Ankur Mandal

March 11, 2024

Datadog Vs Splunk: In-Depth Comparison


Ankur Mandal

5 min read
March 11, 2024

The growing complexity of IT environments emphasizes the significance of observability tools in ensuring reliability, resilience, and operational efficiency. They ensure modern IT systems' effective and seamless functioning. 

Among the prominent players in this field, Datadog and Splunk emerge as distinct contenders, each with unique features, benefits, and limitations. 

We present a Datadog vs Splunk blog if you struggle with which tool to adopt for your business. This comprehensive analysis delves into various factors, enabling you to make an informed decision that aligns perfectly with your needs and requirements.

Effective log management and monitoring are pivotal in maintaining a resilient IT framework by offering valuable insights into the system's well-being, security, and efficiency. Businesses can proactively tackle problems, uphold regulatory requirements, and enhance their IT setup by routinely analyzing logs.

A log monitoring and management system helps with the following:

  • Issue detection and troubleshooting
  • Security 
  • Performance monitoring
  • Compliance and auditing
  • Predictive analysis
  • Resource optimization
  • Incident response
  • Alerts and notifications

What Is Datadog?

Datadog website homepage

Datadog is a monitoring tool that provides exceptional visibility into your infrastructure, no matter where it is located – whether it's on-premises, in the cloud, or in a hybrid environment. This robust platform seamlessly incorporates logs, metrics, and traces, offering a comprehensive perspective that enables organizations to optimize and accurately resolve performance problems effectively.

Although numerous alternatives to Datadog exist in the market, its extensive stack monitoring features and emphasis on end-to-end visibility establish it as a leading option for individuals seeking a comprehensive observability tool.


Datadog is a proficient cloud cost management tool with the following features.

  • Application Performance Monitoring (APM): Enables the tracing of end-to-end processes, encompassing browser and mobile applications, databases, and specific lines of code. With APM, you gain visibility into the entire journey of your applications, allowing for effective troubleshooting and optimization.
  • Infrastructure Monitoring: Provides essential metrics, visualizations, and alerting mechanisms to ensure the smooth maintenance and optimization of your cloud or hybrid environments. Gain actionable insights into the performance of your infrastructure to drive efficient operations.
  • Network Performance Monitoring: Helps attain comprehensive visibility across all network components, spanning on-premises, cloud, and hybrid environments. This monitoring solution minimizes overhead and empowers you to identify and address network-related issues proactively.
  • Real User Monitoring (RUM): Delivers valuable insights into frontend performance from the perspective of real users. It helps understand how applications perform in real-world scenarios and prioritize improvements that enhance the user experience.
  • Synthetic Monitoring: This allows simulating user transactions without writing code, enabling proactive testing across various layers of your system. It also helps identify potential issues before users encounter them, ensuring smooth user experiences.
  • Log Management & Analytics: Allows unifying logs, metrics, and traces, providing a holistic view of your system. Analyzing log data within its rich context allows users to gain deeper insights into your applications' behavior and make data-driven decisions for improved performance.


  • It is simple to use and offers value for money with excellent support and learning tools that users can utilize.
  • It offers extensive and detailed reports that help the organization stay on track and understand the services comprehensively.


  • It lacks metrics and can not be detailed down to granular insights. Moreover, there is some difficulty with the data refreshers.
  • It does not offer mobile support, which means when you are away from the computer, you can use Datadog.

What Is Splunk?

Splunk website homepage

Splunk is an observability tool that empowers developers and business users to analyze monitoring data, optimize performance, and improve IT operations. Users can effortlessly access vast information within the Splunk ecosystem, deeply understanding their infrastructure and strengthening their monitoring processes with unmatched effectiveness.


Splunk, which is focused on monitoring and log management, has the following features.

