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 #60. Datadog holds a 8.5% mindshare in APM, compared to Stackify’s 0.2% mindshare. Additionally, 97% of Datadog users are willing to recommend the solution, compared to 71% of Stackify users who would recommend it.

Datadog

Read 187 Datadog reviews

18,982 Views
13,021 Comparison Views

97% willing to recommend

Stackify

Read 6 Stackify reviews

484 Views
270 Comparison Views

71% willing to recommend

Datadog

Stackify

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Executive SummaryUpdated on Apr 6, 2025

Datadog and Stackify offer monitoring and performance solutions in operational efficiency, with Datadog leading due to its comprehensive analytics and integration options. Stackify, while noted for cost-effectiveness, attracts budget-conscious buyers.

Features: Datadog provides deep analytics, real-time alerting, and extensive integrations, favored for detailed insights and scalability. Stackify excels in application performance with tools for error tracking, log management, and focuses on efficiency.

Room for Improvement: Datadog could enhance user experience by refining anomaly detection and reducing false alerts. Additional documentation for customization would be beneficial. Stackify could improve by expanding integration capabilities and analytics depth, and providing more user training resources.

Ease of Deployment and Customer Service: Datadog's cloud-based deployment and abundant documentation offer seamless integration and responsive support. Stackify simplifies with an intuitive setup and active support channels, but is more straightforward in approach.

Pricing and ROI: Datadog's pricing reflects its extensive feature set, delivering strong ROI for advanced capability needs. Stackify's more affordable setup appeals to small enterprises, offering solid ROI with targeted functionalities.

To learn more, read our detailed Datadog vs. Stackify Report (Updated: May 2025).

Buyer's Guide

Datadog vs. Stackify

May 2025

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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

7.1

Number of Reviews

187

Ranking in other categories

Network Monitoring Software (4th), Container Monitoring (2nd), Cloud Monitoring Software (2nd), AIOps (1st), Cloud Security Posture Management (CSPM) (6th)

Stackify

Ranking in Application Performance Monitoring (APM) and Observability

60th

Ranking in IT Infrastructure Monitoring

58th

Ranking in Log Management

62nd

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Datadog is 8.5%, down from 10.6% compared to the previous year. The mindshare of Stackify is 0.2%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Application Performance Monitoring (APM) and Observability

Featured Reviews

Kevin Palmer

DevOps/Security Principal at Raken

Useful log aggregation and management with helpful metrics aggregation

Datadog provides us value in three major ways: First, Datadog provides best-in-class functionality in many, if not all, of the products to which we subscribe (infrastructure, APM, log management, serverless, synthetics, real user monitoring, DB monitoring). In my experience with other tools that provide similar functionality, Datadog provides the largest feature set with the most flexibility and the best performance. Second, Datadog allows us to access all of those services in one place. Having to learn and manage only one tool for all of those purposes is a major benefit. Third, Datadog provides significant connectivity between those services so that we can view, summarize, organize, translate and correlate our data with maximum effect. Not needing to manually integrate them to draw lines between those pieces of information is a huge time savings for us.

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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

"The ability to easily drill down into log queries quickly and efficiently has helped us to resolve several critical incidents."

"Across CCM and the rest of Datadog, I like how sharable everything is."

"Each component complements the other, creating a cohesive system where data, logs, and metrics are seamlessly integrated."

"The most valuable features of Datadog are the flexibility and additional features when compared to other solutions, such as AppDynamics and Dynatrace. Some of the features include AI and ML capabilities and cloud and analysis monitoring"

"Excellent autocomplete for everything in the UI."

"Synthetic testing is by far the most valuable feature in our organization."

"The centralized pipeline tracking and error logging provide a comprehensive view of our development and deployment processes, making it much easier to identify and resolve issues quickly."

"The ability to create custom dashboards has been incredibly useful, allowing us to visualize key metrics and KPIs in a way that makes sense for different teams and stakeholders."

More Datadog pros

"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 solution is stable and reliable."

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

"The deployment is very fast."

Cons

"One thing to improve would be making it easier to see common patterns across traces."

