We performed a comparison between Datadog and Elastic Security (formerly ELK Logstash) based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Datadog and Elastic Security have a similar user rating for ease of deployment, and users of both felt that the solutions were expensive. Users felt Elastic Security took too long to respond when it came to service and support. In terms of features, reviewers of Datadog had a problem with stability and felt there wasn’t enough monitoring through their dashboard. Reviewers of Elastic Security said they had difficulty retrieving data and felt the solution should offer predictive maintenance.
"The application performance monitoring is pretty good."
"The management of SLOs and their related burn-rate monitors have allowed us to onboard teams to on-call fast."
"For us to have visibility into our app stack and the hardware we run has been highly beneficial."
"Anything I've wanted to do, I found a way to get it done through Datadog."
"It has saved us a lot of trouble in implementation."
"We have been able to set very specific CPU and memory alerts, at the very base level, then we started to pull real business value, like 99th percentile response rates for our API calls."
"It has a nice UI."
"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."
"The most valuable feature is the scalability. We are in Indonesia, more engineers understand Elastic Security here. So it is easier to scale and also develop. In features, the discovery to query all the logs is very important to us. It is very easy, especially with the query function and the feature to generate alerts and create tools. Sometimes we use the alert security dashboard to monitor our clients."
"The most valuable features of the solution are the prevention methods and the incident alerts."
"The solution is compatible with the cloud-native environment and they can adapt to it faster."
"I use the stack every morning to check the errors and it's just so clear. I don't see any disadvantage to using Logstash."
"The most valuable feature is the machine learning capability."
"The performance is good and it is faster than IBM QRadar."
"It is an extremely stable solution. Stability-wise, I rate the solution a ten out of ten."
"The most valuable feature is the speed, as it responds in a very short time."
"Their security features could be improved. We looked at their Security Monitoring feature but it was early in its development. Datadog are just getting into the security space so I'm sure this will improve in the future."
"We need more integration functionality, including certain metrics integration."
"The product is quite complex, and there are so many features that I either didn't know about or wasn't sure how to use."
"I think better access to their engineers when we have a problem could be better."
"Auto instrumentation on tracing has not been very easy to find in the documentation."
"The sheer amount of products that are included can be overwhelming."
"They should continue expanding and integrating with more third-party apps."
"Billing should be more transparent."
"We set up a cron job to delete old logs so that we wouldn't hit a disk space issue. Such a feature should be available in the UI, where old logs can be deleted automatically. (Don’t know if this feature is already there)."
"The tool should improve its scalability."
"Technical support could respond faster."
"This solution cannot do predictive maintenance, so we have to build our own modules for doing it."
"Email notification should be done the same way as Logentries does it."
"We had issues with scalability. Logstash was not scaling and aggregation was getting delayed. We moved to Fluentd making our stack from ELK to EFK."
"The solution could offer better reporting features."
"It could use maybe a little more on the Linux side."
Datadog is ranked 3rd in Log Management with 137 reviews while Elastic Security is ranked 5th in Log Management with 59 reviews. Datadog is rated 8.6, while Elastic Security is rated 7.6. The top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". On the other hand, the top reviewer of Elastic Security writes "A stable and scalable tool that provides visibility along with the consolidation of logs to its users". Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and Elastic Observability, whereas Elastic Security is most compared with Wazuh, Splunk Enterprise Security, Microsoft Sentinel, IBM Security QRadar and Microsoft Defender for Endpoint. See our Datadog vs. Elastic Security report.
See our list of best Log Management vendors.
We monitor all Log Management 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.
It depends on your requirement. If you are looking for a SIEM/log management solution ELK would be a better option.
But if you are looking for more of a monitoring solution Datadog would be better. Also, Datadog provides out-of-the-box integrations with a lot of cloud applications. ELK could be cost-effective but a bit challenging to configure & finetune.
Datadog: Unify logs, metrics, and traces from across your distributed infrastructure. Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
Datadog features offered are:
200+ turn-key integrations for data aggregation
Clean graphs of StatsD and other integrations
Elasticsearch: Open Source, Distributed, RESTful Search Engine. Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Elasticsearch provides the following key features:
Distributed and Highly Available Search Engine.
Multi Tenant with Multi Types.
Various set of APIs including RESTful
Dear,
Unfortunately, I can't say much about Datadog but I have used ELK for a short period.
And I can tell you not everything works the way it should. For example, I noticed heavy CPU usage for a Windows client on MS AD servers. I advise you to consider this if it's important to you.
Good luck!
Where do you want to spend your money, on people or licenses?
ELK requires a long-term investment in engineering resources to manage the system and to provide the capability.
Datadog provides capabilities for you so you only need some administrators. What are the capabilities? Some critical ones include availability, scalability, consuming log files, platform upgrades, ...
If you are consuming smaller data sets (100's of GB) with shorter retention, the size and scaling are much easier making ELK easier.
Do you have admins or engineers? If your team doesn't have dedicated time & skills to spend developing solutions like elastic-alert you should look for a vendor to provide capabilities.
I expect some capabilities in Datadog you will not be able to replicate in ELK.... so that answer makes this obvious.
We are going to evaluate the same for our org. We do about 10 TB a day consumption in ELK and are looking to see if we can shift $$$ from engineers and infra to SaaS.
I have used both ELK and Datadog, and there are lots of variables to consider here. The three important points that I looked at are:
- Cost. In addition to service costs, you have to consider egress and ingress costs as well.
- Real-time observability that you need during development vs long-term Observability. Keep in mind, when you export data over the internet, it comes with the same reliability issues as any other service on the internet. Regardless of how Datadog classifies its service as real-time, it is not real-time, IMO. It very much depends on your definition of real-time.
- Deployment and maintenance complexity. When your ELK cluster grows it has some pain points you need to be aware of.
My general approach is to deploy ELK for development, tune the data, and then pivot toward commercial solutions if I need to. This gives you insight into your data and what you should be preserving and that way you are not paying high costs, when or if you do decide to take advantage of a commercial solution.
Can you tell me what you actually want to do so that I can help you?