Datadog Vs Dynatrace: In-Depth Comparison

System monitoring is essential for ensuring IT infrastructure's overall well-being, effectiveness, and protection. Its significance lies in enabling organizations to enhance operational efficiency, anticipate and address problems in advance, and make well-informed decisions that align with business objectives.

Datadog and Dynatrace are prominent tools widely adopted by businesses and organizations to monitor and optimize their development and operational processes. While both platforms aim to improve visibility into IT environments, they differentiate themselves through unique capabilities and features that cater to the specific requirements of their respective users.

In this blog, we will provide a detailed Datadog vs Dynatrace comparison based on specific criteria to help you make an informed decision.

The growing reliance on IT infrastructure necessitates a robust monitoring tool to closely monitor resource utilization, accessibility, execution, and other factors to improve business agility and scalability. This is where a system monitoring tool can be of help. A system monitoring tool is designed to deliver reliable and high-performance services to the users and offers the following benefits:

• Real-time visibility
• Comprehensive data visualization
• Security monitoring
• Cost optimization
• Capacity planning
• Issue detection and troubleshooting
• Performance optimization

What Is Datadog?

Datadog is a cloud-based monitoring and analytics platform that provides instant visibility into various aspects of IT environments and assists as a cloud cost management tool. It offers real-time monitoring of infrastructure, applications, logs, and user experience, leveraging advanced technologies like machine learning and analytics. With its ability to deliver actionable insights and performance metrics, Datadog empowers IT teams to identify and address issues quickly, ensuring smooth operational efficiency.

Features Of Datadog

Following are some of the features of Datadog.

• Root cause analysis: Monitoring tools utilize root-cause analysis as a pivotal technique to identify the exact cause of problems and streamline the troubleshooting process. Datadog, specifically, employs advanced methods like metric correlations to unveil anomalies within the system. 
  One of the standout features of Datadog is its automated fault detection and trend identification, which not only accelerates issue identification but also improves the overall efficiency of the monitoring process.

• Tracing and APM: Datadog's tracing feature strengthens its already powerful log and metric monitoring capabilities by incorporating tags. These pre-set tag choices are designed to streamline the entire monitoring process. 
  By utilizing Datadog's application performance monitoring, you can transcend mere observation and overcome obstacles in achieving proactive management.

• Custom dashboards: Datadog's dashboard is fully customizable, allowing users to have unparalleled data visibility, surpassing the capabilities of most data visualization platforms. This unique feature empowers users to focus on the logs and metrics crucial to their requirements.

Pros

• Users are provided with the ability to monitor the real-time health of their service and ensure flawless functioning. Furthermore, they can effortlessly incorporate thresholds and alerts for these metrics, ultimately relaying them to Slack and pager duty platforms.
• The platform offers real-time insights into our cloud and system infrastructure, providing valuable information. It includes a watchdog feature equipped with AI and ML capabilities to promptly identify any errors in the company's infrastructure.

Cons

• Certain integration and connection documents related to the setup process may be outdated and challenging for individuals new to the system. Throughout the setup procedure, it will be necessary to reach out to the support team on multiple occasions for assistance.
• The absence of comprehensive metrics and limited ability to analyze data at a detailed level, combined with potential delays in data updates, pose certain limitations.

What Is Dynatrace?

Dynatrace is an application monitoring solution that guarantees uninterrupted system availability for customers. Recognized for its exceptional speed and dependability, this tool stays up-to-date with the latest technological progress through seamless integration with digital applications. It meticulously traces and records each transaction, offering a comprehensive overview of its path from the browser to the database.

Features Of Dynatrace

Some of the features of Dynatrace  are:

• Alerts: Dynatrace allows users to create alerting profiles with various filters (such as management zone, severity, and event) for a personalized experience. Users can access these profiles through the Settings menu.
• Application monitoring with AI technology: By leveraging Dynatrace's AI technology, users can quickly resolve application bugs and access automatic and practical cloud app solutions. This reduces the time spent on manual issue resolution and provides real-time tracking capabilities similar to Datadog.
• Reporting tools and analytics: Dynatrace offers a wide range of tools, allowing users to share every report via email. Collaborating with team members becomes easier as users can easily share URLs. The system provides overall application, service, and infrastructure scores, enabling users to improve their applications and websites by analyzing each score's fault proportions and failure rates.

