AWS Fargate vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #5 with an average rating of 8.7, while Amazon Web Services (AWS) is ranked #2 with an average rating of 8.6. Apache holds a 11.2% mindshare in CS, compared to Amazon Web Services (AWS)’s 9.8% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of Amazon Web Services (AWS) users who would recommend it.

Apache Spark

Read 68 Apache Spark reviews

3,530 Views
953 Comparison Views

90% willing to recommend

AWS Fargate

Read 20 AWS Fargate reviews

1,612 Views
1,419 Comparison Views

100% willing to recommend

Apache Spark

AWS Fargate

Comparison Buyer's Guide

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Executive SummaryUpdated on May 21, 2025

Apache Spark and AWS Fargate compete in large-scale data processing and cloud management arenas. While both offer compelling features, Apache Spark has the edge in handling big data through its in-memory processing capabilities, whereas AWS Fargate streamlines container management without infrastructure hassles.

Features: Apache Spark offers large-scale data processing with tools such as Spark Streaming for real-time event-driven applications, Spark SQL for low-cost data analysis, and MLlib for machine learning. Its strengths lie in fast performance, scalability, and extensive AI connectors. AWS Fargate simplifies container management by eliminating infrastructure management, providing easy integration with AWS services, and offering a user-friendly pay-as-you-go model.

Room for Improvement: Apache Spark users seek better scalability in real-time workflows, improved documentation, enhanced integration with BI tools, and more robust memory management. AWS Fargate could improve by simplifying dynamic scaling configurations, enhancing cost management features, and providing comprehensive setup documentation.

Ease of Deployment and Customer Service: Apache Spark can be deployed on-premises, in hybrid, or cloud environments, backed by community and optional paid support through Connectors like Cloudera. Customer service largely depends on community engagement. AWS Fargate is a cloud-native solution focusing on ease of use, supported by AWS's robust customer service, which offers direct technical assistance often unavailable in open-source platforms.

Pricing and ROI: Apache Spark is open-source with free use unless paired with products like Cloudera, potentially incurring infrastructure and operational costs. Its long-term efficiencies can translate to significant savings. AWS Fargate uses a pay-as-you-go model, often more costly than some AWS services, but justifies the expense by reducing the complexity of deployment and application management, aligning with varying scaling needs.

To learn more, read our detailed AWS Fargate vs. Apache Spark Report (Updated: December 2025).

Buyer's Guide

AWS Fargate vs. Apache Spark

December 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:

ROI

Sentiment score

6.1

Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.

Sentiment score

7.0

AWS Fargate enhances organizational efficiency and customer experience with cost-effective, scalable, serverless solutions, increasing operational capacity and reducing processing costs.

No quotes available

For more quotes and insights, download the Apache Spark report

The pay-as-you-go pricing model of AWS Fargate was one of the major drivers for us to move there because we reduced costs while increasing the quality of the processing services by about 30%.

Jesus_Gomez

Head of Infrastructure at Teamcore

For more quotes and insights, download the AWS Fargate report

Customer Service

Sentiment score

5.9

Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.

Sentiment score

6.8

AWS Fargate offers highly rated support and documentation, with proactive engagement enhancing user experience for all customers.

I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Even though we didn't contract support, every two weeks I had a 30-minute meeting with a cloud architect from AWS to help our team use different products of AWS, especially with SageMaker for a forecasting algorithm we were developing.

Jesus_Gomez

Head of Infrastructure at Teamcore

For pro support, AWS charges additional fees.

Deepak Nemade

Network and system administrator at AyanWorks

For more quotes and insights, download the AWS Fargate report

Scalability Issues

Sentiment score

7.5

Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.

Sentiment score

8.4

AWS Fargate efficiently handles demand fluctuations with dynamic scaling, maintaining high user satisfaction and scalability for containerized environments.

No quotes available

For more quotes and insights, download the Apache Spark report

No quotes available

For more quotes and insights, download the AWS Fargate report

Stability Issues

Sentiment score

7.4

Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.

Sentiment score

8.2

AWS Fargate offers high stability and reliability, ideal for low-traffic applications, but may not suit large-scale traffic.

Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

No quotes available

For more quotes and insights, download the AWS Fargate report

Room For Improvement

Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.

AWS Fargate needs cost efficiency, easier setup, improved documentation, better monitoring, scaling, and UI enhancements for user-friendliness.

Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

For a company that does not require complexity or managing Kubernetes clusters, AWS Fargate is a great way to go.