  • Splunk Infrastructure Monitoring: Offers valuable insights and leverages powerful analytics to monitor and analyze your infrastructure effectively across hybrid and multi-cloud environments.
  • Splunk Application Performance Monitoring (APM): Efficiently collects and analyzes every span and trace from connected services to gain full-fidelity access to crucial application data.
  • Splunk Real User Monitoring (RUM): Acquires deep insights into the front-end user experience, providing a comprehensive view of application performance and health.
  • Splunk Synthetic Monitoring: Optimizes uptime, API/service endpoint performance, and end-user experiences by dynamically measuring the performance of web-based properties.
  • Splunk Log Observer: Effortlessly troubleshoots application and infrastructure behavior using codeless logs queries. Allows users to leverage high-context logs to identify system problems accurately.
  • Splunk On-Call: Offers unified incident response, log management, monitoring, and chat tools for a holistic view of system health. It ensures seamless coordination in maintaining the overall operational efficiency.


  • Efficiently monitor all devices, processing diverse log files to provide insights into device events, with seamless integration into cloud models for swift implementation.
  • Splunk offers robust log aggregation, search capabilities, and comprehensive infrastructure monitoring. Users can create custom dashboards for infrastructure, applications, and more.


  • It is expensive and has a steep learning curve, which can be overwhelming for organization that has large volume of files.
  • The constant user interface (UI) modifications in recent years have led to some dissatisfaction as one needs to familiarize themselves with the relocated features repeatedly. Currently, there are two versions of detectors: one exclusively accessible through CAC and the other available via the UI. It would be advantageous to consolidate these versions.

Datadog Vs Splunk

After developing a fundamental knowledge of Datadog and Splunk, it is time to examine the distinguishing factors and see in detail about Datadog vs Splunk.

1. Core Features

Let's deep dive into Datadog vs Splunk via starting with seeing the core features of the both tools. Following are the core features of Datadog and Splunk that showcase the difference between the two.

Datadog has the following key features:

  • Comprehensive Infrastructure and Application Performance Monitoring (APM): Datadog is well-known for its extensive APM capabilities, providing real-time insights into applications, services, and infrastructure. With this platform, users can proactively address performance issues and maximize resource utilization across their entire environment.
  • Distributed tracing functionality: Datadog excels in its distributed tracing functionality, which enables developers to identify bottlenecks and enhance application performance. This feature offers detailed visibility into the interactions between different components of distributed systems, making troubleshooting and optimization more efficient.
  • Machine learning-powered anomaly detection and alerting: Datadog's anomaly detection and alerting system, powered by advanced machine learning techniques, guarantees timely notifications to users about any performance deviations. By harnessing the power of machine learning algorithms, this innovative platform facilitates rapid issue resolution, efficiently assisting organizations in sustaining optimal performance levels.

Splunk distinguishes itself with the following key features:

  • Log management and analysis: Splunk demonstrates exceptional log management and analysis proficiency by offering robust capabilities for efficiently handling raw log data. With its powerful search, correlation, and visualization features, Splunk transforms unprocessed logs into meaningful insights, empowering users to extract valuable information from their data.
  • Search Processing Language (SPL): Splunk sets itself apart with its distinctive feature, the Search Processing Language (SPL), which empowers users to construct intricate queries and conduct in-depth analysis of their data. By providing a flexible and user-friendly interface, SPL enables sophisticated data exploration and manipulation, facilitating a seamless interaction with the information at hand.
  • Security Information and Event Management (SIEM): Splunk provides enhanced security information and event management (SIEM) capabilities through its offering, Splunk Enterprise Security (ES). This solution empowers organizations to detect, investigate, and respond to security threats, bolstering their security posture.
  •  IT Service Intelligence (ITSI) Module: Splunk's IT Service Intelligence (ITSI) module offers a comprehensive perspective of the IT environment, enabling organizations to gain valuable insights into the performance and robustness of their systems. By proactively identifying and prioritizing issues, ITSI assists in mitigating potential business operation disruptions, ensuring seamless and dependable IT services.

2. Ease Of Use

Let's see Datadog vs Splunk based on the ease of use of both the tools. The user experience is significant in data analytics, shaping the ultimate outcomes. With an easily navigable and user-friendly software solution, your team can experience a significant transformation, leading to increased efficiency and the ability to make informed decisions in the world of data monitoring and management.

Datadog has garnered recognition for its user-friendly interface and intuitive dashboards. The integration of drag-and-drop functionality not only streamlines the process of creating personalized visualizations but also simplifies the configuration of alerts. This customer-centric approach guarantees that even individuals without extensive technical knowledge can effortlessly embark on their monitoring journey with Datadog.

