From Doodle to Pitbull: The Prolific Nature of the DataDog Portfolio

This article was written by Frankenstein Tools Technology Writing Staff

Not since Splunk’s market debut has any vendor impacted the observability space in such a meaningful way. Datadog has emerged as the leading monitoring and operations analytics platform for enterprises looking for a consolidated approach to monitoring, security, and IT operations. DataDog boasts a comprehensive suite of tools designed to give organizations deep visibility into their infrastructure, applications, and digital services. 

With an impressive multi-year roadmap, the DataDog product team adds new major functionality to the platform quarterly- regularly expanding into new market segments outside of traditional observability and adding new user persona-types and use cases. With clear ambitions of growing beyond observability and security point solutions, DataDog seems to be on a collision course with larger platform providers including ServiceNOW.

This article will help clarify their go-to-market approach and explores the various products offered within the Datadog platform, their use stories, the value they bring to businesses, and examples of companies leveraging these capabilities.

Infrastructure Monitoring for Enterprise Compute

DataDog originally made their bones as a provider of low-cost infrastructure metrics targeted at cloud environments. With a reasonable cost model and alignment to hyperscaler cloud environments, they were a darling of DevOps and SRE teams who were looking for “pay as you go models” versus expensive and rigid licensing models by incumbents such as AppDynamics, New Relic and Splunk. 

Fast forward a decade, they now boast over four hundred plus integrations to both popular and esoteric technology platforms, Datadog allows organizations to visualize and monitor their infrastructure, including servers, cloud instances, and containers. It provides real-time metrics, logs, and traces, which are crucial for diagnosing performance issues and ensuring system reliability. 

A versatile platform, DataDog boasts a robust platform which is well suited for infrastructure monitoring across a variety of scenarios including traditional VM compute, containerized environments, and cloud- native. Companies such as Samsung and AirBnB leverage DataDog for complex infrastructure monitoring.

Application Performance Monitoring

DataDog’s application performance management (APM) platform has evolved over the years to emerge as a top-tier player in this highly competitive space. DataDog’s only credible competitor for enterprise-caliber deployments would be Dynatrace, having surpassed New Relic and AppDynamics a few years ago.

Stacked with all the expected APM modules including digital experience management (real user monitoring, session replay and synthetics) and end-to-end application transaction tracing; it capably helps organizations understand performance of applications, identify bottlenecks, and optimize user experiences. Providing end-to-end visibility from frontend devices to backend services (including database and storage tiers), the platform aids in diagnostics at every tier. Recent advances in out-of-the-box analytics provides digital-marketing focused team members an experience like Quantum Metric, Glassbox or FullStory.

Limitations for DataDog’s APM include gaps in support and qualifications for legacy and on-premises focused application runtimes. This limits enterprises still operating critical on-premises or hybrid workloads from consolidating total APM monitoring onto DataDog as they can with Dynatrace. 

Major eCommerce focused customers include Peloton and Shopify who manage key elements of their application stack with DataDog APM. 

Log Management

DataDog’s entry into the log management space has been one of their most consequential moves which disrupted the observability and security incident and event management markets. Major players like Splunk and SumoLogic experienced customer erosion and new project loss at the hands of DataDog, especially in cloud environments across both operations and security.

DataDog’s Log Management provides customers a powerful platform to collect, analyze, and search logs from any source in real-time. It helps in debugging, auditing, compliance and integrates well with other DataDog modules for security and observability providing a unified view for a plethora of stakeholders across the enterprise.

Boasting a robust, scalable, and centralized architecture complete with an enterprise-grade log pipeline (via their acquisition of Vector.Dev), DataDog aggregates logs from various sources into a single platform. DataDog is battle-tested in its ability to facilitate quick identification and resolution of issues in high-intensity environments and is powerful in aiding and maintaining logs for audit trails and regulatory compliance.

Companies leveraging Datadog for centralized log management include The Washington Post and Zillow and helps ensure the reliability of their digital platforms.

Synthetic Monitoring

Synthetic Monitoring simulates user interactions with applications to ensure they are performing as expected. It helps in proactively detecting issues before they impact real users.

A staple of any sound monitoring strategy, synthetic monitoring continues to reaffirm its value to the enterprise despite rapidly changing application architectures. The need to proactively validate production system’s performance, availability and usability continue to escalate as organizations migrate capabilities into the cloud, mobile, IoT, robotics and AI. 

DataDog provides a synthetic monitoring platform in line with major competitors including Dynatrace and New Relic (Cisco’s ThousandEyes Synthetics continues to maintain market leadership status, however, limited value-add integrations with AppDynamics have allowed DataDog and others to “catch-up”). Versatile and integrated within the larger observability platform, DataDog’s synthetic monitoring enables customers to consolidate proactive and reactive observability tactics to safeguard user experience.