Jesus_Gomez

Head of Infrastructure at Teamcore

AWS Fargate is pretty straightforward for simple tasks and it should remain this way; an additional feature would make it complex and possibly not so stable.

Fedir Plotnikov

Senior DevOps Engineer at Blankfactor

They need to improve some UI-based interaction.

Deepak Nemade

Network and system administrator at AyanWorks

For more quotes and insights, download the AWS Fargate report

Setup Cost

Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.

AWS Fargate offers flexible consumption-based pricing, valuable for enterprises, though costlier than alternatives, with discounts improving affordability.

No quotes available

For more quotes and insights, download the Apache Spark report

No quotes available

For more quotes and insights, download the AWS Fargate report

Valuable Features

Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.

AWS Fargate offers serverless, auto-scaling container deployment, boosting productivity and cost-efficiency by removing infrastructure management concerns.

Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

It's very fast in terms of scaling my containers; it's much faster than other solutions.

Fedir Plotnikov

Senior DevOps Engineer at Blankfactor

What I find best about AWS Fargate is that compared to deploying containers on EC2, where we need to check everything manually such as uptime, error logs, and other issues, AWS Fargate manages all these aspects automatically.

Deepak Nemade

Network and system administrator at AyanWorks

One of the best features of AWS Fargate is that it was useful for us because we didn't require to run container workloads and we didn't need to deal with the management of a Kubernetes cluster directly, and the ability to run those workloads just in a scheduled manner is also a great feature.

Jesus_Gomez

Head of Infrastructure at Teamcore

For more quotes and insights, download the AWS Fargate report

Categories and Ranking

Apache Spark

Ranking in Compute Service

5th

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

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

AWS Fargate

Ranking in Compute Service

2nd

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Compute Service category, the mindshare of Apache Spark is 11.2%, down from 11.4% compared to the previous year. The mindshare of AWS Fargate is 9.8%, down from 15.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Market Share Distribution
Product	Market Share (%)
AWS Fargate	9.8%
Apache Spark	11.2%
Other	79.0%

Compute Service

Featured Reviews

Devindra Weerasooriya

Data Architect at Devtech

Provides a consistent framework for building data integration and access solutions with reliable performance

The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

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Jesus_Gomez

Head of Infrastructure at Teamcore

Flexibility in workload accommodation and supportive user experience drive efficiency

Currently, I think that the program is great the way it is, and maybe we use less than 50% of the current features of the platform. For example, we have been evaluating Dask with Python to work with distributed processing, and I don't know if AWS Fargate could be used to build Dask clusters, which would be great because there are many people on our team working with Python workloads. Having something that can manage distributed processing using Dask in a containerized deployment could be valuable. For a company that does not require complexity or managing Kubernetes clusters, AWS Fargate is a great way to go with the use of containers in a simple way, providing the power of containers and scalability without the complexity of going into Kubernetes.

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

Comms Service Provider

Government

16%

Financial Services Firm

14%

Comms Service Provider

12%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	10
Midsize Enterprise	4
Large Enterprise	7

Questions from the Community

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?

Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...

See all answers

What do you like most about AWS Fargate?

The most valuable feature of Fargate is that it's self-managed. You don't have to configure your own clusters or deploy any Kubernetes clusters. This simplifies the initial deployment and scaling p...

See all answers

What needs improvement with AWS Fargate?

They need to improve some UI-based interaction.

See all answers

What advice do you have for others considering AWS Fargate?

Using AWS Fargate is becoming easier as the platform improves. On a scale of 1-10, I rate AWS Fargate a 7.

See all answers

Comparisons

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Compared 16% of the time

AWS Lambda vs Apache Spark

Compared 7% of the time

AWS Batch vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

SAP HANA vs Apache Spark

Compared 5% of the time

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Amazon EC2 Auto Scaling vs AWS Fargate

Compared 24% of the time

Heroku vs AWS Fargate

Compared 17% of the time

Amazon EC2 vs AWS Fargate

Compared 15% of the time

AWS Batch vs AWS Fargate

Compared 11% of the time

Amazon Elastic Inference vs AWS Fargate

Compared 6% of the time

More AWS Fargate Competitors

Product Reports

Buyer's Guide

Apache Spark

January 2026

Download Apache Spark product report

Buyer's Guide

AWS Fargate

January 2026

Download AWS Fargate product report

Overview

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

A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.

Fargate has flexible configuration options so you can closely match your application needs and granular, per-second billing.

Amazon Web Services (AWS)

Sample Customers

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

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

Buyer's Guide

AWS Fargate vs. Apache Spark

December 2025

Free Report: AWS Fargate vs. Apache Spark

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