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."
"Anything I've wanted to do, I found a way to get it done through Datadog."
"The most valuable features have been: Sharable dashboards, TimeBoards, dogstatsd API, Slack Integration, Event logging API. CloudTrail Events, Tags, alerts, and anomaly detection. EBS Volume Snapshot Age, which they added upon request."
"Sometimes it's more user friendly for development teams. There are some parts of Datadog that are more understandable for development teams. For example, the APM in Datadog works more manually and works like the tools in New Relic or Grafana, or Elastic. It is easier to understand for software development teams."
"I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before."
"Datadog has proven to be easy to set up and legible for both development and operational teams."
"Its integration definitely stands out. It provides seamless monitoring of all our systems, services, apps, and whatever else we secure and monitor. Visualizations have become simpler with dashboards. We are getting visibility into systems, services, and apps stack through a single pane of glass, which is good. We are able to put logs in context."
"I like the amount of tooling and the number of solutions they sold with their monitoring. Datadog was highly intuitive to use."
"Good design and easy to use once implemented."
"We can view and connect different sources to the dashboard using it."
"The most valuable feature of Elastic Observability is the text search."
"The ability to ensure that the data is searchable and maintainable is highly valuable for our purposes."
"The product has connectors to many services."
"The price is very less expensive compared to the other solutions."
"It is a powerful tool that allows users to collect and transform logs as needed, enabling flexible visualization and analysis."
"Machine learning is the most valuable feature of this solution."
"I found the solution to be stable, I did not experience any bugs or glitches. However, some of the managing team did."
"While I like the ease of use, when compared with Tenable Nessus they could still improve their usability."
"The ease of implementation needs improvement."
"This service could be less costly."
"If there were a more cost-effective manner of deploying the tool, we'd be more likely to adopt it more widely."
"Delta traces on the Golang profiler are extremely expensive concerning memory utilization."
"We would like to see some versioning system for the Synthetic Tests so that we could have a backup of our tests since they are time-consuming to make and very easy to damage in a moment of error."
"Its pricing model can be improved. Its settings should be improved for a better understanding of billing. They should also provide some alerts when there is an increase in the usage. For example, if there is 20% more increase from one week to another, the customer should get an alert."
"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."
"Elastic Observability is reactive rather than proactive. It should act as an ITSM tool and be able to create tickets and alerts on Jira."
"Elastic APM's visualization is not that great compared to other tools. It's number of metrics is very low."
"If we had some pre-defined templates for observability that we could start using right away after deploying it – instead of having to build or to change some of the dashboards – that would be helpful."
"There's a steep learning curve if you've never used this solution before."
"More web features could be added to the product."
"Elastic Observability needs to improve the retrieval of logs and metrics from all the instances."
"Elastic Observability’s price could be improved."
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.