Datadog vs Stackify comparison

Datadog and Stackify are both solutions in the Application Performance Monitoring (APM) and Observability category. Datadog is ranked #1 with an average rating of 8.7, while Stackify is ranked #61. Datadog holds a 5.5% mindshare in APM, compared to Stackify’s 0.6% mindshare. Additionally, 97% of Datadog users are willing to recommend the solution, compared to 71% of Stackify users who would recommend it.

Datadog

Read 211 Datadog reviews

22,772 Views
13,208 Comparison Views

97% willing to recommend

Stackify

Read 6 Stackify reviews

1,335 Views
734 Comparison Views

71% willing to recommend

Datadog

Stackify

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 25, 2026

Datadog and Stackify compete in the APM and monitoring space. Datadog holds a stronger position with its comprehensive suite of monitoring services, appealing to enterprises needing extensive features. Stackify targets developers with its specialized tools for performance management, which might justify its cost for those focused on developer efficiency.

Features: Datadog offers real-time monitoring, custom dashboards, and seamless integration with third-party services, providing extensive visibility and alerting capabilities. Stackify focuses on error tracking, robust log management, and performance metrics aimed at enhancing developer efficiency.

Room for Improvement: Datadog could enhance its user interface to cater more effectively to developers and reduce complexity for non-enterprise users. Stackify may benefit from expanding its monitoring capabilities to cover more general infrastructure needs, improving scalability, and offering more integrations with common development platforms and tools.

Ease of Deployment and Customer Service: Datadog provides a cloud-based deployment model that ensures quick setup and scalability, supported by comprehensive customer service. Stackify excels in offering an intuitive setup process with considerable developer support and thorough documentation, promoting ease of use for development teams.

Pricing and ROI: Datadog's tiered pricing structure is cost-effective for large enterprises needing extensive customization, offering potential long-term value through its extensive features. Stackify, with its lower initial costs, appeals to smaller organizations or developers focusing on performance management, where its ROI hinges on specific developer challenges.

To learn more, read our detailed Datadog vs. Stackify Report (Updated: January 2026).

Buyer's Guide

Datadog vs. Stackify

January 2026

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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 Application Performance Monitoring (APM) and Observability

1st

Ranking in IT Infrastructure Monitoring

2nd

Ranking in Log Management

3rd

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

211

Ranking in other categories

Network Monitoring Software (3rd), Container Monitoring (2nd), Cloud Monitoring Software (2nd), AIOps (1st), Cloud Security Posture Management (CSPM) (5th), AI Observability (1st)

Stackify

Ranking in Application Performance Monitoring (APM) and Observability

61st

Ranking in IT Infrastructure Monitoring

59th

Ranking in Log Management

59th

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Datadog is 5.5%, down from 9.7% compared to the previous year. The mindshare of Stackify is 0.6%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Application Performance Monitoring (APM) and Observability Market Share Distribution
Product	Market Share (%)
Datadog	5.5%
Stackify	0.6%
Other	93.9%

Application Performance Monitoring (APM) and Observability

Featured Reviews

Dhroov Patel

Site Reliability Engineer at Grainger

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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Moses Arigbede

Head of DevOps at Partsimony

Easy to set up with great custom dashboards but needs to improve non-.NET infrastructure

They need to improve non-.NET infrastructure. We always had difficulty when it comes to reporting or metrics that come from Linux operating systems and Docker containers. For anything that runs within the Unix environment, we always had problems with them, however, if it was a document-based application, Stackify was 100%, it gave everything. Now, the aggregation agent, the metric agent for Stackify for Linux, collects everything. When I say everything, I mean, everything. It collects so much information that we now started to term it as useless data as all that ingestion will just come in and overwhelm your log retention limit for the month and really this spike up your cost at the end of the month. You'll need to do a lot in order to train down the data coming in from all your Linux environments, to get to what you really need, which actually takes some time as well. I would like to be able to see metrics about individual running containers on the host machines. Stackify has not really gotten that right, as far as I'm concerned. Netdata has done a better job and New Relic has also done a better job. They need to improve on that. We need to be able to see the individual resource usage of containers running within a particular host.

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Quotes from Members

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

Pros

"Datadog has positively impacted my organization by shortening our time to resolve incidents because it's a central place for getting all the data that we need for troubleshooting."

"Several critical dashboards were created years ago and are still in use today."

"The solution is useful for monitoring logs."

"Datadog agents act as an integration to different services, providing easy access and management."

"Datadog infrastructure monitoring has helped us identify health issues with our virtual machines, such as high load, CPU, and disk usage, as well as monitoring uptime and alerting when Kubernetes containers have a bad time staying up."

"Datadog will positively impact my organization by allowing me to handle ticket resolutions at a much faster pace and bring productivity by reducing the number of support engineers required at the monitoring level."