Pros 

• The product performs exceptionally well and functions precisely as specified. You can effortlessly construct a comprehensive dashboard using system data, enabling you to gain precise insights into your systems.
• One key advantage is the seamless integration capability with other testing tools. The inclusion of Dynatrace's Davis AI and its user-friendly interface greatly aids in accurately assessing the performance of our applications.

Cons

• Learning how to optimize the use of DDU cost and DQL queries cost can be both risky and expensive, making it imperative to approach it with caution. While trial and error is an effective learning method, it can significantly drain resources when working with Dynatrace.
• Despite setting the alert monitoring threshold, companies sometimes encounter inaccurate alerts while the application is in operation. The extensive functionality provided by Dynatrace can be overwhelming for a newcomer or someone with limited experience, posing challenges akin to crossing a vast ocean.

Datadog Vs Dynatrace: 8 Key Differeces

Let's deep dive into Datadog vs Dynatrace based on 8 parameters. Now that we have obtained a basic understanding of Datadog and Dynatrace, let's examine the aspects that differentiate them.

1. Deployment

Ease of deployment plays a crucial role in ensuring rapid implementation and enabling continuous improvement in an application. Datadog and Dynatrace have different approaches to deployment. Let us look Datadog vs Dynatrace based on their deployment.

Datadog

• Options: Datadog can be deployed in various cloud environments, making it one of the most proficient multi-cloud cost management tools.
• Flexibility: Users can choose the most suitable deployment solution for their specific needs, considering Datadog's highly adaptable nature.
• Complexity: The initial setup of Datadog can be intricate, offering advantages and challenges.
• Customization: A tailored solution with Datadog demands extensive groundwork, patience, and expertise.

Dynatrace

• Approach: Dynatrace emphasizes a Software as a Service (SaaS) approach, offering a cloud-centric solution.
• Simplicity: Deploying Dynatrace is a straightforward process and can be deployed in the cloud, on-premise, and as a SaaS service.
• Onboarding assistance: The initial setup of Dynatrace provides adequate assistance to facilitate onboarding.
• Agent deployment: Three code snippets provided by Dynatrace need to be executed to deploy the agent.

2. Monitoring capabilities

Datadog and Dynatrace are prominent providers of comprehensive monitoring solutions, offering businesses valuable real-time insights to efficiently manage their systems and applications. These platforms enable organizations to optimize operational efficiency and performance by leveraging advanced monitoring functionalities. Let's see Datadog vs Dynatrace based on their monitoring capabilities.

Datadog

• Datadog excels in monitoring capabilities across various areas such as infrastructure, application performance, network, and log management. 

Dynatrace 

• It specializes in delivering a holistic monitoring experience designed explicitly for cloud-native environments. 
• Its features include Application Performance Management (APM), infrastructure monitoring, and digital experience monitoring. 

3. Incident Management

Proper incident management is paramount when utilizing a monitoring tool. It serves the critical purpose of detecting issues early, reducing downtime, enhancing service reliability, minimizing business impact, optimizing resource utilization, performing root cause analysis, and responding to security incidents. Let's find out Datadog vs Dynatrace based on their incident management capabilities.

Datadog Incident Management Tool Overview

• Tool overview: Datadog offers a robust Incident Management tool encompassing every aspect of incident handling and resolution.
• Scope: This tool covers incident declaration, management, and resolution in a comprehensive manner. However, it does not include on-call schedules or incident communication through status pages.
• Simplicity: Datadog simplifies the entire incident-handling process by providing an easy way for users to create, classify, and track incidents.
• Responsibility assignment: Users can assign responsibilities to individuals and teams, ensuring a streamlined resolution process for all involved parties.
• Communication channels: Basic email and Slack notifications keep stakeholders informed throughout the incident lifecycle.

