We performed a comparison between Datadog and Elastic Observability based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Features: Datadog offers a range of valuable features, such as customizable dashboards and detailed reporting. It also excels in error reporting and log centralization, making it easier to identify and address issues. The platform's ease of use and simple setup process are appreciated by users. Performance monitoring and infrastructure monitoring are reliable, and the platform offers flexibility and additional features. Elastic Observability is known for its cost-effectiveness and favorable licensing. The comprehensive features and easy deployment and flexibility are key strengths, and the platform's machine learning capabilities are appreciated. Elastic Observability offers stable performance and has a well-designed interface. Datadog could enhance its usability, integration, learning curve, external website monitoring, and SSL security. Elastic Observability, meanwhile, requires improvements in auto-discovery, visualization, metrics, and role-based access control.
Service and Support: Some users have found the support provided by Datadog to be helpful and responsive, while others have experienced slow or unresponsive support. Elastic Observability's customer service has been highly praised for its excellent technical support and quick responses. Some customers even have a dedicated resource for their issues.
Ease of Deployment: Users generally findDatadog's initial setup to be simple and uncomplicated, with support readily accessible. The setup process for Elastic Observability varies in difficulty. While it is deemed straightforward for Docker installation, some users encounter difficulties due to various cluster configurations and distributed solutions.
Pricing: Datadog's setup cost is mixed in terms of its affordability. The pricing model is unclear and lacks documentation. Elastic Observability provides various pricing options, including a self-managed license with three tiers. It incorporates embedded or open-source components, potentially making it more economical.
ROI: Users have reported various benefits from using Datadog, including time savings and faster debugging. Elastic Observability has been found to be cost-effective, helping to reduce incidents and identify issues effectively.
Comparison Results: Elastic Observability is praised for its cost-effectiveness, favorable licensing, comprehensive features, easy deployment, and customization flexibility. Users highly value its machine learning capabilities and stable performance, making it the preferred solution.
"It helps us better manage our logs."
"Datadog's ability to group and visualize the servers and the data makes it relatively easy for the root cause analysis."
"The most valuable feature is the dashboards that are provided out of the box, as well as ones we were able to configure."
"Thanks to the logs, we manage to make better reports through Jira and also to trace the request with more facility than we would be able to do otherwise."
"Datadog has given us near-live visibility across our entire cloud platform."
"APM is great and has provided low-effort out-of-the-box observability for various services."
"Datadog helps us detect issues early on and helps in troubleshooting."
"Datadog's log aggregation is really helpful since it lets me and every other engineer on my team login, view, and share logs when we need to debug our application."
"The product has connectors to many services."
"It's easy to deploy, and it's very flexible."
"The solution is open-source and helps with back-end logging. It is also easy to handle."
"The ability to ensure that the data is searchable and maintainable is highly valuable for our purposes."
"The most valuable feature of Elastic Observability is the text search."
"Elastic APM has plenty of features, such as the Elastic server for Kibana and many additional plugins. It's a comprehensive tool when used as a logging platform."
"We can view and connect different sources to the dashboard using it."
"Machine learning is the most valuable feature of this solution."
"The incident management beta looks promising, but it is still missing the ability to automatically create incidents based on certain alerts."
"I sometimes log in and see items changed, either in the UI or a feature enabled. To see it for the first time without proper communication can sometimes come as a shock."
"The menu on the left is pretty dense (and I know it has to be). I never knew about the cmd+k functionality until recently. It would be helpful to offer more tips/cheat sheets to see handy shortcuts like that."
"When it comes to storing the logs with Datadog, I'm not sure why it costs so much to store gigabytes or terabytes of information when it's a fraction of the cost to do so myself."
"Auto instrumentation on tracing has not been very easy to find in the documentation."
"Could be a little more user friendly."
"The product needs a better Datadog agent installation."
"I think better access to their engineers when we have a problem could be better."
"Elastic Observability needs to improve the retrieval of logs and metrics from all the instances."
"There is room for improvement regarding its APM capabilities."
"There could be more low-code features included in the product."
"The tool's scalability involves a more complex implementation process. It requires careful calculations to determine the number of nodes needed, the specifications of each node, and the configuration of hot, warm, and cold zones for data storage. Additionally, managing log retention policies adds further complexity. The solution's pricing also needs to be cheaper."
"The solution needs to use more AI. Once the product onboards AI, users would more effectively be able to track endpoints for specific messages."
"The auto-discovery isn't nearly as good. That's a big portion of it. When you drop the agent onto the JVM and you're trying to figure things out, having to go through and manually do all that is cumbersome."
"Elastic Observability needs to have better standardization, logging, and schema."
"Elastic APM's visualization is not that great compared to other tools. It's number of metrics is very low."
Datadog is ranked 1st in Application Performance Monitoring (APM) and Observability with 137 reviews while Elastic Observability is ranked 7th in Application Performance Monitoring (APM) and Observability with 22 reviews. Datadog is rated 8.6, while Elastic Observability is rated 7.8. The top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". On the other hand, the top reviewer of Elastic Observability writes "The user interface framework lets us do custom development when needed. ". Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and AppDynamics, whereas Elastic Observability is most compared with Dynatrace, New Relic, Azure Monitor, Sentry and Zabbix. See our Datadog vs. Elastic Observability report.
See our list of best Application Performance Monitoring (APM) and Observability vendors, best IT Infrastructure Monitoring vendors, and best Log Management vendors.
We monitor all Application Performance Monitoring (APM) and Observability reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.