Datadog and Sentry compete in performance monitoring and error tracking software. Datadog appears to have an edge due to its extensive integrations and feature-rich ecosystem.
Features: Datadog offers centralized monitoring with customizable dashboards and real-time visualizations. It integrates seamlessly with AWS and other services, providing robust anomaly detection. Sentry focuses on error tracking, efficiently logging error contexts for quick debugging and performance monitoring, catering specifically to software teams.
Room for Improvement: Datadog users desire better query flexibility, more consistent UI design, and lower memory utilization. Sentry users want enhanced custom metrics, broader integration capabilities, and improved analytics.
Ease of Deployment and Customer Service: Datadog is praised for its flexibility across cloud environments and generally responsive customer service, though there are reports of inconsistencies in issue resolution speeds. Sentry offers cloud and on-premises deployments with quick and helpful support, although improved training for representatives is recommended.
Pricing and ROI: Datadog's pricing is seen as expensive, yet it delivers significant ROI when fully utilized. The pay-as-you-use model can lead to cost transparency issues without careful management. Sentry offers a more flexible pricing model with a free plan for early-stage projects, considered justified given the value of its features.
It has been easy to use and configure across multiple systems, each having several environments.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
There should be a clearer view of the expenses.
Integrations or single sign-on capability with Microsoft would be beneficial for securing all assets.
The setup cost for Datadog is more than $100.
Compared to New Relic, it provides the necessary features at a cheaper cost, especially since we moved infrastructure monitoring to Azure.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
The technology itself is generally very useful.
Real-time error tracking helps our Quality Assurance team easily identify the root causes of problems or bugs and promptly inform the developers about specific issues.
At this time, I focus on finding and fixing bugs.
Datadog is a comprehensive cloud monitoring platform designed to track performance, availability, and log aggregation for cloud resources like AWS, ECS, and Kubernetes. It offers robust tools for creating dashboards, observing user behavior, alerting, telemetry, security monitoring, and synthetic testing.
Datadog supports full observability across cloud providers and environments, enabling troubleshooting, error detection, and performance analysis to maintain system reliability. It offers detailed visualization of servers, integrates seamlessly with cloud providers like AWS, and provides powerful out-of-the-box dashboards and log analytics. Despite its strengths, users often note the need for better integration with other solutions and improved application-level insights. Common challenges include a complex pricing model, setup difficulties, and navigation issues. Users frequently mention the need for clearer documentation, faster loading times, enhanced error traceability, and better log management.
What are the key features of Datadog?
What benefits and ROI should users look for in reviews?
Datadog is implemented across different industries, from tech companies monitoring cloud applications to finance sectors ensuring transactional systems' performance. E-commerce platforms use Datadog to track and visualize user behavior and system health, while healthcare organizations utilize it for maintaining secure, compliant environments. Every implementation assists teams in customizing monitoring solutions specific to their industry's requirements.
Sentry is a tool for monitoring web and application performance, tracking errors, processing request times, and managing user data access.
Developers integrate Sentry with web and application environments to capture front-end and back-end errors, utilize error logs, trace requests, and observe metrics without real-time production access. Sentry's use extends to monitoring internal applications, leveraging CyberArk PAM integration, deploying notifications, and detecting silent failures. Users benefit from detailed error and performance reports, contextual cause-stack information, and real-time breakdowns. There is room for improvement, as users desire refined integration and administrative settings, enhanced alert policies, and more customization in event metrics.
What are Sentry's most important features?Sentry is implemented across industries ranging from tech startups to large enterprises. These organizations use Sentry to enhance application reliability, track performance, and secure user data within protected environments. Integration with CyberArk PAM ensures secure deployment. Organizations find Sentry useful for monitoring internal applications, efficiently processing request times, and tracing changes in production without direct access.
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