AWS Auto Scaling vs Akamai mPulse comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Akamai mPulse
Ranking in Application Performance Monitoring (APM) and Observability
53rd
Average Rating
6.6
Number of Reviews
6
Ranking in other categories
No ranking in other categories
AWS Auto Scaling
Ranking in Application Performance Monitoring (APM) and Observability
15th
Average Rating
8.8
Number of Reviews
18
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2024, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Akamai mPulse is 0.4%, down from 0.6% compared to the previous year. The mindshare of AWS Auto Scaling is 0.1%, down from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Application Performance Monitoring (APM) and Observability
Unique Categories:
No other categories found
No other categories found
 

Featured Reviews

Arivu Arumugam - PeerSpot reviewer
Oct 18, 2022
Lacking in regard to observation of the entire platform but does dynamic injections from within
The primary use case of mPulse is for real-time user monitoring. Our use case was to observe the entire platform from the user all the way to the backend system and that's what it did. It's all in the discovery state. We explored a variety of different tools and the pros and cons of each within our…
Alex Kabugo - PeerSpot reviewer
Aug 21, 2023
Addresses issues with malfunctioning or unavailable EC2 instances
The good thing about Autoscaling is that it provides the capacity to minimize downtime. So, it gives you the assurance of stability and robustness within your system We deal with applications – specifically, the ones deployed in the production environment of the cloud. So, the aim is to ensure…

Quotes from Members

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

Pros

"Enables dynamic injections from within the product which is great."
"The most valuable feature is the solutions overall performance. It is very efficient and accurate for our usage."
"The solution's monitoring effectively monitors our application and CPU utilization."
"It is a stable platform."
"The tool gives you the flexibility to scale up and grow. The solution is also fast to deploy."
"It helps us to reduce the cost."
"It efficiently handles traffic, ensuring we are not running expenses and the infrastructure is strong enough to handle the load."
"AWS Auto Scaling is cost-effective and very useful for businesses."
"The most valuable feature of the solution is that it scales automatically without manual intervention based on the metrics we provide."
"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."
 

Cons

"The end-to-end distributor tracing connectivity isn't there."
"In the next release, I would like to see the possibility of sharing the metric from this solution with other solutions."
"The product could add more features for managing instances."
"The solution is not out-of-the-box and you have to study to use it. It should be more easier to use."
"The billing and cost optimization of the solution could be improved."
"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."
"AWS Auto Scaling's documentation could be better."
"Flexibility in configuring the workload is missing in AWS Auto Scaling."
"The tool's stability could be improved."
"The product’s pricing needs improvement."
 

Pricing and Cost Advice

Information not available
"The product has moderate pricing."
"AWS Auto Scaling's price is high."
"AWS Auto Scaling is an expensive solution."
"AWS Auto Scaling is a pay-per-use and pay-as-you-use service."
"AWS Auto Scaling is a cheap solution."
"The product is expensive."
"The pricing is good. I have not had any customers that have complained about the price."
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Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
12%
Retailer
9%
Government
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Akamai mPulse?
Enables dynamic injections from within the product which is great.
What needs improvement with Akamai mPulse?
The end-to-end distributor tracing needs some work because the connectivity from the user to the backend wasn't there. Akamai mPulse was not able to deliver on our requirements regarding observatio...
What is your primary use case for Akamai mPulse?
The primary use case of mPulse is for real-time user monitoring. Our use case was to observe the entire platform from the user all the way to the backend system and that's what it did. It's all in ...
What do you like most about AWS Auto Scaling?
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. W...
What is your experience regarding pricing and costs for AWS Auto Scaling?
AWS Auto Scaling is an expensive solution. We pay a monthly licensing fee for the solution.
What needs improvement with AWS Auto Scaling?
In comparison to other public clouds, the product is costly.
 

Comparisons

No data available
 

Also Known As

SOASTA mPulse
AWS Auto-Scaling
 

Overview

 

Sample Customers

Nordstrom, Gatwick, DirecTV, MSN, SquareSpace, SAP, Lenovo, Hallmark, myspace, Intuit, Kentucky Derby, Toys "R" Us, Netflix, Newsweek, The Washington Post, Lowe's, Nike, REI, Apple, Sears, Verizon, Wendy's, Huawei
Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
Find out what your peers are saying about AWS Auto Scaling vs. Akamai mPulse and other solutions. Updated: May 2024.
787,779 professionals have used our research since 2012.