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Datadog vs Unomaly 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:
 

Categories and Ranking

Datadog
Ranking in Cloud Monitoring Software
2nd
Average Rating
8.6
Reviews Sentiment
7.0
Number of Reviews
207
Ranking in other categories
Application Performance Monitoring (APM) and Observability (1st), Network Monitoring Software (3rd), IT Infrastructure Monitoring (2nd), Log Management (3rd), Container Monitoring (1st), AIOps (1st), Cloud Security Posture Management (CSPM) (5th)
Unomaly
Ranking in Cloud Monitoring Software
37th
Average Rating
7.0
Reviews Sentiment
2.4
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2025, in the Cloud Monitoring Software category, the mindshare of Datadog is 8.0%, down from 11.8% compared to the previous year. The mindshare of Unomaly is 0.1%, up from 0.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Monitoring Software Market Share Distribution
ProductMarket Share (%)
Datadog8.0%
Unomaly0.1%
Other91.9%
Cloud Monitoring Software
 

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.
reviewer2771775 - PeerSpot reviewer
Has improved error detection significantly but still needs deeper integration with intelligent automation
I have been using Unomaly or LM Envision by LogicMonitor for a year for internal purposes. I personally don't use metrics to evaluate Unomaly's performance as I have a team who handles that aspect. The endgame has moved towards agentic AI. Two years back, it was supposed to be the endgame with ML and prediction anomaly. The world has moved on. Having Unomaly, even the best anomaly doesn't make too much of a difference. The endgame is now about the metrics of autonomy rather than anomaly. What is the degree of autonomy? What is the return on autonomy? Those are the metrics I'm more interested in than just having the anomaly. The world order has shifted, and the KPIs have shifted. They already have Gen AI and agentic AI features, but we haven't used them so far. I will continue to use it in the future for now as it's only been a year. We don't want to change anything internally for now. I would recommend Unomaly to other customers because anybody using observability can and should use Unomaly in the new world. I can't think of any types of companies I would not recommend it to because observability cannot exist without Unomaly nowadays. On a scale of 1 to 10, I rate Unomaly a seven.

Quotes from Members

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

Pros

"The ease of use allowed me to get up to speed with log management since it's my first time using Datadog."
"The management of SLOs and their related burn-rate monitors have allowed us to onboard teams to on-call fast."
"The agents feature in Datadog stands out as a valuable asset within our organization due to its robust functionality, versatility, and role in providing comprehensive monitoring and observability capabilities."
"Datadog dashboards are pretty great."
"Datadog has proven to be easy to set up and legible for both development and operational teams."
"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."
"The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents."
"Datadog has impacted our organization positively since we were previously using AppDynamics and then we switched to Datadog, which improved a lot in our alerting and monitoring in the infrastructure space and application space, allowing us to monitor business transactions and take proactive action before an end user reports it."
"Unomaly's anomaly detection capabilities contribute to maintaining system reliability; we cannot find all errors humanly, we cannot configure every possible threshold, and in the new world of intelligence and AI, we need to have this intelligent way of finding out the anomalies."
"Unomaly's anomaly detection capabilities contribute to maintaining system reliability; we cannot find all errors humanly, we cannot configure every possible threshold, and in the new world of intelligence and AI, we need to have this intelligent way of finding out the anomalies."
 

Cons

"While the UI and search functionality are excellent, further improvement could be made in the querying of logs by offering more advanced templates or suggestions based on common use cases."
"I found the documentation can sometimes be confusing."
"The FinOps needs improvement."
"The way data is represented can be limiting. When I first tried it out a long time ago, you could graph a metric and another metric, and they'd overlay, but you couldn't take the ratio between the two."
"Another issue that I have is with the search syntax, it could be simpler and it feels like there are too many ways to do the same things."
"It would also be nice if we had more insight into our own usage of Datadog (agents and custom metrics). They provide a usage page which does help, but it is not in real-time."
"Network device and performance monitoring could be improved, as we've faced some limitations in this area."
"Interactive tutorials could be a game changer."
"Having Unomaly, even the best anomaly doesn't make too much of a difference."
"Having Unomaly, even the best anomaly doesn't make too much of a difference."
 

Pricing and Cost Advice

"It costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost."
"It has a module-based pricing model."
"It has always scaled for us. Cost scales up too, but that is not necessarily a bad thing. It's reasonable for what they're providing."
"This solution is budget friendly."
"My advice is to really keep an eye on your overage costs, as they can spiral really fast."
"They prefer monthly subscriptions."
"Pricing seemed easy until the bill came in and some things were not accounted for."
"Datadog does not provide any free plans to use the solution. When I start with a proof of concept it would be sensible to have a free plan to test the tool and check whether it fits the requirements of the project. Before the production stage, it is always good to have a free plan with some limited features, number of requests, or logs."
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
14%
Manufacturing Company
8%
Retailer
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business80
Midsize Enterprise46
Large Enterprise95
No data available
 

Questions from the Community

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Comparisons

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Sample Customers

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