Dynatrace Incident Management Tool Overview

• Alerting tools: Dynatrace offers limited pre-configured alerting tools and fundamental incident management capabilities.
• Information access: Users can access information related to problems via the intuitive problems user interface.
• Tailored monitoring: The monitoring preferences can be personalized to fit specific event categories, which include monitoring unavailable, availability, error, slowdown, resource, and Custom.
• Monitoring excellence: While excelling in various monitoring aspects, Dynatrace might necessitate additional customization to provide a comprehensive incident management solution.

4. Ease Of Use And User Interface

The significance of having an easy-to-use and user-friendly interface in a monitoring tool goes beyond convenience. It leads to faster acceptance, a shorter learning process, streamlined training, heightened productivity, improved decision-making, fewer mistakes, and accessibility for non-technical users.

Datadog

• Intuitive user interface: Datadog sets itself apart by providing a user-friendly interface specifically designed to promote ease of use and accessibility for all users.
• Seamless navigation: When it comes to exploring the wide range of features and dashboards offered by Datadog, the navigation experience is exceptionally smooth, effectively enhancing the platform's overall usability.
• Streamlined data monitoring: Datadog goes above and beyond to simplify the process of monitoring and analyzing data, employing intuitive visualizations that ensure an efficient and user-friendly experience.

Dynatrace

Dynatrace offers a higher level of comprehensiveness and an extensive range of features. However, effectively utilizing its capabilities may demand a greater degree of technical proficiency due to its inherent complexity. However, after gaining proficiency in using the tool, users have reported that the interface is indeed uncomplicated and easy to navigate.

5. Automation

The importance of automation in a monitoring tool rests in its capacity to deliver immediate detection, facilitate proactive incident response, efficiently handle scalability, optimize resource usage, guarantee continual monitoring, and assist in identifying root causes. Let's see Datadog vs Dynatrace based on their automation capabilities.

Datadog Automation Capabilities

• Comprehensive DevOps automation: Enables access to a suite of automation tools catering to a broad spectrum of DevOps functions.
• Effortless log exploration: Streamlines the identification and exploration of logs for efficient analysis.
• Automated monitoring setup: Simplifies the monitoring setup through automated configuration of checks and integrations.
• Seamless integration with automation tools: Ensures smooth connectivity between the solution and various automation tools, including configuration management and orchestration platforms.
• Advanced automation for DevOps monitoring: Provides extensive automation functionalities to comprehensively monitor the performance of all DevOps tools.
• End-to-end testing automation: Sustains application performance by automating end-to-end testing processes, ensuring robust and reliable operations.
• Swift issue detection and resolution: Swiftly detects and resolves issues, leveraging the power of automated monitoring features, minimizing downtime, and enhancing system reliability.

Dynatrace Automation Capabilities

• Organizational performance monitoring: Monitors and analyzes the performance of organizational processes and DevOps workflows for enhanced operational insights.
• Auto-discovery and instrumentation activation: Automatically discovers all processes on the host, activating instrumentation and mapping relevant data for comprehensive monitoring.
• Automatic data dependency mapping: Enables automatic mapping of data dependencies, providing real-time visualization of relationships and dependencies within the system.
• Proactive problem identification: Proactively identifies issues based on typical performance patterns, alerting users to potential problems before they impact operations.
• Root-cause analysis and automated remediation: Performs root-cause analysis and offers remediation capabilities for incidents, facilitating automated issue resolution and minimizing downtime.

6. Synchronized Dashboards

Dashboards are crucial in monitoring tools because they can present data visually, deliver real-time insights, and customize different user roles. Moreover, it aids centralized monitoring, enables analysis of historical data, improves user engagement, visualizes alerts, monitors performance, and generates executive reports.

