splunk use their own proprietary query languages to search and analyze data. These query languages are designed to be robust and flexible, allowing you to perform complex searches and aggregations on your data. Both platforms provide robust search capabilities that allow you to easily and quickly analyze and visualize your data.

In comparison, Splunk has a powerful search engine that allows you to quickly search through large volumes of data and find the information you need. It also provides advanced search capabilities, such as regular expressions and data transformation commands. Datadog has more limited search capabilities, but it does allow you to filter and group data in various ways.

6. Machine learning - Splunk

Datadog includes several machine learning features such as Anomaly Detection, which uses a combination of statistical modeling and machine learning algorithms to automatically identify patterns in your metrics data, and Forecasting, which uses historical data to predict future values. Additionally, Datadog allows you to use their own library of machine learning models, or even bring your own models using their API.

Splunk also has built-in machine learning capabilities, such as its Machine Learning Toolkit, which provides a wide range of algorithms and models that can be used to analyze and extract insights from data. Splunk also offers the ability to create custom machine learning models using Python and R, and the ability to deploy models in a production environment.

Overall, both Datadog and Splunk have ML capabilities, but Splunk is more suited for advanced machine learning and data analysis tasks, while Datadog is more focused on monitoring and anomaly detection.

7. Scalability - tie

Both Datadog and Splunk are highly scalable, with the ability to handle large amounts of data and handle increasing loads.

Datadog allows for horizontal scalability by adding more servers to your cluster, which allows for more data to be collected, stored and queried. Additionally, it offers auto-scaling capabilities in their SaaS option which automatically adjusts the number of hosts and resources to handle the load.

Splunk also allows for horizontal scalability by adding more indexers and forwarders as the volume of data increases. Splunk also offers a feature called "Indexer Cluster" which allows multiple instances of splunk to share the load of indexing, search and query. As a result, Splunk is typically used for more complex and larger-scale log management and analysis needs.

8. Pricing - Datadog

Datadog and Splunk have different pricing models.

The cost of a Datadog subscription depends on the features and services you need, as well as the size of your infrastructure. For example, for the infrastructure monitoring service, Datadog offers a free tier, a pro tier that starts at $15 per host per month, and an enterprise tier that is $23 per host per month. Datadog also offers log management starting at $0.10 per GB per month and APM at $31 per host per month.

Splunk also offers different prices for different solutions. Its observability solution starts at $15 per host per month. For the other products such as the Splunk Cloud Platform and the Splunk Enterprise Platform, you must contact Splunk for detailed pricing.

Datadog wins for having a free plan, but both options can get expensive really quickly.

9. UI and UX - tie

Datadog is packed with various visualization and customization features, as well as dashboards where you can customize the layout and add widgets to the dashboards to display different types of data.

Splunk aims to provide an intuitive and easy-to-use interface that helps users quickly and efficiently find the information they need.

Its user interface consists of several key components, such as a navigation menu, which provides access to various features and tools within the Splunk platform; a dashboard that displays a customizable set of panels that display data and visualization in real-time; a results panel that shows the results of a search query, including data tables and charts, and so on.

Datadog and Splunk have user-friendly interfaces but differ in their focus and capabilities. Datadog has a more streamlined interface focused on real-time monitoring, while Splunk has a more robust interface geared toward data analysis and search.

10. Documentation and support - tie

Both Datadog and Splunk provide extensive documentation on their platforms, including guides, tutorials, and API references. Datadog's documentation is organized around specific product areas, while Splunk's documentation is organized by task.

Both companies offer a variety of support options, including online resources, community forums, and premium support plans. Datadog offers a range of support plans, including a free community support plan and paid plans with various levels of support. Splunk offers a range of support options, including a free community support plan and paid plans with various levels of support and access to advanced features.

Overall, both Datadog and Splunk offer a range of documentation and support resources to help users get the most out of their platforms.

Better Stack: the modern alternative to Datadog and Splunk

Better Stack is a comprehensive observability platform that unifies logging, monitoring, and incident response. This all-in-one solution helps you achieve complete visibility without juggling multiple tools.

Better Stack Telemetry (formerly Logtail) makes it easy to aggregate logs from diverse sources across your infrastructure. Whether you're collecting from cloud services like AWS, GCP, Azure, or on-premises servers, the platform streamlines the process with automatic parsing and real-time ingestion.

You can connect new log sources within minutes. The platform handles data processing, compression, and secure transmission while providing powerful SQL-compatible search capabilities. It integrates seamlessly with Kubernetes, Heroku, Docker, AWS, and more using any log shipper of your choice. ClickHouse technology ensures fast search performance with automated alerting.

Better Stack includes uptime monitoring that tracks your applications, APIs, and scheduled tasks with notifications via email, SMS, phone calls, Slack, or mobile apps. On-call scheduling integrates with Google Calendar, and escalation policies automate responses when alerts aren't acknowledged.

The platform offers a generous free tier with 3 GB of log storage for 3 days plus 10 monitors. Additional usage costs $0.45 per GB for ingestion and $0.025 per GB weekly for retention (rates may vary by region). Bundled plans start at $25 monthly.

Final thoughts

In this article, we compared Datadog and Splunk. Both are software platforms that provides a range of tools and features for monitoring, analyzing, and visualizing data.

Datadog is a cloud-based monitoring and analytics platform designed specifically for modern, distributed applications and infrastructure. It offers a range of tools for monitoring and analyzing data from various sources, including logs, metrics, and traces.

Splunk is a data analytics and visualization platform that is designed to help users search, analyze, and visualize data from various sources and applications.

Overall, Datadog is often used by developers, operations teams, and other technical users who need to monitor and troubleshoot distributed applications and infrastructure. Splunk is often used by business analysts, data scientists, and other non-technical users who need to search, analyze, and visualize data from various sources.

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