Splunk's interface is renowned for its extensive capabilities, specifically designed to cater to highly skilled users with advanced technical expertise. By harnessing the power of Splunk's Search Processing Language (SPL), users are empowered to embark on a thorough data analysis journey, where they can construct personalized queries to uncover insightful details. While this high level of customization provides a considerable advantage for experienced users, it is essential to acknowledge that the extensive range of features may require a more substantial learning curve for newcomers to the platform.

3. Troubleshooting

When looking for Datadog vs Splunk, troubleshooting is one of the important parameter to consider. If any mishap occurs, it is essential to have a monitoring solution that promptly notifies you about technical stack issues. Ideally, the solution should also detect and resolve problems beforehand for more efficient management.


  • Single-click correlation: Datadog streamlines troubleshooting by enabling users to correlate related metrics, traces, and logs throughout the stack with a single click. 
  • Swift identification of issues: It allows for efficient identification of problems and a deeper understanding of their impact on other components within the infrastructure. 
  • Comprehensive view of interconnected systems: By adopting an integrated approach, the tool enhances the troubleshooting process by offering a holistic view of interconnected system dynamics.


  • Quick notification: Splunk demonstrates remarkable proficiency in real-time streaming analytics, guaranteeing prompt notifications about emerging problems within the system.
  • Quick identification of issues: Using AI-powered troubleshooting, Splunk takes proactive measures to identify and highlight potential problems, employing dynamic thresholds, various conditions, and intricate rules for accurate predictions.
  • Enhances overall efficiency: It significantly diminishes the Mean Time To Repair (MTTR), improving the efficiency of resolving problems.

4. Dashboard

Dashboards in observability tools are a focal point for monitoring, analyzing, and collaborating, providing a visually appealing and adaptable representation of crucial data to facilitate efficient decision-making and problem-solving. Let's see Datadog vs Splunk based on their dashboard.


  • Comprehensive examination: Datadog's ready-made dashboards thoroughly examine data across the entire technology ecosystem on a single page. By utilizing user-friendly templates and automatically generated dashboards, 
  • Enhances accessibility: Datadog guarantees accessibility without the need for coding skills or familiarity with query languages. 
  • Intuitive data visualization: This intuitive data visualization method streamlines the user experience, enabling diverse users to acquire valuable insights effortlessly.


  • Automatic templates: Splunk offers dashboard templates for extensive stack monitoring. These templates empower users to automatically discover, analyze, group, and explore different components such as clouds, services, systems, availability systems, and Kubernetes clusters.
  • Challenging to understand: Although Splunk's functionality is robust, the ease of use may differ. Whether users require coding or query language proficiency to leverage these features is not immediately evident. Consequently, this could result in a more challenging learning curve than intuitive platforms like Datadog.

5. Scalability

Scalability plays a vital role in observability tools as it allows effective handling of the complexities in modern and dynamic IT environments. By enabling these tools to collect, process, and analyze data efficiently, scalability ensures their adaptability to scale, complexity, and technology changes.

Datadog leverages a cloud-native architecture that seamlessly scales to meet the requirements of organizations of any size. Its auto-scaling capabilities efficiently manage unexpected spikes in data volume without compromising performance. Datadog is an outstanding choice for those who prioritize scalability and adaptability, particularly in evolving operational environments.

Datadog enables horizontal scalability by including additional servers to augment the capacity for collecting, storing, and querying data. 

Additionally, its Software as a Service (SaaS) offering incorporates auto-scaling features that effortlessly adapt the number of hosts and resources to manage fluctuating workloads effectively.

Splunk stands out due to its robust architecture, which enables it to handle massive amounts of data effectively. This makes it especially suitable for large enterprises with complex infrastructures. However, it is worth noting that this increased scalability often comes with higher costs associated.

Splunk facilitates horizontal scalability by incorporating more indexers and forwarders to effectively handle the expanding data volume. An advanced feature, "Indexer Cluster," promotes scalability by allowing multiple Splunk instances to collectively manage the workload of indexing, searching, and querying. 

6. Incident Management And Alerting

Incident management and alerting play a crucial role in observability tools as they enable efficient detection, response, and resolution of issues. This ultimately enhances the reliability and performance of systems and applications, contributing significantly to overall operational effectiveness.

