Datadog and Elastic Observability are leaders in the field of IT monitoring and observability tools, each offering distinct advantages. Datadog holds an edge with its extensive integration ecosystem and ease of deployment for cloud platforms.
Features:Datadog offers features like sharable dashboards, powerful alerting, and seamless cloud integrations, enabling comprehensive infrastructure management without heavy reliance on internal systems. Elastic Observability excels in handling large data volumes and providing robust search capabilities, making it ideal for critical data analysis needs.
Room for Improvement:Datadog could benefit from enhanced cost transparency, improved alert customization, and more real-time usage metrics to mitigate unexpected charges. Elastic Observability users point to the complexity of query language and visualization tools, along with a desire for more advanced predictive analytics and improved integration capabilities.
Ease of Deployment and Customer Service:Datadog is praised for its straightforward deployment process across cloud environments and responsive customer service, with quick, effective support. Elastic Observability, while reliable on hybrid and on-premises systems once deployed, offers knowledgeable support teams but could improve in terms of response speed and proactivity.
Pricing and ROI:Datadog users often cite the product's high costs but acknowledge the comprehensive features justify the investment. Elastic Observability offers a more cost-effective solution, favorable for large-scale users or those seeking open-source platforms, delivering a strong ROI by offering essential observability features at lower 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.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
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.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
One example is the inability to monitor very old databases with the newest version.
The setup cost for Datadog is more than $100.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
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.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
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.
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.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
Product | Market Share (%) |
---|---|
Datadog | 7.2% |
Elastic Observability | 3.9% |
Other | 88.9% |
Company Size | Count |
---|---|
Small Business | 78 |
Midsize Enterprise | 42 |
Large Enterprise | 82 |
Company Size | Count |
---|---|
Small Business | 8 |
Midsize Enterprise | 4 |
Large Enterprise | 15 |
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 offers a comprehensive suite for log analytics, application performance monitoring, and machine learning. It integrates seamlessly with platforms like Teams and Slack, enhancing data visualization and scalability for real-time insights.
Elastic Observability is designed to support production environments with features like logging, data collection, and infrastructure tracking. Centralized logging and powerful search functionalities make incident response and performance tracking efficient. Elastic APM and Kibana facilitate detailed data visualization, promoting rapid troubleshooting and effective system performance analysis. Integrated services and extensive connectivity options enhance its role in business and technical decision-making by providing actionable data insights.
What are the most important features of Elastic Observability?Elastic Observability is employed across industries for critical operations, such as in finance for transaction monitoring, in healthcare for secure data management, and in technology for optimizing application performance. Its data-driven approach aids efficient event tracing, supporting diverse industry requirements.
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