Datadog

• Interactive and personalized dashboards: Datadog provides dynamic dashboards that offer interactivity and personalization, allowing users to customize visuals and access real-time data effortlessly.
• Effortless data combination: The platform's dashboards enable seamless data combination, ensuring a smooth and intuitive user experience for effective analysis.
• Freedom to create custom dashboards: Users enjoy creating and automatically generating their custom dashboards within the Datadog system.
• Advanced analysis capabilities: Datadog facilitates advanced analysis through transformations, custom functions, and correlations, empowering users with sophisticated data analysis tools.
• Flexible data manipulation: Users can easily manipulate data using built-in formulas or code editing options, ensuring flexibility in the analysis process.
• Empowered customization features: Datadog's customization features empower users to personalize views and visualizations according to their specific requirements.
• Diverse display options: Besides graphing and mapping, Datadog offers diverse display options, such as alerts and value highlighting, enhancing the overall visualization experience.
• User-friendly and resilient platform: Datadog delivers a user-friendly and resilient platform designed for efficient data analysis and visualization, ensuring a positive and productive user experience.

Dynatrace

• Focused monitoring data: Dynatrace dashboards provide concentrated monitoring data, ensuring quick access to relevant information for effective decision-making.
• Configurable charts and tiles: Users have the flexibility to tailor the dashboard layout to their specific needs through configurable charts and tiles, enhancing customization.
• Performance metrics display: Components on the dashboards display performance metrics with settings for sharing, management zones, and customizable timeframes, offering comprehensive insights.
• Customization options: Dynatrace offers customization options that allow users to visualize metrics and reports on monitored entities, aligning the dashboard with specific monitoring requirements.
• Connectivity and collaboration: The platform facilitates easy connectivity and collaboration between organizational teams through customizable dashboards, fostering efficient information sharing.

7. Security

The importance of employing a reliable monitoring tool resides in safeguarding data, guaranteeing confidentiality, upholding the integrity of monitoring details, thwarting internal and external threats, and fulfilling compliance obligations. Moreover, a secure system helps mitigate cybersecurity hazards, fortify communication channels, avert data breaches, ensure security during incident response, sustain uninterrupted availability, and establish trust in the monitoring system. Let's find out Datadog vs Dynatrace based on their security.

Datadog

• Prioritized security: Datadog places utmost importance on security and offers a comprehensive range of robust features to ensure the protection of user data.
• Multi-factor authentication: An additional layer of security is implemented through multi-factor authentication, adding an extra barrier against unauthorized access.
• Encryption: Datadog utilizes encryption for data at rest and in transit, reinforcing the confidentiality and integrity of data throughout its entire lifecycle.
• Role-Based Access Control (RBAC): To enhance security and prevent unauthorized access, Datadog employs role-based access control, which grants organizations precise control over user permissions.
• Compliance Standards: The platform adheres to essential security standards, including SOC 2 and HIPAA, demonstrating a solid commitment to maintaining a safe and reliable environment for managing confidential data.

Dynatrace

• Emphasis on security: Dynatrace prioritizes implementing stringent security measures to ensure optimal data protection.
• Cutting-edge techniques: The security measures encompass robust data encryption, access control protocols, and comprehensive audit trails. These advanced techniques establish a resilient defense against potential security threats.
• Security-first approach: The platform is deliberately designed with a security-first approach, ensuring users that their data is safeguarded at every stage of its lifecycle.
• Compliance standards: Dynatrace upholds compliance with essential standards, including GDPR, HIPAA, and SOC 2.

8. Pricing

Datadog has implemented a decentralized pricing system for its products, each with its pricing logic. For instance, the APM & Continuous Profiler product follows a per-host pricing structure. Similarly, Infrastructure Monitoring, a requirement for APM, also follows the same per-host pricing approach. On the other hand, features like Log Management are charged based on the amount of ingested data, while Incident Management incurs charges per contributing user.

Dynatrace offers a flexible pricing structure that caters to full-stack and individual product models. The Full-stack level, starting at $74 per 8GB per host, comprises a comprehensive range of services. It is essential to mention that specific tools like Application Security are available as add-ons and are not included in the standard pricing.

Lucidity: Comprehensive Storage Idle/Unused Resource Identification and Auto-Scaling Solution

While Datadog and Dynatrace offer comprehensive observability,  providing insight on storage metrics like overprovisioned and idle/unused resources, they come with disadvantages. Implementing these tools to access the entire cloud infrastructure to understand resource usage and wastage is costly. Moreover, an indefinite number of instances must be monitored, which means it will also be time-consuming. 

