Datadog and Sentry compete in the realm of application monitoring and error tracking. Datadog seems to have the upper hand due to its extensive feature set and extensive integration capabilities.
Features: Datadog offers sharable dashboards, integration capabilities, and APM features. It centralizes data effectively and provides actionable insights with an extensive list of integrations. Sentry specializes in real-time error tracking, which includes rich contextual information aiding in debugging, but lacks the broader feature set of Datadog.
Room for Improvement: Datadog could improve its performance with older metrics and simplify its pricing structure, in addition to offering better dashboard control. Sentry can enhance its integration capabilities, provide more detailed documentation, and offer more granular customizations.
Ease of Deployment and Customer Service: Datadog supports deployment across private, public, and hybrid clouds with generally responsive customer support, though response times can vary. Sentry's deployment is primarily in public and private clouds and is praised for quick customer support responses.
Pricing and ROI: Datadog's pricing is complex and demands careful cost monitoring, though its extensive features provide substantial ROI through operational efficiencies. Sentry features a straightforward and cost-effective pricing model, with open-source options and flexible paid plans offering good value, particularly noted for its cost-efficiency among competitors.
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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