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 8.6, while Amazon Web Services (AWS) is ranked #2 with an average rating of 8.5. Apache holds a 11.3% mindshare in CS, compared to Amazon Web Services (AWS)’s 14.4% 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 66 Apache Spark reviews

1,415 Views
1,077 Comparison Views

90% willing to recommend

AWS Fargate

Read 17 AWS Fargate reviews

2,958 Views
1,720 Comparison Views

100% willing to recommend

Apache Spark

AWS Fargate

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between Apache Spark and AWS Fargate based on real PeerSpot user reviews.

Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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

Buyer's Guide

AWS Fargate vs. Apache Spark

April 2025

Download the complete report

Helped 851,604 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

Apache Spark

Ranking in Compute Service

5th

Average Rating

8.4

Reviews Sentiment

7.7

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

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 10.2% compared to the previous year. The mindshare of AWS Fargate is 14.4%, down from 18.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

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.

Read full review

Subrata Mukherjee

Senior Technical Architect; Head of Platform at Blenheim Chalcot IT Services India

Boost demand response with cost-efficient serverless architecture

We are a venture builder company, and if we select AWS for our product. Our design is based on a serverless architecture model. ECS Fargate is the most convenient way in terms of scalability, integration, and cost control Thanks to the serverless model and easy integration features, a few…

Read full review

Quotes from Members

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

Pros

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"The solution has been very stable."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

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

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

"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."

More Apache Spark pros

"By using a server's compute resources, one can observe the resource metrics. With AWS, one can determine when servers will be used based on CloudWatch results. For example, CloudWatch informs the application and service platform when the hit ratio has reached the threshold value."

"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it."

"AWS Fargate is an easy-to-use tool to simplify setup. You only pay for the resources you use. If you need to quickly create, delete, or scale applications without managing resources like EC2 instances, Fargate is the best service to use."

"The most valuable feature of AWS Fargate is its ease of use."

"AWS Fargate has many valuable services. It does the job with minimal trouble. It's very observable. You can see what's going on and you have logs. You have everything. You can troubleshoot it. It's affordable and it's flexible."

"Fargate itself is a stable product. We are quite satisfied with its performance."

"Fargate's integration with other AWS services is really good."

"It allows for focusing on applications instead of managing infrastructure."

More AWS Fargate pros

Cons

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

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

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

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"It should support more programming languages."

"The logging for the observability platform could be better."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

More Apache Spark cons

"I heard from my team that it's not easy to predict the cost. That is the only issue we have with AWS Fargate, but I think that's acceptable. AWS Fargate isn't user-friendly. Anything related to Software as a Service or microservice architecture is not easy to implement. You're required to have DevOps from your side to implement the solution. AWS Fargate is just a temporary solution for us. When we grow to a certain level, we may use AKS for better control."

"There are features that could be added or enhanced."

"Sometimes, Fargate can be really hard to configure."

"If there are any options to manage containers, that would be good. That relates more to the cost point. For example, over the next three months, I'll be making a comparison between solutions like CAST AI and other software-as-a-service platforms that offer Kubernetes management with an emphasis on cost reduction."

"As I have not worked with Fargate extensively, I do not have any specific improvements."

"AWS Fargate needs improvement in terms of setup complexity."

"I would like to see the older dashboard instead of the newer version. I don't like the new dashboard."

"AWS Fargate could improve the privileged mode containers. We had some problems and they were not able to run."

More AWS Fargate cons

Pricing and Cost Advice

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

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"They provide an open-source license for the on-premise version."

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

"Spark is an open-source solution, so there are no licensing costs."

"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."

"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."

"Apache Spark is an expensive solution."

More Apache Spark pricing and cost advice

"I rate the price of AWS Fargate a four out of five."

"We would advise that this solution has a slightly-higher price point than others on the market. There is a free plan available for start-ups, but the free and lower range licensing models do not provide the full functionality."

See which vendors are best for you

Use our free recommendation engine to learn which Compute Service solutions are best for your needs.

See recommendations

851,604 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

22%

Computer Software Company

13%

Comms Service Provider

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

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

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?

The monitoring capabilities of AWS Fargate could be improved and made more robust. The error handling aspect sometimes causes issues and can get stuck during deployment, making the process not very...

See all answers

What advice do you have for others considering AWS Fargate?

I would recommend AWS Fargate as an alternative to AWS Lambda for running loads or hosting a service. It is a good service for keeping instances running, which minimizes initial latency. Overall, I...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 11% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Amazon Corretto vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Amazon EC2 Auto Scaling vs AWS Fargate

Compared 41% of the time

Heroku vs AWS Fargate

Compared 17% of the time

Oracle Compute Cloud Service vs AWS Fargate

Compared 9% of the time

Amazon EC2 vs AWS Fargate

Compared 9% of the time

AWS Lambda vs AWS Fargate

Compared 4% of the time

More AWS Fargate Competitors

Product Reports

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Buyer's Guide

AWS Fargate

May 2025

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

April 2025

Free Report: AWS Fargate vs. Apache Spark

Find out what your peers are saying about AWS Fargate vs. Apache Spark and other solutions. Updated: April 2025.

DOWNLOAD NOW

851,604 professionals have used our research since 2012.

See our AWS Fargate vs. Apache Spark report.

See our list of best Compute Service vendors.

We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.