Amazon EC2 vs Apache Spark comparison

Amazon Web Services (AWS) and Apache are both solutions in the Compute Service category. Amazon Web Services (AWS) is ranked #6 with an average rating of 9.1, while Apache is ranked #4 with an average rating of 8.6. Amazon Web Services (AWS) holds a 5.1% mindshare in CS, compared to Apache’s 11.5% mindshare. Additionally, 98% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon EC2

Read 67 Amazon EC2 reviews

895 Views
427 Comparison Views

98% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,209 Views
833 Comparison Views

90% willing to recommend

Amazon EC2

Apache Spark

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Executive SummaryUpdated on May 21, 2025

Apache Spark and Amazon EC2 are both leading solutions in the data processing and cloud computing domains, respectively. Apache Spark, known for its in-memory data processing, seems to have a competitive edge due to its speed and scalability in handling large datasets efficiently, while Amazon EC2 excels in flexible scalability and integration with AWS services despite having complex pricing structures.

Features: Apache Spark is designed for in-memory data processing, enabling efficient handling of large datasets. It includes Spark Streaming for real-time data processing, Spark SQL for querying large data volumes economically, and MLlib for machine learning. Amazon EC2 provides flexible and scalable cloud computing services with the capability to quickly launch and manage server instances. It integrates well with other AWS services, offering cost-effective scalability and versatility.

Room for Improvement: Apache Spark could enhance stability, scalability, and integration with BI tools, as real-time querying limitations and complex APIs pose challenges. It also lacks user-friendly interfaces and comprehensive documentation. Amazon EC2 is critiqued for its intricate pricing structures, leading to potential high costs and occasional connectivity issues during AMI upgrades, necessitating better integration and cost management.

Ease of Deployment and Customer Service: Apache Spark can be deployed in both on-premises and cloud environments, offering flexibility. As an open-source solution, its support is community-driven, which can sometimes lack depth and immediacy. Amazon EC2 operates within the public cloud, well-regarded for ease of use and deployment. Its customer service is more structured, providing comprehensive support options for commercial users.

Pricing and ROI: Apache Spark, being open-source, provides cost savings on software, though requires significant resources for optimal performance, impacting operational costs. It delivers substantial ROI through reduced operational expenses and its mature ecosystem. Amazon EC2 follows a pay-as-you-go model, perceived as expensive due to its complex billing structure. Regardless, it offers flexibility in instance types, improving ROI when usage is strategically managed.

To learn more, read our detailed Amazon EC2 vs. Apache Spark Report (Updated: June 2025).

Buyer's Guide

Amazon EC2 vs. Apache Spark

June 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

Amazon EC2

Ranking in Compute Service

6th

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark

Ranking in Compute Service

4th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Hadoop (1st), Java Frameworks (2nd)

Mindshare comparison

As of July 2025, in the Compute Service category, the mindshare of Amazon EC2 is 5.1%, down from 7.5% compared to the previous year. The mindshare of Apache Spark is 11.5%, up from 11.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

KatlegoMabila

Senior AWS Account Manager at Silicon Overdrive

Offers customization and flexibility with great support

Scalability depends on whether the client wants to scale up or scale down. It decreases resources based on demand. The great aspect of scalability is the flexibility to allow business success to optimize resource solutions and cost efficiency. Another crucial aspect of scalability is auto-scaling. When you have the opportunity to auto-scale, it can't always be available for everything. If you have chosen to integrate with auto-scaling, it's marvellous and doesn't require additional effort. Auto-scaling gives you the edge by using the capacity you have efficiently, scaling up or down as needed. These flexibilities within the EC2 feature instances of AWS play a crucial role in helping me utilize AWS EC2 Intelligent efficiently.

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Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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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 most valuable features are the scalability options, low maintenance, and options to upgrade. AWS support is also pretty good. The generation upgrade is pretty simple and standardized."

"This is a user-friendly solution."

"Its ease of use is valuable."

"EC2 has the typical advantages of using the cloud. It's easy to provision and set up."

"Stable, scalable, and simple to implement."

"The ethernet configuration is stable and the product is reliable."

"The product is easy and quick to set up."

"The tool's performance, reliability, security and flexibility are good. We can use it remotely. The autoscaling functionality of EC2 is quite good. I appreciate the DevOps suite for tracking development tasks. This functionality is important for pure software development."

More Amazon EC2 pros

"Features include machine learning, real time streaming, and data processing."

"The solution is very stable."

"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"The product is useful for analytics."

"We use Spark to process data from different data sources."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"The solution is scalable."

More Apache Spark pros

Cons

"We found Amazon EC2 to be pricey."

"EC2 is a little expensive."

"I think the pricing needs to be adjusted and better security."

"The scalability could improve."

"I would like to see improvement in the information available up-front for users around tailoring the package to their actual requirements. At present it can take time to work with the on demand instance until you are used to what features are right for the user."

"The product could benefit from offering more mixed instance types that combine features from different series to suit diverse workload requirements better."

"The solution is pretty expensive."

"The tool’s stability could be better."

More Amazon EC2 cons

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

"The product could improve the user interface and make it easier for new users."

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

More Apache Spark cons

Pricing and Cost Advice

"The price of Amazon EC2 could improve. The Google Cloud Platform is more cost-effective."

"Reducing the price of the solution could lead to an improvement."

"The price of Amazon EC2 could improve. The Google Cloud Platform is more cost-effective."

"The license fee for Amazon EC2 is higher than its competitors."

"It's competitive but can vary based on instance types and usage patterns."

"The licensing of Amazon EC2 is expensive. Microsoft Windows Servers are expensive to license."

"It is not an expensive solution."

"We are using a pay-as-you-go model."

More Amazon EC2 pricing and cost advice

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

"We are using the free version of the solution."

"The solution is affordable and there are no additional licensing costs."

"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."

"Apache Spark is an expensive solution."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"It is an open-source solution, it is free of charge."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

19%

Financial Services Firm

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Amazon EC2?

The scalability and elasticity are helpful.

See all answers

What is your experience regarding pricing and costs for Amazon EC2?

Amazon EC2 is a bit expensive.

See all answers

What needs improvement with Amazon EC2?

The main thing that needs improvement is the cost. Other than that, there is nothing that needs improvement.

See all answers

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

See all answers

Comparisons

AWS Batch vs Amazon EC2

Compared 28% of the time

AWS Lambda vs Amazon EC2

Compared 19% of the time

AWS Fargate vs Amazon EC2

Compared 18% of the time

Google Compute Engine vs Amazon EC2

Compared 13% of the time

More Amazon EC2 Competitors

Spring Boot vs Apache Spark

Compared 25% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EC2

June 2025

Download Amazon EC2 product report

Buyer's Guide

Apache Spark

June 2025

Download Apache Spark product report

Also Known As

Amazon Elastic Compute Cloud, EC2

No data available

Overview

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.

Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment. Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change. Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate them from common failure scenarios.

Amazon Web Services (AWS)

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Sample Customers

Netflix, Expedia, TimeInc., Novaris, airbnb, Lamborghini

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

Buyer's Guide

Amazon EC2 vs. Apache Spark

June 2025

Free Report: Amazon EC2 vs. Apache Spark

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