This is where Lucidity steps in seamlessly at this stage, providing a comprehensive solution to address and resolve the challenges unearthed by these monitoring tools. 

With Lucidity, you can streamline and enhance your storage provisioning process, ensuring a swift and efficient resolution to maintain the optimal performance of your systems. Harnessing cloud automation, Lucidity offers two cloud cost automation solutions to decrease costs associated with storage usage:

Lucidity Storage Audit- For Identification of Overprovisioned and Idle/Unused Storage Resources

Lucidity Block Storage Auto-Scaler- Automated Scaling Of Storage Resources For Eliminating Overprovisioning of Storage Resources

Why do you need a separate tool for storage?

Storage is one of the significant contributors to the overall cloud bill. This is stated in the Virtana investigation titled, State of Hybrid Cloud Storage, where 94% of the respondents said their cloud cost was increasing alarmingly. Another 54% said their cloud cost grew faster than the overall cloud bill.

We decided to dig deeper to understand the impact of storage resources on the overall cloud bill in a slightly better way. We conducted an independent study on over 100 enterprises using cloud service providers like AWS, Azure, or GCP, and we discovered that.

• On average, approximately 40% of cloud expenses were attributed to storage-related costs.
• Block storage, i.e., AWS EBS, Azure Managed Disk, and GCP Persistent Disks, were responsible for a significant portion of the overall cloud bill. 
• Block Storage disk utilization was very low for root volumes, application disks, and self-hosted databases.
• Organizations tend to overestimate growth and overprovision despite facing at least one downtime per quarter.

This is why, instead of optimizing the storage resources, organizations prefer overprovisioning them. However, it is essential to understand the reason behind the deliberation, which are- 

• Optimizing storage demands time and effort: Storage optimization involves multiple steps, such as selecting the appropriate storage class, implementing policies for managing the data lifecycle, and consistently monitoring and adjusting storage based on the specific requirements.
  To effectively meet the needs of applications, DevOps teams must assess their current storage requirements, analyze data access patterns, and align storage resources accordingly. Careful planning and consistent maintenance are essential to implementing cost-effective storage solutions, managing data redundancy, and ensuring efficient data retrieval.
  Additionally, DevOps teams must stay informed about advancements and improvements offered by cloud providers, as these can potentially optimize storage efficiency by utilizing new features. These processes require the DevOps team to allocate time and effort away from their primary tasks and reduce productivity.

• Cost-intensive: Integrating storage optimization strategies typically necessitates investing in specialized tools, technologies, and knowledge. Many organizations may view these initial expenses as a barrier, particularly when working within constrained budgets or focusing on immediate cost reductions. Moreover, deploying monitoring tools across the entire cloud infrastructure can get costly; hence, organizations deploy these tools to the production environment, resulting in limited visibility.

• Custom tool development: The insufficiency of features offered by Cloud Service Providers (CSP) prompts the need to develop a tailor-made solution for optimizing storage. However, this custom tool development entails substantially greater DevOps efforts and time commitment.

However, this overprovisioning leads to resource inefficiency and significant cloud costs since you are paying for the resources that you are not using. There is an urgent need to reduce this hidden cloud cost, and one of the effective ways to do it is through automating the scaling of storage resources. 

This is where Lucidity steps in with its automated storage audit and scaling solutions.

Lucidity Storage Audit- For Identification of Storage Resource Wastage

There is no doubt that monitoring tools like those mentioned above can help identify anomalies. However, monitoring tools are frequently constrained due to the arduous tasks involved in the DevOps process or the additional costs linked to their implementation. In an environment characterized by ever-increasing storage system intricacies, handling these tools can quickly become daunting and complex. 

Moreover, purchasing an additional monitoring tool would be a significant investment of money. Additionally, these tools are powered by agents, which would cause installation hassle, increased complexity, and increased resource requirement, which adds burden to the existing system, making monitoring cumbersome.

