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Datadog vs Elastic Observability comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Oct 19, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.5
Datadog enhances efficiency by optimizing resources, reducing downtime, and improving incident response, offering significant cost savings and performance benefits.
Sentiment score
6.5
Elastic Observability enhances cost-effectiveness by reducing incidents, automating fixes, and visualizing cloud operations, saving time and resources.
Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five.
Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization.
I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for.
Elastic Observability has saved us time as it's much easier to find relevant pieces across the system in one screen compared to our own software, and it has saved resources too since the same resources can use less time.
 

Customer Service

Sentiment score
6.7
Datadog's customer service is praised, though technical support feedback is mixed; expertise and engagement are frequently appreciated.
Sentiment score
7.6
Elastic Observability customers appreciate their helpful support, quick responses, and valuable documentation, despite some challenges in complex issue resolution.
When I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional.
I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.
Elastic support really struggles in complex situations to resolve issues.
Their excellent documentation typically helps me solve any issues I encounter.
 

Scalability Issues

Sentiment score
7.6
Datadog is praised for scalability, easy integration, and reliable performance, but users should monitor costs as usage increases.
Sentiment score
7.2
Elastic Observability is praised for scalability and ease of deployment, despite potential complexities and internal process limitations.
Datadog's scalability has been great as it has been able to grow with our needs.
We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us.
Datadog's scalability is strong; we've continued to significantly grow our software, and there are processes in place to ensure that as new servers, realms, and environments are introduced, we're able to include them all in Datadog without noticing any performance issues.
I rate the scalability of Elastic Observability as a ten, as we have never seen issues even with a lot of data coming in from more customers, provided we have the appropriate configuration.
Elastic Observability seems to have a good scale-out capability.
Elastic Observability is easy in deployment in general for small scale, but when you deploy it at a really large scale, the complexity comes with the customizations.
 

Stability Issues

Sentiment score
8.0
Datadog is praised for its reliability and stability, with occasional minor issues that are quickly resolved.
Sentiment score
8.2
Elastic Observability is stable and reliable, with high user ratings, efficiently handling large data volumes with proper configuration.
Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time.
Datadog seems stable in my experience without any downtime or reliability issues.
These incidents are related to log service, indexes, and metric capturing issues.
There are some bugs that come with each release, but they are keen always to build major versions and minor versions on time, including the CVE vulnerabilities to fix it.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
I would rate the stability of Elastic Observability as a ten, as we don't experience any issues.
 

Room For Improvement

Datadog faces user complaints about performance, pricing, complexity, logging, notifications, integration, AI, cost control, and documentation.
Elastic Observability needs automation, AI, and customization improvements, addressing complex deployment, market presence, metrics, licensing, and usability issues.
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
Some areas such as AI Ops still require data scientists to understand machine learning and AI, and it doesn't have a quick win with no-brainer use cases.
 

Setup Cost

Datadog pricing is high yet reasonable; flexible subscriptions are available, but usage-based costs require careful monitoring.
Elastic Observability provides competitive pricing, benefiting large enterprises with comprehensive licensing, but may be costly for smaller users.
The setup cost for Datadog is more than $100.
Everybody wants the agent installed, but we only have so many dollars to spread across, so it's been difficult for me to prioritize who will benefit from Datadog at this time.
My experience with pricing, setup cost, and licensing is that it is really expensive.
The license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
 

Valuable Features

Datadog offers unified data monitoring with extensive integrations, simplifying observability, debugging, analytics, and performance tracking across platforms.
Elastic Observability excels with flexible integration, powerful search, scalability, real-time insights, affordability, and robust support, enhancing efficiency.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time.
Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
 

Categories and Ranking

Datadog
Ranking in Application Performance Monitoring (APM) and Observability
1st
Ranking in IT Infrastructure Monitoring
3rd
Ranking in Log Management
4th
Ranking in Container Monitoring
2nd
Ranking in Cloud Monitoring Software
2nd
Average Rating
8.6
Reviews Sentiment
7.0
Number of Reviews
206
Ranking in other categories
Network Monitoring Software (4th), AIOps (1st), Cloud Security Posture Management (CSPM) (6th)
Elastic Observability
Ranking in Application Performance Monitoring (APM) and Observability
7th
Ranking in IT Infrastructure Monitoring
10th
Ranking in Log Management
14th
Ranking in Container Monitoring
4th
Ranking in Cloud Monitoring Software
6th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
29
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Datadog is 7.4%, down from 10.4% compared to the previous year. The mindshare of Elastic Observability is 3.9%, down from 6.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Application Performance Monitoring (APM) and Observability Market Share Distribution
ProductMarket Share (%)
Datadog7.4%
Elastic Observability3.9%
Other88.7%
Application Performance Monitoring (APM) and Observability
 

