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.
"Datadog has made it much easier to have a central place for people to look for logs and made it much easier to notify them of any elevated error rates or failures."
"Using the data, our operation teams works with the dashboards to get their statistics, analytics, etc."
"The dashboards and the performance of the software have been great."
"The monitoring functionality, in general, and tagging infrastructure are great."
"The service catalog helped improve our organization by giving a good view of the flow for our microservices applications."
"The full stack of integrations made it easier to monitor the different technologies and platform providers, including Software as a Service providers, that otherwise would need a lot of work and customization to be able to see what is happening."
"The management of SLOs and their related burn-rate monitors have allowed us to onboard teams to on-call fast."
"Its integration is most valuable because you can integrate it with various service providers such as AWS, .Net, etc."
"Its diverse set of features available on the cloud is of significant importance."
"The Elastic User Interface framework lets us do custom development when needed. You need to have some Javascript knowledge. We need that knowledge to develop new custom tests."
"The price is very less expensive compared to the other solutions."
"Good design and easy to use once implemented."
"We can view and connect different sources to the dashboard using it."
"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."
"The architecture and system's stability are simple."
"It has always been a stable solution."
"It lacks consistency in the APIs."
"Lately, chat support has a longer waiting time."
"I'm still exploring the trial version, and it is fine. One thing that I haven't been able to figure out is how to retrieve a report. This is something that could be improved. I probably need to navigate to a place to access the reports."
"It can have a more modernized pricing mechanism. We're actually working with them to figure out how to become more modular and have a better and more modernized pricing mechanism. The issue with Datadog is that you have to buy the whole suite of different products, and you kind of get stuck in the old utilization of 40% of their suite. Most organizations today break down between application development, networking, and security. Therefore, there should be a way to break down different modules into just app dev, infosec, networking, etc. Customers have various needs across their business lines, and sometimes, they're just not willing to have tools that they're not using 100%. AppDynamics is probably a little bit better in terms of being modular."
"I would like the tooling to have better integration in Slack, specifically sending out reminders to the relevant people to take breaks, do a retrospective, and specify with emojis which messages to log."
"One thing we have run into is that it is so easy to add monitoring that we turn on things without really understanding the costs."
"The real issue with this product is cost control."
"We need to learn more about the session reply feature inside of DD."
"Elastic APM's visualization is not that great compared to other tools. It's number of metrics is very low."
"Elastic Observability needs to have better standardization, logging, and schema."
"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."
"More web features could be added to the product."
"The price is the only issue in the solution. It can be made better and cheaper."
"Elastic Observability is difficult to use. There are only three options for customization but this can be difficult for our use case. We do not have other options to choose the metrics shown, such as CPU or memory usage."
"The cost must be made more transparent."
"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."
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, AppDynamics, Azure Monitor and Grafana. 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.