Try our new research platform with insights from 80,000+ expert users

Amazon EC2 Auto Scaling vs Apache Spark comparison

 

Comparison Buyer's Guide

Executive Summary

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

Amazon EC2 Auto Scaling
Ranking in Compute Service
3rd
Average Rating
9.0
Reviews Sentiment
8.2
Number of Reviews
46
Ranking in other categories
No ranking in other categories
Apache Spark
Ranking in Compute Service
4th
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
65
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
 

Mindshare comparison

As of April 2025, in the Compute Service category, the mindshare of Amazon EC2 Auto Scaling is 10.8%, down from 12.6% compared to the previous year. The mindshare of Apache Spark is 11.2%, up from 9.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service
 

Featured Reviews

Erick  Karanja - PeerSpot reviewer
Scaling is as easy as hitting a button and setup is straightforward
AWS has already made improvements. In the past, if you provisioned a large EC2 instance and underutilized it, you still paid a premium. Now, AWS encourages using Kubernetes, where you primarily pay for the compute power you actually use in production. There is room for improvement. You might end up paying a high price if you're not careful and you provision a server that's underutilized. AWS has left it to engineers to figure out solutions. If you find the cost too high, you can move to Kubernetes, which might be a better solution for you than large EC2 instances. So, the improvements need to come from the user side, not the provider. Software engineers and engineering teams need to know their limits with EC2 instances. They need to recognize when it's time to transition their applications to Kubernetes. This means building with the cloud in mind from the start, making it easier to move solutions to the cloud without suffering upgrades and integration issues.
Ilya Afanasyev - PeerSpot reviewer
Reliable, able to expand, and handle large amounts of data well
We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

Quotes from Members

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

Pros

"The integration capabilities are good."
"Sometimes, Auto Scaling is more beneficial, and sometimes, Reserved Instances are preferred, especially for longer-term usage."
"The product is flexible."
"Amazon EC2 Auto Scaling has good integration."
"The initial setup of Amazon EC2 Auto Scaling is easy...Since we are an enterprise-sized company and a client of Amazon, the response from the technical support team was immediate."
"The documentation is good."
"The most useful feature is elasticity. You can scale up or down based on traffic."
"It uses features like target tracking scaling policy, which automatically maintains CPU utilization levels."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The main feature that we find valuable is that it is very fast."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"The solution is very stable."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The product is useful for analytics."
 

Cons

"The technical support needs to be improved."
"The product's setup is complex for an intermediate user."
"There is a need for improvement in understanding the pricing structure, as it is complex and depends on several factors such as the location of data centers."
"There is room for improvement in the pricing model."
"The spinning up in the solution can be much faster...The product should have a faster scalability option."
"For future improvements, I suggest focusing on cost reduction."
"As we are transitioning to managing containerized applications, the solution could improve by adding more managed container services as a feature in the solution."
"The support to manage the processes could be better."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"Apache Spark should add some resource management improvements to the algorithms."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The main concern is the overhead of Java when distributed processing is not necessary."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"The solution must improve its performance."
"The solution needs to optimize shuffling between workers."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
 

Pricing and Cost Advice

"The solution is not expensive."
"The product's pricing depends on the traffic and workload."
"There is no specific pricing for Amazon EC2 Auto Scaling, but we have to pay for the number of machines getting scaled up."
"The solution's licensing is based on a pay-as-you-go model. You only pay for the resources you use, whether it's RAM, processing power, or storage. So, it's calculated based on the time you use those resources, typically billed in hours or minutes."
"Pricing could be a little bit more competitive."
"I have not explored the price of the solution extensively, but from what I have seen the price is alright."
"The product is quite expensive."
"The product is expensive."
"It is an open-source platform. We do not pay for its subscription."
"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"The product is expensive, considering the setup."
"Spark is an open-source solution, so there are no licensing costs."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"Apache Spark is an expensive solution."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
report
Use our free recommendation engine to learn which Compute Service solutions are best for your needs.
847,625 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
15%
Government
7%
Real Estate/Law Firm
7%
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon EC2 Auto Scaling?
The solution removes the need for hardware. We can easily create servers or machines. Just by clicking or specifying our requirements, like memory size or disk space, it's set up for us. The tool e...
What is your experience regarding pricing and costs for Amazon EC2 Auto Scaling?
The pricing structure from AWS is really complex and depends on factors like the region and specific services used. Prices can vary significantly even within the same service across different locat...
What needs improvement with Amazon EC2 Auto Scaling?
There is a need for improvement in understanding the pricing structure, as it is complex and depends on several factors such as the location of data centers.
What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...
What needs improvement with Apache Spark?
The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...
 

Also Known As

AWS RAM
No data available
 

Overview

 

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Find out what your peers are saying about Amazon EC2 Auto Scaling vs. Apache Spark and other solutions. Updated: March 2025.
847,625 professionals have used our research since 2012.