The Lucidity Storage Audit solves these challenges effortlessly with its free and agentless solution, automating the optimization process using a user-friendly and readily available tool. Since it is agentless, it does not require any additional software installation to identify the idle/unused and overprovisioned resources for the system they are conducting an audit on. This tool simplifies disk health and usage comprehension, making allocating resources effective and preventing downtime easier. With Lucidity Storage Audit, you get the following insights:

Holistic Disk Expenditure Optimization:

• Acquire a thorough comprehension of your current expenses related to disk usage.
• Discover strategies to reduce costs significantly, up to an impressive 70%.

Analysis of Disk Utilization:

• Identify sources of resource wastage, covering overprovisioned and idle storage volumes.
• Gain insights on eliminating inefficiencies to foster a more optimized utilization of resources.

Mitigation of Disk Downtime Risks: Identify potential downtimes, minimizing financial and reputational risks.

Features of Lucidity Storage Audit

• Lucidity Audit utilizes CSP's internal service to collect necessary storage metadata, such as storage utilization percentage and disk size. 
• The tool prioritizes security by strictly avoiding customer access to Personally Identifiable Information (PII) or sensitive data.
• Lucidity Audit guarantees that the auditing process does not disturb the customer's cloud environment or resources. This commitment to zero impact ensures a seamless and secure optimization experience without interfering with ongoing operations.

Once idle or unused resources are identified, take decisive actions such as deletion. If the resources are overprovisioned, you can rightzie them based on your specific requirements.

Lucidity Block Storage Auto-Scaler - Automated Scaling Of Storage Resources For Eliminating Overprovisioned & Underutilized/Idle Resources

Unlike Datadog and Dynatrace, which only offer monitoring services, Lucidity goes one step ahead and offers auto-scaling for block storage with its Lucidity Block Storage Auto-Scaler.

Lucidity Block Storage Auto-Scaler is the industry's first autonomous storage orchestration solution designed to automate the shrinkage and expansion process of storage resources, regardless of the fluctuations in the requirements. Once the overprovisioned and idle/unused storage resources are identified, Lucidity's Block Storage Auto-Scaler is deployed, offering the following benefits:

• Automated shrinkage and expansion: Built with precision in mind, Lucidity Auto-Scaler effortlessly adjusts disk scaling with remarkable efficiency, completing the process within an impressive 90 seconds. This capability guarantees streamlined coordination of large datasets with utmost ease.
  Including a strategic buffer mechanism in Lucidity Auto-Scaler demonstrates its meticulous design, specifically crafted to manage unexpected spikes in data adeptly. This forward-thinking approach ensures that the imposed block storage throughput limit remains intact, augmenting the scalability and reliability of your storage infrastructure.

• Storage cost savings by 70%: By automating the shrinkage and expansion of storage resources, Lucidity Block Storage Auto-Scaler cuts down the expense associated with storage resource usage by 70%. This is because, with Lucidity Block Storage Auto-Scaler, you no longer have to worry about paying for unused resources.
• No downtime: The manual provisioning process involved significant manual intervention, which would demand downtime. This is not the case with Lucidty Block storage Auto-Scaler. As soon as the demand for the storage resource changes, Lucidity Block Storage Auto-Scales expands or shrinks the resources within a few minutes, eliminating the possibility of downtime or performance lags.

Tailor Monitoring Solutions To Organization Needs By Choosing Between Datadog and Dynatrace

Deciding between Dynatrace and Datadog depends on the unique needs and requirements of the organization. For businesses that place importance on advanced AI and automation features in cloud-native and hybrid environments, Dynatrace offers exceptional value. On the other hand, organizations that require comprehensive monitoring and analytics capabilities for various IT systems and applications may find Datadog the most suitable option.

If you are facing challenges with monitoring tools or struggling to determine the precise percentage of total costs related to storage, it is the right moment to consider the advantages of Lucidity's automated storage audit. Reach out for a personalized demonstration with us to experience firsthand how our cutting-edge automation simplifies cloud cost management and maximizes storage savings. Let Lucidity enhance your operations with efficiency and clarity in maneuvering through the complexities of your cloud expenses.