Datadog's incident management and alerting entails the following.

  • Detection utilizing machine learning: Datadog's tool, Watchdog, employs advanced machine learning algorithms to identify and inform users about potential issues within their IT infrastructure.
  • Alerting across multiple channels: Through the multi-channel alerting system, alerts are delivered to users through various channels, including PagerDuty, email, and Slack, ensuring timely notification of any potential problems.
  • Insightful alerts for swift issue resolution: These alerts notify users and provide actionable insights to resolve identified issues quickly and efficiently, boosting operational efficiency.
  • Expanded integration options: Unlike Splunk, Datadog offers a broader range of integration options, enabling greater flexibility in defining alert rules to suit individual requirements.
  • Tailorable alert rule definition: This flexibility empowers users to customize their alerting mechanisms according to specific needs, enhancing the monitoring solution's adaptability.

With Splunk, users get the following incident management and alerting benefits.

  • Enhanced alerting process: Splunk offers an advanced alerting process encompassing notifications, detectors, and events. This process includes specific alerts designed to inform administrators about any detected anomalies.
  • Splunk On-Call: To further elevate incident management operations, Splunk extends its functionalities with the incorporation of Splunk On-Call. This incident response and alerting tool is an additional resource, strengthening the overall incident management procedures.

7. Integration

Incorporating integration within an observability tool is crucial to attain a consolidated, extensive, and flexible strategy for overseeing intricate IT environments. It fosters compatibility, boosts cooperation, and empowers organizations to utilize various tools for maintaining optimal visibility and governance over their systems and applications.

Datadog emerges as a leader in the observability domain, offering an expansive collection of more than 500 integrations. This integration enables users to effortlessly collect logs and metrics from various technologies, including databases, containerized solutions, cloud providers, and more. 

Datadog's adaptability encompasses bare metal servers and virtual machines, making it a holistic solution that caters to the complexities of contemporary IT infrastructures.

Splunk offers built-in assistance for roughly 250 technologies, spanning a wide range encompassing popular and commonly used ones.

8. Pricing

Datadog provides users with different pricing options to cater to their needs. Users can avail of the free tier or choose from the pro tier, which begins at $15 per host per month. The enterprise tier is $23 per monthly host for those seeking top-notch services. In addition, Datadog offers log management options starting at $0.10 per GB per month. If you require Application Performance Monitoring (APM), this service costs $31 per host per month.

Splunk provides varying prices for its diverse range of solutions. The observability solution commences at $15 per host per month. However, for detailed pricing information on other products like the Splunk Cloud Platform and the Splunk Enterprise Platform, it is advisable to connect with Splunk.

Lucidity For Automated Identification & Provisioning of Storage Resources 

Storage should be a top priority of all your discovering aspects. This is because storage is a significant contributor to the overall cloud bill. Moreover, storage-related costs are increasing exponentially with the rapid increase in cloud complexity. This has been fortified by a study done by Virtana titled "State of Hybrid Cloud Storage in 2023". It was discovered that 94% of the cloud decision-makers agreed that their storage cost was increasing, and 54% said that when compared with overall cloud cost, the storage cost was growing faster.

Driven to find a solution to reduce storage usage expenses, we at Lucidity conducted an independent study on over 100 enterprises using cloud service providers like Azure, AWS, and GCP. We discovered that

  • On average, 40% of the cloud cost stems from storage usage and wastage.
  • Block Storage, aka AWS EBS, Azure Managed Disk, GCP Persistent disk, was responsible for a significant portion of the overall cloud cost.
  • Block Storage disk utilization for Root Volume, application disk, and self-hosted databases was extremely low.
  • Organizations were overestimating the growth and hence overprovisioning storage resources. Despite this overprovisioning, they struggled with at least one downtime per quarter. 

