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.
"Even if we don't end up using Datadog, it revealed problems and optimizations to us that weren't obvious before."
"The solution is sufficiently stable."
"The most valuable aspect is for us to have everything in one place."
"I find the greatest feature is being able to search across logs from various microservices."
"I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before."
"We have a better grasp of what is occurring during the deployment cycle. If something fails, we have an idea what has failed, where it has failed, and how it failed to better mitigate the situation."
"Datadog has given us near-live visibility across our entire cloud platform."
"Flame graphs are pretty useful for understanding how GraphQL resolves our federated queries when it comes to identifying slow points in our requests. In our microservice environment with 170 services."
"Elastic Security is very customizable, and the dashboards are very easy to build."
"The most valuable feature is the ability to collect authentication information from service providers."
"The most valuable features are the speed, detail, and visualization. It has the latest standards."
"It's a good platform and the very best in the current market. We looked at the Forester report from December 2022 where it was said to be a leader."
"The solution has a good community surrounding it for lots of helpful documentation for troubleshooting purposes."
"The most valuable feature is the machine learning capability."
"It can handle millions of loads at a time, and you can always use the filters to find exactly what you are looking for and detect errors in every log message you are searching for, basically."
"It's not very complicated to install Elastic."
"We'd like Datadog to make the log storage cheaper."
"Datadog does not have the feature where you can monitor external websites or check the SSL secure for websites."
"The documentation leaves a lot to be desired for new users."
"It would be great if usage metrics were automatically created and we could create custom metrics, instead we ended up building some of our own stuff to track and alert on our own usage."
"It seems that admin cost control granularity is an afterthought."
"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."
"I'd like to see better pricing and more integration in the next release."
"To be very fair, I haven't had enough experience with Datadog to pick out improvements."
"The problem with ELK is it's difficult to administer. When you have a problem, it can be very, very difficult to rebuild indexes."
"This solution is very hard to implement."
"With Elastic, you have to build the use cases for the specific requirement. Other products have a simple integration and more use cases to integrate out-of-the-box solutions for SIEM."
"The tool needs to integrate with legacy servers. Big companies can have legacy servers that may not always be updated."
"Email notification should be done the same way as Logentries does it."
"There should be a simulation environment to check whether my Elastic implementation is functioning perfectly fine. Other solutions have their own Android and iOS applications that I can install on my mobile so that I am continuously connected to the SIEM."
"Their visuals and graphs need to be better."
"We'd like to see some more artificial intelligence capabilities."
Datadog is ranked 2nd in Log Management with 136 reviews while Elastic Security is ranked 5th in Log Management with 58 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 AppDynamics, whereas Elastic Security is most compared with Wazuh, Splunk Enterprise Security, Microsoft Sentinel, Microsoft Defender for Endpoint and IBM Security QRadar. 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?