AWS Auto Scaling vs Stackify comparison

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AWS Auto Scaling

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Executive SummaryUpdated on Jul 24, 2024

Stackify and AWS Auto Scaling are prominent solutions in the cloud management space. AWS Auto Scaling appears to have the upper hand due to its extensive feature set and robust scalability.

Features: Stackify is praised for its intuitive application performance tracking, error monitoring features, and real-time logs and metrics. AWS Auto Scaling is valued for its adaptive scaling based on real-time demand, seamless integration with other AWS services, and automation capabilities.

Room for Improvement: Stackify users suggest enhancements in its integration capabilities and performance lags. AWS Auto Scaling users would like improvements in initial setup complexity and more straightforward user documentation.

Ease of Deployment and Customer Service: Stackify users highlight easy implementation and responsive customer service. AWS Auto Scaling's deployment is complex, requiring more expertise, but has extensive AWS support resources available.

Pricing and ROI: Stackify users appreciate its cost-effectiveness and quick ROI through rapid issue diagnosis and resolution. AWS Auto Scaling users find pricing higher but justify it with the range and power of features for long-term ROI through efficient resource management.

To learn more, read our detailed AWS Auto Scaling vs. Stackify Report (Updated: June 2026).

AWS Auto Scaling vs. Stackify

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Helped 902,588 peers since 2012

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

AWS Auto Scaling

Ranking in Application Performance Monitoring (APM) and Observability

39th

Average Rating

8.8

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Stackify

Ranking in Application Performance Monitoring (APM) and Observability

58th

Average Rating

7.8

Number of Reviews

Ranking in other categories

IT Infrastructure Monitoring (67th), Log Management (57th)

Mindshare comparison

As of July 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of AWS Auto Scaling is 0.5%, up from 0.2% compared to the previous year. The mindshare of Stackify is 0.7%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Application Performance Monitoring (APM) and Observability Mindshare Distribution
Product	Mindshare (%)
AWS Auto Scaling	0.5%
Stackify	0.7%
Other	98.8%

Application Performance Monitoring (APM) and Observability

Featured Reviews

Ishaka Michael Efe

DevOps Engineer Intern at HNG Tech

Automation has simplified traffic management and improved workload efficiency

I'm not really sure what improvements I would want to see in AWS Auto Scaling because I haven't really used it extensively or explored most of it. To the extent that I've used it so far, I think it is very good. I can't really say for certain what should be improved because I haven't really explored it a lot. However, what I've been using it for has been very good. If there could be training for AWS Auto Scaling, that would be fine. If you could add more training on how to use it correctly and on the functions that I haven't used before or some people have not really used before, that would help. If there could be more documentation and training on it, that would be beneficial.

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Idoko Emmanuel

Senior Software Engineer at a tech services company with 1,001-5,000 employees

Has good filtering and rating features and helps with resource and load management

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.

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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 solution helps optimize the cost of the AWS environment."

"It can scale."

"The various scaling options available, such as step scaling, are particularly useful."

"The tool gives you the flexibility to scale up and grow. The solution is also fast to deploy."

"Our internal business applications are hosted in AWS Auto Scaling."

"Auto Scaling is a cool feature that works well and its automatic scaling capabilities are very useful."

"The setup is not very complex."

"The tool's most valuable feature is vertical auto-scaling, which is easy to use. However, most companies now prefer horizontal scaling. I set up the health check integration to monitor CPU usage. When it reaches seventy percent, it sends me an email notification."

More AWS Auto Scaling pros

"What stood out to us were the metrics and granular details we received."

"The solution is stable and reliable."

"The deployment is very fast."

"My advice to anyone who wants to use Stackify is to go for it because my experience with it is good."

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

"We switched from New Relic and Loggly as it provides us more info at a lower price."

"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."

"Within few hours of install we've identify the source of issue we've been investigating for few days and couldn't pin point."

More Stackify pros

Cons

"The product could add more features for managing instances."

"It is sometimes very critical to deploy on AWS since some servers are already running in the background. There are challenges for employees on how to deploy at a given time."

"Flexibility in configuring the workload is missing in AWS Auto Scaling."

"The tool must include AI features."

"We can have more auto scaling algorithms implemented in AWS Auto Scaling."

"Setting up the configuration involves too much work for the cloud engineer, like configuring the ALB, the target group, and all the steps."

"The only area of improvement is the speed at which servers are launched. When cleaning up to six servers at a time, it can take up to 15 to 20 minutes to launch new servers."

"It could be cheaper."

More AWS Auto Scaling cons

"The search feature could be improved."

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

"Another improvement would be the agent memory utilization, which led to our recent reevaluation."

"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."

"Better mobile support."

"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."

"It's not easy to set up. It's hard especially for juniors to understand."

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

More Stackify cons

Pricing and Cost Advice

"AWS Auto Scaling is a pay-per-use and pay-as-you-use service."

"The product is expensive."

"AWS Auto Scaling's price is high."

"The pricing is good. I have not had any customers that have complained about the price."

"AWS Auto Scaling is a cheap solution."

