Datadog and Elastic Observability compete in the monitoring and observability category. Datadog seems to have the upper hand with its ease of use and strong integration capabilities, whereas Elastic Observability stands out for its customization and cost-effectiveness.
Features: Datadog's remarkable features include sharable dashboards, robust integration with services like AWS and Docker, and extensive visualization tools for comprehensive monitoring. Elastic Observability excels in analytics and search functionality, offering a highly customizable platform that integrates well with various data sources and monitoring systems.
Room for Improvement: Datadog users often encounter challenges with the complexity of customizing dashboards and managing high costs, especially for logs and APM features. They also seek more advanced integrations and improved documentation. Elastic Observability faces criticism for its steep learning curve and lacks more automated features, predictive analytics, and proactive alerting capabilities, which users hope will improve to enhance scalability.
Ease of Deployment and Customer Service: Datadog offers flexible deployment across multiple cloud environments, making it adaptable but potentially complex. Its customer service is generally helpful and responsive, although inconsistency and delays are noted. Elastic Observability is commonly deployed on-premises, providing strong data control but increasing setup complexity. Its support is seen as high-quality but could improve in timeliness.
Pricing and ROI: Datadog's pricing, perceived as higher due to usage-based billing, can lead to unexpected costs. It provides a reasonable ROI with comprehensive monitoring capabilities, though cost management remains a concern. Elastic Observability is viewed as more cost-effective, particularly for larger deployments, thanks to its open-source model that offers flexibility and savings. However, close management of its licensing is crucial to avoid potential high costs.
Elastic Observability seems to have a good scale-out capability.
What is not scalable for us is not on Elastic's side.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
Elastic Observability is really stable.
One example is the inability to monitor very old databases with the newest version.
Elastic Observability could improve asset discovery as the current requirement to push the agent is not ideal.
The license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
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.
Elastic Observability is primarily used for monitoring login events, application performance, and infrastructure, supporting significant data volumes through features like log aggregation, centralized logging, and system metric analysis.
Elastic Observability employs Elastic APM for performance and latency analysis, significantly aiding business KPIs and technical stability. It is popular among users for system and server monitoring, capacity planning, cyber security, and managing data pipelines. With the integration of Kibana, it offers robust visualization, reporting, and incident response capabilities through rapid log searches while supporting machine learning and hybrid cloud environments.
What are Elastic Observability's key features?Companies in technology, finance, healthcare, and other industries implement Elastic Observability for tailored monitoring solutions. They find its integration with existing systems useful for maintaining operation efficiency and security, particularly valuing the visualization capabilities through Kibana to monitor KPIs and improve incident response times.
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