Additionally, the organization needed to improve the buffer to ensure sufficient space to accommodate changing storage requirements. However, enhancing the buffer is a complicated process that involves the following:

  • Manual intervention: Improving the buffer involves three processes- deploying, alerting, and monitoring, each necessitating three different tools. Moreover, you would need to assign a DevOps team to ensure these tools are seamlessly installed and working. This would lead to significant time and effort investment from the team.
  • Time wastage: When utilizing certain cloud service providers, activities such as decreasing 1 TB of disk space or upgrading disks require a minimum downtime of 4 and 3 hours, respectively. These downtime limitations pose considerable difficulties in guaranteeing uninterrupted operations, especially when maintaining constant service availability is crucial.
  • Increased latency: The process of disk upgrade leads to an increase in the latency period. This impacts the responsiveness of networked applications and services and impairs performance.
  • Gap for subsequent expansion: The initiation of the following expansion is subject to a minimum waiting period of 6 hours. This delay during the expansion process can impede the system's prompt adaptation to evolving demands, which could, in turn, impact the overall performance and responsiveness.

Despite this, organizations tend to keep storage optimization on the sideline. However, we understand the reasons for this compromise, which are as follows.

  • Storage optimization through custom tool development: The limited features offered by Cloud Service Providers (CSPs) for storage optimization often necessitate the development of custom tools. Organizations are compelled to create tailored solutions that align with their specific storage optimization requirements.
  • Heightened DevOps efforts and time commitment: Creating and maintaining a custom storage optimization tool significantly increases DevOps efforts and time investment. These custom tools demand ongoing development, rigorous testing, and continuous maintenance, contributing to the intricacy and additional workload in the DevOps workflow.
  • Cost-intensive: Investing in and deploying a monitoring tool can get costly. Moreover, since implementing these tools on the entire infrastructure can be time-consuming, organizations only implement them in production instances, leading to limited visibility. This means you do not have holistic insights into how different aspects, such as storage, are adding to the bill.
  • Shortcomings of CSP-provided tools: Reliance on CSP-provided tools for storage optimization can result in inefficiencies due to their limited capabilities. While these tools offer basic functionality, their lack of sophistication often hinders comprehensive storage optimization tasks.
  • Manual and resource-intensive work: Exclusively depending on the tools offered by CSPs may necessitate adopting labor-intensive approaches to meet optimization requirements. These hands-on procedures can be arduous and unfeasible for routine tasks, resulting in the depletion of valuable resources and workforce.
  • Lack of live shrinkage: Leading cloud service providers offer expansion of storage process, but live shrinkage of EBS volume/managed disk/persistent disk is impossible. While there is an alternative method to achieve shrinkage, it involves several manual processes such as creating new volume, taking snapshots, stopping the instance, and more. This leads to downtime. 

As you can see, storage optimization through the conventional method can easily become cumbersome and meddle with day-to-day operations, thus impacting performance. This is why organizations prefer overprovisioning the storage resources. However, overprovisioning not only reflects operational inefficiency but also increases cloud bills. This is because you are paying for the resources you are not using. 

The practice of overprovisioning necessitates cloud cost automation that can prove instrumental in reducing hidden cloud costs. This is where Lucidity's Storage Audit and Block Storage Auto-Scaler come into the picture. 

Lucidity uses automation as a cloud cost optimization technique for AWS, Azure, or GCP to automate the identification of storage wastage and provide storage resources. Lucidity has designed two solutions

Lucidity Storage Audit

Lucidity Storage Audit is an easy-to-use, executable tool designed to automate identifying overprovisioned and idle/unused storage resources. 

Why do we need to automate the process when monitoring tools exist, or can we just manually discover them?

This is because utilizing only manual discovery techniques or depending exclusively on monitoring tools can be hindered by the labor-intensive nature of DevOps endeavors or the associated implementation costs. With the rising complexity of storage environments, managing them can quickly become overwhelming.

This is where Lucidity's Storage Audit solution can help. With just a click of a button and automating the identification solution, Lucidity offers the following insights.

  • Overall disk spend: Know your current disk spend, how much your optimized bill should be, and how you can save up to 70% on storage costs.
  • Disk wastage: Discover how much of our disk is idle/unused and overprovisioned and understand how to eliminate them.
  • Disk downtime risk: Know the possibility of a potential downtime before it can happen to eliminate any financial or reputational damage.

With Lucidity Storage Audit, you get the following advantage.