"The ingestion points are unlimited and support customization. We haven't had anything yet that we haven't been able to integrate with it."

"The observability pipelines are the most valuable aspect of the solution."

More Datadog pros

"The deployment is very fast."

"The solution is stable and reliable."

"The filter feature on Stackify is one of the features I found valuable. It's awesome. When I want to get the application logs, the solution gives me many filters. For example, if I want to get logs from my test environment, the option is there for me to select the environment from Stackify, and you can also select the particular application, and you'll see the information you need there. The filter feature alone and the fact that Stackify offers a lot of different filters is what I like the most about the solution because I've used other tools with the filter feature, but the filtering was very difficult, versus Stackify that has good filtering. On Stackify, you can filter the information by the last one hour, or the last four hours, and you can also select the date range and specify the timestamp, then the solution will give you the information based on the date range you specified. Another feature I found valuable on Stackify is its rating feature because it tells you how your application is faring. For example, a rating of A means excellent, while a rating of F means very bad, or that your application is not doing well at all. The ratings are from A to F. I also like that Stackify helps you in terms of load management because the solution gives you information on overutilized resources. These are the most valuable features of the solution."

"The performance dashboard and the accurate level of details are beneficial."

Cons

"While I like the ease of use, when compared with Tenable Nessus they could still improve their usability."

"We would like to see some versioning system for the Synthetic Tests so that we could have a backup of our tests since they are time-consuming to make and very easy to damage in a moment of error."

"We would really like to see more from the Service Catalog."

"It's not just that Datadog is expensive—it's that the cost is incredibly complex and hard to predict."

"I would like testing for data in the future."

"There should be a clearer view of the expenses."

"Datadog has a lot of documentation, but a lot of that documentation assumes you know how the service works, which can lead to confusion."

"While it’s powerful, the interface can feel cluttered and overwhelming for new users."

More Datadog cons

"I've not used Stackify for a while, and I'm currently using a solution now that's not as good as Stackify. Among the solutions I've been using so far, Stackify has been one of the best for me, but there's always room for improvement. For example, I don't know if it's just me, but when I try to get the log from Stackify, sometimes it doesn't appear in real-time. It takes a few minutes before the logs appear. When I redeploy my solution and the application starts, I don't see the logs immediately, and it would take two to three minutes before I see the logs. I don't know if other customers have a similar experience. It's the wait time for the logs to appear that's a concern for me, could be improved, and is what the Stackify team should be looking into. In terms of any additional feature that I'd like added to the solution, I'm not sure if Stackify has a way to export logs out. I've been trying to do it. On the solution, you can click on a spiral-like icon and it shows you the entire error, and I'd prefer an export button that would let me download the error and save that into a text file, for example, so it'll be available on my local machine for me to reference it, especially because the log keeps going and as you're using the solution, the system keeps pushing messages on to Stackify, so if I'm looking at a particular error at 12:05 PM, for example, by the time I go back to my system and would like to revisit the error at 12:25 PM, on Stackify, the logs would have gone past that level and I won't see it again which makes it difficult. When you now go back to that timestamp, you don't tend to see it immediately, but if the solution had an export feature for me to save that particular error information on my local machine for reference at a later time, I won't have to go back to Stackify. I just go to that log, specifically to that particular export that I've received on my local machine. I can get it and review it, and it would be easier that way versus me going back to Stackify to find that particular error and request that particular information."

"I would like to be able to see metrics about individual running containers on the host machines."

"It should be easily scalable and configurable in different instances."

"The search feature could be improved."

Pricing and Cost Advice

"This solution is budget friendly."

"The price is better than some competing products."

"I am not satisfied with its licensing. Its payment is based on the exported data, and there was an explosion of the data for three or four weeks. My customer was not alerted, and there was no way for them to see that there has been an explosion of data. They got a big invoice for one or two months. The pricing model of Datadog is based on the data. The customer was quite surprised about not being alerted about this explosion of data. They should provide some kind of alert when there is an increase in usage."

"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."

"The pricing and licensing through AWS Marketplace has been good. It would be nice if it was cheaper, but their pricing is reasonable for what it is. Sometimes, for their newer features, they charge as if it's fully fleshed out, even though it is a newer feature and it may have less stuff than their other items."

"The solution is fairly priced but history and log storage can get costly depending on your needs."

"Pricing seems reasonable. It depends on the size of your organization, the size of your infrastructure, and what portion of your overall business costs go toward infrastructure."

"The solution's pricing depends on project volume."

More Datadog pricing and cost advice

"The price is variable. It depends on how much data we have received in that particular month. Usually, it goes up to $2,000, or, at times, $3,000 USD per month."

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Comms Service Provider

11%

Media Company

11%

Performing Arts

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	80
Midsize Enterprise	46
Large Enterprise	99

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	2

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...

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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...

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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 ...