"AWS Auto Scaling is an expensive solution."

"The product has moderate pricing."

"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

Construction Company

17%

Financial Services Firm

11%

Educational Organization

Comms Service Provider

Construction Company

21%

Comms Service Provider

13%

Media Company

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	13
Midsize Enterprise	2
Large Enterprise	12

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

Questions from the Community

What is your experience regarding pricing and costs for AWS Auto Scaling?

I'm not sure about the pricing aspect of AWS Auto Scaling or the cost of it.

See all answers

What needs improvement with AWS Auto Scaling?

AWS Auto Scaling can be improved in many ways, such as providing better visibility into scaling decisions and clarifying why exactly scaling happens. Sometimes, I am not sure why scaling occurs. Ea...

See all answers

What is your primary use case for AWS Auto Scaling?

My main use case for AWS Auto Scaling is automatically scaling EC2 instances behind a load balancer based on traffic, CPU, and memory usage, as well as custom CloudWatch metrics. I also use it with...

See all answers

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Comparisons

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Snyk vs AWS Auto Scaling

Compared 7% of the time

Alluvio AppResponse vs AWS Auto Scaling

Compared 6% of the time

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New Relic vs Stackify

Compared 7% of the time

Dynatrace vs Stackify

Compared 6% of the time

Splunk Enterprise Security vs Stackify

Compared 5% of the time

OpenText SiteScope vs Stackify

Compared 4% of the time

Splunk AppDynamics vs Stackify

Compared 4% of the time

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Product Reports

Application Performance Monitoring (APM) and Observability

Download AWS Auto Scaling product report

Download Stackify product report

Also Known As

AWS Auto-Scaling

No data available

Overview

AWS Auto Scaling optimizes resource use by automatically adjusting instances based on demand. It integrates with CloudWatch for seamless monitoring, enhancing system reliability and cost efficiency without manual intervention.

AWS Auto Scaling is designed to dynamically scale resources in response to demand, supporting horizontal and vertical scaling for optimal performance. It integrates well with AWS services like EC2 and ECS, allowing for flexible and scalable solutions. Predictive scaling and intelligent automation reduce costs and ensure reliability, particularly during unpredictable traffic variations. Users implement it to maintain efficiency and minimize downtime, benefiting from features such as self-healing and health checks.

What are the key features of AWS Auto Scaling?

Automatic Scaling: Adjusts resources based on real-time demand metrics.
CloudWatch Integration: Seamless monitoring integration for efficient resource management.
Target Tracking: Maintains desired performance levels by adjusting resources as needed.
Scheduled Scaling: Pre-configure adjustments based on expected demand changes.
Self-Healing: Automatically replaces unhealthy instances to ensure maximum uptime.

What benefits should users expect from AWS Auto Scaling?

Cost Efficiency: Reduces cost by adjusting resources based on fluctuating demand.
Reliable Performance: Ensures consistent performance even with varying traffic levels.
Minimal Downtime: Automatically adapts to traffic changes to keep services running smoothly.
User-Friendly Interface: Simple operation with intuitive controls and configurations.
Flexibility: Supports a range of AWS services, adapting to different use cases and demands.

In industries with variable demand, AWS Auto Scaling is deployed to manage real-time traffic surges, ensuring efficient use of resources during periods such as events and festive seasons. Users grow dynamic environments while balancing costs and maintaining stability, integrating the tool with CI/CD processes for continuous and efficient deployment.

Amazon Web Services (AWS)

Stackify enhances application performance monitoring with features like detailed logging and effective filtering options, integrating capabilities to eliminate the need for multiple tools, and providing rapid deployment and stable performance.

Stackify offers a comprehensive solution for business insight through custom dashboards, error monitoring, load management, and application scoring. Its ability to visualize performance by creating responsive dashboards helps connect business and technical teams. Despite its strengths, users seek improvements such as better mobile support, real-time log appearance, and Docker documentation. Enhanced Linux support and agent memory optimization are also desired, along with better visibility into container and application performance metrics. Integrating more plugins and options would make Stackify a more robust monitoring tool.

What are the key features of Stackify?

Detailed Log and Error Data: Provides in-depth traces and logs for error and data analysis.
Custom Dashboards: Offers business insights and application scoring through tailored dashboards.
Performance Metrics: Monitors application and server performance effectively.
Simple Configuration: Ensures easier setup and deployment without complexities.
Filtering Options: Enables effective management of logs and errors through filters.

What benefits should be expected from using Stackify?

Improved Performance Monitoring: Provides tools to identify root causes and enhance application and server monitoring.
Resource Management: Manages server utilization and handles resource spikes efficiently.
Proactive Alerts: Allows setting up alerts across platforms like AWS and Docker.
Log Aggregation: Compiles logs for testing and analysis to improve system performance.

Organizations across industries use Stackify for extensive application performance monitoring, particularly in testing environments. It is crucial for analyzing database calls, third-party requests, and aggregating logs. Its capabilities in monitoring across AWS and Docker environments make it especially valuable for dynamic and distributed infrastructures.

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

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

AWS Auto Scaling vs. Stackify