  • Automated process: Eliminate the need for manual efforts and cumbersome monitoring tools with Lucidity Storage Audit. This tool simplifies auditing through a user-friendly, pre-configured executable tool.
  • Comprehensive understanding: Effortlessly grasp the health and utilization of your disks with Lucidity Storage Audit. It provides valuable insights that enable you to optimize spending and proactively prevent downtime by offering clear visibility into your storage environment.
  • Utilization optimization: Effectively analyze storage utilization percentages and disk sizes with Lucidity Audit. This information lets you make well-informed decisions to improve resource allocation and maximize efficiency.

Lucidity Block Storage Auto-Scaler

Automating scaling has become the need of the hour. This is because the traditional approaches to scaling storage resources often lead to inefficient resource allocation. This can manifest in excessive provisioning, wasting valuable resources, or inadequate provisioning, leading to performance bottlenecks.

This is where Lucidity Block Storage Auto-Scaler can help. An innovative cloud storage solution eliminates the challenges associated with traditional storage scaling methods.

Lucidity Block Storage Auto-Scaler has the following features.

  • Highly responsive: When faced with sudden traffic or workload spikes, the Block Storage Auto-Scaler swiftly responds by providing additional storage capacity. With expansion capabilities that only take one minute, you can efficiently handle unexpected surges without disruption. This rapid response ensures you always have the resources to manage high demands effectively.
  • No impact on performance: The Lucidity solution is intricately designed to minimize its impact on your instance's resources, including CPU and RAM usage, during onboarding. Our highly optimized Lucidity agent is designed to consume only a tiny portion of your resources, consistently operating at less than 2% CPU and RAM utilization. This intentional design choice ensures that your workload within the instance remains unaffected, enabling you to concentrate on your tasks without any disruptions.

With Lucidty Block Storage Auto-Scaler, you get the following benefits.

Benefits of Lucidity for block storage optimization
  • Automated expansion and shrinkage of storage resources: The Lucidity Block Storage Auto-Scaler efficiently and autonomously manages disk scaling in a concise timeframe of just 90 seconds. This effectively streamlines the handling of extensive data volumes with great ease. 
    Unlike conventional block storage volumes, which face limitations of approximately ~8GB per minute (equivalent to 125MB/sec) when using Standard block storage, our Block Storage Auto-Scaler is skillfully crafted to maintain a robust buffer. This enables it to seamlessly navigate unexpected data surges without surpassing the imposed block storage throughput limit.
  • Storage cost reduction of up to 70%: Cloud service providers charge based on how much resources are provisioned. Since the possibility of overprovisioning is eliminated with Lucidity Block Storage Auto-Scaler, you no longer have to pay for the resources you are not using. This way, you can save significantly on the storage-related expense.

If you want an estimate of how much you will save on storage-related costs, you can check the same in the ROI Calculator. Choose your preferred cloud provider, be it Azure or AWS, and provide the necessary information about your monthly or annual spending and specifics regarding disk utilization and growth rate. 

Lucidity ROI calculator to determine potential saving in cloud spend
  • Zero downtime: The manual provisioning process involves processes that lead to downtime. But this is not the case with Lucidity Block Storage Auto-Scaler. Since Block Storage acts within minutes of fluctuating storage space requirements, downtime is impossible. 
    Moreover, Lucidity offers a "Create Policy" feature under which you can customize the policy to cater to different situations based on the use case and buffer or loading time. The Block Storage Auto-Scaler will automatically scale the storage resources per the set policies. 
Lucidity custom policy feature for eliminating downtime and maintaing storage buffer

If you are looking for a comprehensive storage identification solution that can also help with your storage overprovisioning issues, then you should reach out to Lucidity for a demo, and we will help you understand how automation can simplify the whole process. 

Coming to deciding between Datadog and Splunk; they are both valuable tools for managing and monitoring logs, each with strengths that cater to different needs. With its strong search capabilities and advanced analytics, Splunk stands out, making it ideal for organizations relying on thorough data analysis. On the other hand, Datadog excels in infrastructure monitoring and offers a user-friendly interface.

When choosing between these options, it is crucial to carefully assess your organization's specific requirements, scalability needs, integration preferences, and budget limitations. By contemplating these factors, you can make an informed decision that aligns seamlessly with your objectives and maximizes the value derived from your chosen solution.

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