"A tool as powerful as Datadog is, understandably, going to have a bit of a learning curve, especially for new team members who are unfamiliar with the bevy of features it offers."

"The parallel editing of the dashboards should not cause users to lose the work of another person."

"We need to learn more about the session reply feature inside of DD."

"More helpful log search keywords/tips would be helpful in improving Datadog's log dashboard."

"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."

"It would be ideal if the product offered a bit more monitoring from our dashboard."

"Lately, chat support has a longer waiting time."

More Datadog cons

"The search feature could be improved."

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

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

"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."

Pricing and Cost Advice

"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."

"The cost is high and this can be justified if the scale of the environment is big."

"At my last company, we did see ROI, specifically around response time. We could get to mission critical things that were down and losing revenue on immediately. So, the product paid itself back."

"It has a module-based pricing model."

"Sometimes it's very hard to project how much it will cost for the monthly subscription for the next month when you add certain features. Having better visibility of the cost would give a better experience."

"Licensing is based on the retention period of logs and metrics."

"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."

"Pricing and licensing are reasonable for what they give you. You get the first five hosts free, which is fun to play around with. Then it's about four dollars a month per host, which is very affordable for what you get out of it. We have a lot of hosts that we put a lot of custom metrics into, and every host gives you an allowance for the number of custom metrics."

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

Computer Software Company

14%

Financial Services Firm

14%

Educational Organization

12%

Manufacturing Company

Financial Services Firm

19%

Computer Software Company

14%

Media Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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...

See all answers

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 12% of the time

Azure Monitor vs Datadog

Compared 10% of the time

AWS X-Ray vs Datadog

Compared 5% of the time

Splunk Enterprise Security vs Datadog

Compared 5% of the time

New Relic vs Datadog

Compared 4% of the time

More Datadog Competitors

New Relic vs Stackify

Compared 45% of the time

Dynatrace vs Stackify

Compared 36% of the time

Splunk Enterprise Security vs Stackify

Compared 19% of the time

More Stackify Competitors

Product Reports

Buyer's Guide

Datadog

June 2025

Download Datadog product report

Buyer's Guide

Stackify

June 2025

Download Stackify product report

Overview

Datadog is a comprehensive cloud monitoring platform designed to track performance, availability, and log aggregation for cloud resources like AWS, ECS, and Kubernetes. It offers robust tools for creating dashboards, observing user behavior, alerting, telemetry, security monitoring, and synthetic testing.

Datadog supports full observability across cloud providers and environments, enabling troubleshooting, error detection, and performance analysis to maintain system reliability. It offers detailed visualization of servers, integrates seamlessly with cloud providers like AWS, and provides powerful out-of-the-box dashboards and log analytics. Despite its strengths, users often note the need for better integration with other solutions and improved application-level insights. Common challenges include a complex pricing model, setup difficulties, and navigation issues. Users frequently mention the need for clearer documentation, faster loading times, enhanced error traceability, and better log management.

What are the key features of Datadog?

Graphs and Dashboards: Visualizes servers and infrastructure with customizable views.
Metrics: Offers deep insights into system performance and user behavior.
Log Management: Provides powerful analytics for logs and seamless log aggregation.
Unified Tagging: Facilitates organization and retrieval of metrics and logs.
APM and RUM: Enables application performance monitoring and real user monitoring for comprehensive insights.
Synthetic Testing: Allows the simulation of user interactions to test application performance.

What benefits and ROI should users look for in reviews?

System Reliability: Helps ensure uptime and performance across cloud environments.
Proactive Issue Resolution: Assists in identifying and resolving issues before they impact users.
Operational Efficiency: Streamlines monitoring and log management tasks.
Flexible Integrations: Adapts to various cloud services and infrastructure setups.
Real-Time Alerts: Provides timely notifications to address problems swiftly.

Datadog is implemented across different industries, from tech companies monitoring cloud applications to finance sectors ensuring transactional systems' performance. E-commerce platforms use Datadog to track and visualize user behavior and system health, while healthcare organizations utilize it for maintaining secure, compliant environments. Every implementation assists teams in customizing monitoring solutions specific to their industry's requirements.

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

May 2025

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