Featured Reviews

Dhroov Patel - PeerSpot reviewer
Has improved incident response with better root cause visibility and supports flexible on-call scheduling
Datadog needs to introduce more hard limits to cost. If we see a huge log spike, administrators should have more control over what happens to save costs. If a service starts logging extensively, I want the ability to automatically direct that log into the cheapest log bucket. This should be the case with many offerings. If we're seeing too much APM, we need to be aware of it and able to stop it rather than having administrators reach out to specific teams. Datadog has become significantly slower over the last year. They could improve performance at the risk of slowing down feature work. More resources need to go into Fleet Automation because we face many problems with things such as the Ansible role to install Datadog in non-containerized hosts. We mainly want to see performance improvements, less time spent looking at costs, the ability to trust that costs will stay reasonable, and an easier way to manage our agents. It is such a powerful tool with much potential on the horizon, but cost control, performance, and agent management need improvement. The main issues are with the administrative side rather than the actual application.
Stefan Decuypere - PeerSpot reviewer
Real-time dashboards and visual insights have streamlined issue analysis and monitoring
After careful consideration about areas for improvement in Elastic Observability, aspects such as pricing, customization, implementation, and scalability could be improved. As a user of the system, I know what it costs but am not directly involved in cost-benefit evaluations or maintenance, which is handled by another team. I develop the visual representation of the data and frankly, I don't see major gaps in my application or anything I would really miss; I appreciate the fast pace of the developments that have occurred in the last couple of years. Regarding room for improvement in Elastic Observability, I would have preferred built-in tools to manage the indexes on deployment for better visual representation, as the initial feedback regarding system performance and data storage was fairly primitive and lacking.
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
14%
Manufacturing Company
8%
Retailer
6%
Financial Services Firm
17%
Computer Software Company
14%
Manufacturing Company
8%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business80
Midsize Enterprise46
Large Enterprise94
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise4
Large Enterprise16
 

Questions from the Community

Any advice about APM solutions?
There are many factors and we know little about your requirements (size of org, technology stack, management systems, the scope of implementation). Our goal was to consolidate APM and infra monitor...
Datadog vs ELK: which one is good in terms of performance, cost and efficiency?
With Datadog, we have near-live visibility across our entire platform. We have seen APM metrics impacted several times lately using the dashboards we have created with Datadog; they are very good c...
Which would you choose - Datadog or Dynatrace?
Our organization ran comparison tests to determine whether the Datadog or Dynatrace network monitoring software was the better fit for us. We decided to go with Dynatrace. Dynatrace offers network ...
What do you like most about Elastic Observability?
Elastic Observability significantly improves incident response time by providing quick access to logs and data across various sources. For instance, searching for specific keywords in logs spanning...
What is your experience regarding pricing and costs for Elastic Observability?
The problem is their licensing model, which is a bit confusing. Many customers struggle to understand their total cost of ownership because Elastic licensing is not dependent on easy, quantifiable ...
What needs improvement with Elastic Observability?
Out-of-the-box use cases have room for improvement in Elastic Observability. They don't invest a lot in building out-of-the-box observable use cases, and they are more focusing on giving a very fle...
 

Comparisons

 

Overview

 

Sample Customers

Adobe, Samsung, facebook, HP Cloud Services, Electronic Arts, salesforce, Stanford University, CiTRIX, Chef, zendesk, Hearst Magazines, Spotify, mercardo libre, Slashdot, Ziff Davis, PBS, MLS, The Motley Fool, Politico, Barneby's
PSCU, Entel, VITAS, Mimecast, Barrett Steel, Butterfield Bank
Find out what your peers are saying about Datadog vs. Elastic Observability and other solutions. Updated: October 2025.
872,029 professionals have used our research since 2012.