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Cribl vs Datadog 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
5.5
Cribl reduces costs, enhances efficiency, and automates tasks, significantly lowering SIEM expenses and data ingestion costs for organizations.
Sentiment score
6.5
Datadog enhances efficiency by optimizing resources, reducing downtime, and improving incident response, offering significant cost savings and performance benefits.
What we've seen is really an overall reduction of just shy of 40% in our ingest into our SIM platform versus prior to having Cribl.
In terms of reduction, we were able to save almost ~40% of our total cost.
In the case of optimization, it has helped return on investment to somewhere close to 50%.
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.
 

Customer Service

Sentiment score
5.8
Cribl's support is praised for quick, effective responses, knowledgeable staff, and valuable resources, despite needing better customer requirement understanding.
Sentiment score
6.7
Datadog's customer service is praised, though technical support feedback is mixed; expertise and engagement are frequently appreciated.
They had extensive expertise with the product and were able to facilitate everything we needed.
Usually, within an hour, we get a response, and we are able to work with them back and forth until we resolve the issues.
If they could enhance their internal logging, we won't require Cribl support to engage.
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.
 

Scalability Issues

Sentiment score
6.4
Cribl offers scalable workload distribution and seamless cloud integration, efficiently scaling from small organizations to large enterprises.
Sentiment score
7.6
Datadog is praised for scalability, easy integration, and reliable performance, but users should monitor costs as usage increases.
The infrastructure behind Cribl Search is also scalable as it uses a CPU and just spawns horizontally more instances as it demands and requires.
It's an enterprise version, and we have a good amount of users using this solution.
I don't need to talk to a Cribl engineer to connect a new log source.
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.
 

Stability Issues

Sentiment score
6.5
Cribl is generally stable and reliable, with minor issues mitigated by improvements, strong support, and effective documentation.
Sentiment score
8.0
Datadog is praised for its reliability and stability, with occasional minor issues that are quickly resolved.
Migrating from those SC4S servers to Cribl worker nodes has truly been a game-changer.
I would rate the stability as ten out of ten.
If the pipeline is down and we receive an alert that it's not sending information to the log collection platform for more than one or two hours, if we receive an alert, it would be great.
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.
 

Room For Improvement

Cribl should enhance compatibility, improve user interface, offer training, optimize performance, and expand features to boost functionality and usability.
Datadog faces user complaints about performance, pricing, complexity, logging, notifications, integration, AI, cost control, and documentation.
If we can have more internal logs and more debug logs to validate the error, that would be beneficial because instead of reaching out to Cribl support, we can troubleshoot and find the root cause ourselves.
In terms of large datasets—whether they originated from network inputs, virtual machines, or cloud instances—ingesting the data into the destination was relatively easy.
Since Cribl is such a large platform with numerous features, having a clear, structured approach would make it easier for me and others to understand and utilize its capabilities.
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.
 

Setup Cost

Cribl offers cost-effective pricing with scalability and efficiency, appreciated for handling large data volumes despite annual price increases.
Datadog pricing is high yet reasonable; flexible subscriptions are available, but usage-based costs require careful monitoring.
Over time, the licensing cost has increased.
Cribl is very inexpensive, with enterprise pricing around 30 cents per GB, which is really decent.
They have a universal license that allows us to consume the portions of Cribl that we want to use or flex into other portions of Cribl.
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.
 

Valuable Features

Cribl transforms and manages data efficiently with scalable, intuitive UI, enhancing integration, reducing storage costs, and maintaining integrity.
Datadog offers unified data monitoring with extensive integrations, simplifying observability, debugging, analytics, and performance tracking across platforms.
The data reduction and preprocessing capabilities make Cribl really unique.
Cribl has a feature called JSON Unroll or Unroll function that allows you to differentiate the events; each event will come ingested as a single log instead of piling it up with multiple events.
The Cribl UI is very simple and easy to use, particularly when working with data from various sources; it makes it very easy to create pipelines, add complex logic to those pipelines, and then gives you a preview of what your data looks like before applying that pipeline and what you get after.
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.
 

Categories and Ranking

Cribl
Ranking in Application Performance Monitoring (APM) and Observability
12th
Ranking in Log Management
6th
Average Rating
8.6
Reviews Sentiment
6.7
Number of Reviews
25
Ranking in other categories
Security Information and Event Management (SIEM) (10th), Observability Pipeline Software (1st)
Datadog
Ranking in Application Performance Monitoring (APM) and Observability
1st
Ranking in Log Management
4th
Average Rating
8.6
Reviews Sentiment
7.0
Number of Reviews
206
Ranking in other categories
Network Monitoring Software (4th), IT Infrastructure Monitoring (3rd), Container Monitoring (2nd), Cloud Monitoring Software (2nd), AIOps (1st), Cloud Security Posture Management (CSPM) (6th)
 

Mindshare comparison

As of October 2025, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Cribl is 1.1%, up from 0.3% compared to the previous year. The mindshare of Datadog is 7.4%, down from 10.4% 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%
Cribl1.1%
Other91.5%
Application Performance Monitoring (APM) and Observability
 

Featured Reviews

Richard McIver - PeerSpot reviewer
Simplifies data processing and reduces ingest costs through real-time transformation
My favorite feature of Cribl is just how easy it makes working with the data; it's always been a pain point for us with other solutions, just taking our raw data from the source, transforming and manipulating it into what we need on the SIM side. That's always been a pretty heavy lift, however, Cribl has made that much easier. The tools built into the platform allow us to work with the data, see the results in real-time, see what the output's going to look before we commit it, and has really made our job in that respect a lot easier. The Cribl UI is very simple and easy to use, particularly when working with data from various sources; it makes it very easy to create pipelines, add complex logic to those pipelines, and then gives you a preview of what your data looks like before applying that pipeline and what you get after. As we're bringing data in and Cribl's processing it, it makes it very easy to identify subsets of data or certain events that source data that maybe are less useful or just noisy, not really applicable to to what we need what our security team needs, and we're able to just drop those events before they get sent out and and ingested by our SIEM. So that helps keep our data pipeline streamlined, keeps our output clean. It filters out noise, and then it makes our analysis more efficient. That reduces the data volume going into our SIMs, and that reduces and limits the ingest costs associated with that end. With less data, there's less to process when you're running complex searches. So we have charges against those compute resources reduced.
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.
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
8%
Manufacturing Company
8%
Healthcare Company
7%
Financial Services Firm
15%
Computer Software Company
14%
Manufacturing Company
8%
Retailer
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise5
Large Enterprise14
By reviewers
Company SizeCount
Small Business80
Midsize Enterprise46
Large Enterprise94
 

Questions from the Community

What is your experience regarding pricing and costs for Cribl?
Cribl is very inexpensive, with enterprise pricing around 30 cents per GB, which is really decent. Organizations looking to ingest terabytes or petabytes of data each day find it quite an inexpensi...
What needs improvement with Cribl?
The product is very good. They could add more AI-assisted pipeline development in the future release.
What is your primary use case for Cribl?
My current use cases involve using it as a pipeline to process data, to route data from cloud logs to different repositories. Some data goes to Splunk and others go to different data lakes. I didn'...
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 ...
 

Comparisons

 

Overview

 

Sample Customers

Information Not Available
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
Find out what your peers are saying about Cribl vs. Datadog and other solutions. Updated: September 2025.
872,706 professionals have used our research since 2012.