Several major organizations driving value from DataDog’s synthetic platform include Squarespace who mirrors complex user-based scenarios in their web-site building platform - spanning thousands of customer generated permutations requiring monitoring. eBay is another major organization which leverages synthetic tests to maintain high availability and performance of its online marketplace.

Network Performance Monitoring (NPM)

DataDog provides network performance monitoring (NPM) capabilities with detailed insights around network traffic patterns and performance. It helps in diagnosing network-related issues and optimizing network infrastructure.

DataDog’s NPM solution is not a full-featured network monitoring platform in the vein of SolarWinds or PRTG which are well-capable of handling high-intensity environments and are tailored to network engineering and operations stakeholders. However, with the ability to integrate network telemetry alongside application and infrastructure monitoring delivers a unified experience to drive operational efficiencies for incident response.

With insights into the performance, health, and dependencies of an organization’s network infrastructure, DataDog enables real-time monitoring of network traffic across cloud and on-premises environments, making a good choice for customers who will be in hybrid mode for the foreseeable future. 

Key capabilities include identifying latency, packet loss, and bottlenecks that could impact application performance and contextualizes network data with intuitive maps and topology views, showing how different devices and services are connected. By capturing traffic metrics at the packet level, it provides detailed insights into network behavior, allowing for proactive network management and optimization.

Several notable enterprises using DataDog include Dropbox and Cisco which manage parts of their global network operations using the NPM module.

Security Monitoring

The vast majority of DataDog customers are primarily using it for observability and IT monitoring use cases. With that said, they are an emerging provider of information security and cyber defense capabilities. With the integration of observability with security monitoring, DataDog is helping customers detect threats across various operating dimensions and support compliance with integration points to existing security tools to enhance security insights.

DataDog’s Cloud Security Management product focuses on identifying and mitigating vulnerabilities detected in the monitored environment including misconfigurations, and threats in cloud workloads and infrastructure. It integrates with DataDog's observability platform, enabling detection and response to security issues across the entire stack, furthering security integration across the development and operations lifecycle. 

The Application Security Management module helps customers protect applications at runtime with real-time detection and attack mitigation as they occur. Well suited for web applications with its ability to manage against threats like SQL injection, cross-site scripting (XSS), and other exploited vulnerabilities found in web architectures. With the integration of MELT (metrics, events, logs, and traces) telemetry with security signals, DataDog provides enterprises the ability to correlate security events with systems performance. 

Mature enterprises will benefit greatly from this approach to cloud-native security that aligns with modern DevOps practices and are particularly beneficial for organizations looking to enhance their security posture while maintaining the agility of continuous integration and continuous deployment (CI/CD) pipelines.

Large Language Model (LLM) Observability & Monitoring

DataDog recently extended general availability for their first iteration of LLM-based monitoring. Datadog LLM Observability provides end-to-end traceability of large-language model chains by helping customers isolate on input-output, deep-dive into errors. Token usage and latency is visualized at each step, along with robust output quality and security evaluations. 

DataDog correlates LLM traces with application performance and fuses cluster visualization to identify drifting and response deviations, Datadog enables rapid resolution of issues to help scale AI applications. DataDog provides customers with capabilities ranging from error-detection, performance management and safety-risk mitigation such as toxicity and prompt injection.

Conclusion

DataDog is redefining the modern, consolidated observability and IT operations platform. If not by reimagining monitoring, but by integrating traditionally disparate capabilities and blurring the swim lanes which for so long were left untouched. As time has passed, DataDog has steadily increased their pricing to be one of the, if not most expensive overall platforms to operate in the enterprise cloud. 

As Zakir Mohammed - who leads a team Digital Automation Engineering at Toyota Motor North America says, “Datadog’s precision monitoring capabilities and AI/ML is a game changer for us . . . Before using Watchdog, we were very reactive. Now we can be much more proactive.”

By offering real-time visibility, advanced monitoring, security management and comprehensive analytics- Datadog is enabling their customers to seamlessly traverse between core observability disciplines of infrastructure, application performance management, log analytics, incident response automation and even IT services case management.

To understand if DataDog makes sense for your organization, consider whether your organization can benefit from a consolidated observability platform which offers many strong capabilities albeit not always the “best of breed” in those respective segments (such as APM where Dynatrace currently is the space overall leader). Another consideration would be whether your environment still has critical legacy workloads that may not be supported by DataDog’s more cloud-relevant qualifications. Finally, ensure that you are ready to spend too-dollar, DataDog doesn’t come cheap.