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Comparisons

Dynatrace vs Datadog

Compared 9% of the time

Azure Monitor vs Datadog

Compared 5% of the time

New Relic vs Datadog

Compared 4% of the time

Splunk Enterprise Security vs Datadog

Compared 3% of the time

Splunk AppDynamics vs Datadog

Compared 3% of the time

More Datadog Competitors

New Relic vs Stackify

Compared 14% of the time

Splunk Enterprise Security vs Stackify

Compared 7% of the time

DX Spectrum vs Stackify

Compared 6% of the time

Nagios XI vs Stackify

Compared 6% of the time

Dynatrace vs Stackify

Compared 6% of the time

More Stackify Competitors

Product Reports

Buyer's Guide

Datadog

January 2026

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Buyer's Guide

Stackify

January 2026

Download Stackify product report

Overview

Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.

Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.

What are Datadog's Key Features?

Sharable Dashboards: Facilitate collaboration with customizable, intuitive dashboards.
Extensive Integrations: Seamlessly connects with tools like Slack and AWS to support workflows.
APM and RUM: Provides deep insights into application performance and user interactions.
Real-Time Tools: Enables agile responses to system issues with real-time monitoring.
Unified Tagging and Visualization: Eases management of complex systems with simplified visualization.

What Benefits and ROI Should Users Look For?

Efficient Troubleshooting: Centralized logging speeds up the identification and resolution of issues.
Streamlined Workflows: Integrations with popular tools enhance operational efficiency.
Improved Observability: Comprehensive monitoring improves system reliability and performance.
Resource Optimization: Efficient microservices and container management leads to better cloud resource utilization.
Responsive Alerts: Customizable alerts ensure timely identification of critical issues.

In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and proactive issue resolution.

Datadog

Stackify is an application performance management (APM) solution that combines application performance monitoring with logs, errors, and reporting. It is a SaaS solution that is developer-focused. Users can quickly scan, identify, and repair issues with applications. Stackify APM offers valuable tools, such as Prefix and Retrace, which help to make it a comprehensive and valuable APM solution. Stackify is now part of the Netreo family of IT Infrastructure Management (ITIM), which is considered one of the fastest-growing IT organizations in the marketplace today.

Stackify Prefix

Stackify Prefix helps developers write better code, faster. The tool examines, tests, and approves code as it is being written. Almost every new application is code-perfect, negating the need for exhausting troubleshooting and frustrating time-consuming code review.

Prefix is able to discover poor-performing SQL queries, ORM queries, potential bottlenecks, and concealed exceptions prior to moving the application into production.

Prefix offers Summary Dashboards, intuitive suggestions, integrated logs, and distributed tracing. Distributed tracing expands visibility to cloud-native applications, microservices, and containers and can also provide additional transparency to cache services, web services, third-party services, and more. Users are able to easily move from logs to traces and back.

This valuable tool ensures developers are able to consistently release the best code possible in the least amount of time, while improving performance, productivity, and profitability.

Prefix is a very robust and easy-to-use tool. It can be used seamlessly with Linux, macOS, and Windows. Prefix integrates well with numerous languages, such as Java, Python, Ruby, PHP, Node.js, .Net, and .Net Core.

Stackify Retrace

Stackify Retrace is a user-friendly, trusted APM solution used in more than fifty countries worldwide. Users know that Retrace is able to ensure they can complete quicker, more efficient application development and consistently enhance overall application performance by suggesting important intuitive suggestions users need.

This solution is beneficial to both developers (Dev) and operations (Ops) personnel to learn to improve code and immediately finetune issues by:

Establishing effortless collaboration between Dev and Ops personnel via an easy-to-use GUI dashboard.
Delivering complete transparency of all stages of the application development process, from pre-development to production.
Utilizing performance protocols, such as error tracking, application logs, and code profiling, in real time in order to thoroughly understand how long a code will take to complete various tasks.
Improving overall efficiency and productivity by immediately discovering and repairing application issues.

Retrace Real User Monitoring (RUM) uses both front-end and back-end monitoring to give users a complete picture of what is going on with the applications. This intuitive dashboard displays performance with a complete breakdown of resource usage and integrates the server-side and client traces into one engaging, user-friendly, extensive view.

Retrace is an out-of-the-box solution that works seamlessly with Java stacks, PHP, Node.js, Ruby, Python, .Net, and .Net Core. It is also compatible with many of today’s popular frameworks, such as AWS, Azure, Elasticsearch, MongoDB, MySQL, Oracle, PostgreSQL, Redis, and SQL Server. Additionally, Retrace will work effectively with many cloud providers, containers, and languages, and offers excellent and easy integration with today's favorite tools such as Jira, Slack, Jenkins, and more.

Stackify

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

MyRacePass, ClearSale, Newitts, Carbonite, Boston Software, Children's International, Starkwood Media Group, Fewzion

Buyer's Guide

Datadog vs. Stackify

January 2026

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