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Apache Spark vs Spot by Flexera comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 19, 2026

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
5.6
Apache Spark provides up to 50% cost savings, boosting efficiency and reducing expenses significantly in machine learning analytics.
Sentiment score
7.6
Spot by Flexera enabled substantial savings in compute costs and enhanced efficiency, though ROI may take longer for some.
I have seen a return on investment as I was able to reduce my compute cost by 60 to 65% for some of my applications that needed light temporary processing tasks with ephemeral storage and stateless applications.
Dev Ops Engineer at a construction company with 11-50 employees
Our technology predicts demand, selects the cheapest instance mix, sizes the workloads appropriately, and scales it automatically based on policy, resulting in significant savings by reducing manual work and enhancing our flexibility, which has helped accelerate our deployments.
Global Hyperscaler Alliances Director at a tech vendor with 1,001-5,000 employees
 

Customer Service

Sentiment score
6.0
Apache Spark offers vibrant community support and resources, with commercial support available through vendors like Cloudera and Hadoop.
Sentiment score
8.4
Spot by Flexera's customer service is highly rated for its excellent technical support, responsiveness, and reliability, ensuring customer satisfaction.
I would rate the technical support of Apache Spark an eight because when we had questions, we found solutions, and it was straightforward.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG
I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.
Data Architect at Devtech
I would rate the customer support a nine out of ten.
Global Hyperscaler Alliances Director at a tech vendor with 1,001-5,000 employees
 

Scalability Issues

Sentiment score
7.4
Apache Spark's scalability and versatility enable efficient large-scale data processing, making it a reliable choice for diverse teams.
Sentiment score
7.4
Spot by Flexera is highly scalable, effectively supporting multiple users across environments, particularly with AWS services, boosting user confidence.
Spot's scalability is quite good, and it can be expanded to multiple environments.
Dev Ops Engineer at a construction company with 11-50 employees
 

Stability Issues

Sentiment score
7.4
Apache Spark is praised for its robust stability and reliability, with high user ratings despite minor configuration challenges.
Sentiment score
7.0
Spot by Flexera is mostly stable and reliable, though some users report occasional bugs indicating room for improvement.
MapReduce needs to perform numerous disk input and output operations, while Apache Spark can use memory to store and process data.
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.
Data Architect at Devtech
 

Room For Improvement

Apache Spark needs improvements in real-time querying, user-friendliness, logging, large dataset handling, and expanded programming language support.
Spot by Flexera needs OCI support, better multi-cloud integration, and UI improvements, though it's stable and mature.
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Data Architect at Devtech
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG
Continuously making advancements based on customer feedback.
Global Hyperscaler Alliances Director at a tech vendor with 1,001-5,000 employees
More guided onboarding would help teams adopt advanced features faster.
Dev Ops Engineer at a construction company with 11-50 employees
Spot could be improved by adding features that help identify data to optimize storage costs by detecting datasets within our environment based on criteria such as age and usage.
AVP DevOps and Product Support at a recruiting/HR firm with 1,001-5,000 employees
 

Setup Cost

Apache Spark is cost-effective but can incur high infrastructure costs, especially in cloud setups like Databricks, with setup time variability.
Enterprise users praise Spot by Flexera's pricing and setup, with structured management of licensing, via AWS Marketplace.
The pricing is reasonable and convenient, and the value it offers is completely in line with what I am spending.
Dev Ops Engineer at a construction company with 11-50 employees
 

Valuable Features

Apache Spark provides scalable, in-memory data processing with flexible support for distributed computing, streaming, and machine learning integration.
Spot by Flexera offers cost optimization, Kubernetes management, and security compliance with intelligent autoscaling, AWS integration, and AI-driven insights.
The most important part is that everything can be connected, and the data exchange across overseas connections is fast and reliable.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG
Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code.
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.
Data Architect at Devtech
The fact that it can mix dozens of instance families dynamically is something which the AWS native autoscalers simply cannot do.
Dev Ops Engineer at a construction company with 11-50 employees
Spot's automated cost optimization works by continuously analyzing the workload and applying policies to minimize cloud spend without sacrificing performance.
Global Hyperscaler Alliances Director at a tech vendor with 1,001-5,000 employees
One use case we plan to implement within the next few months is the overall commitment savings, which we used to manage manually, but with Spot, it automatically helps us detect when and what kind of instances to purchase, making it easier for us to manage our overall commitment for the savings plan.
AVP DevOps and Product Support at a recruiting/HR firm with 1,001-5,000 employees
 

Categories and Ranking

Apache Spark
Ranking in Compute Service
5th
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
69
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
Spot by Flexera
Ranking in Compute Service
9th
Average Rating
8.6
Reviews Sentiment
7.5
Number of Reviews
5
Ranking in other categories
Cloud Management (20th), Server Virtualization Software (14th), Cloud Operations Analytics (1st), Cloud Analytics (2nd), Containers as a Service (CaaS) (3rd), Cloud Cost Management (5th)
 

Mindshare comparison

As of March 2026, in the Compute Service category, the mindshare of Apache Spark is 10.1%, down from 11.3% compared to the previous year. The mindshare of Spot by Flexera is 4.3%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service Mindshare Distribution
ProductMindshare (%)
Apache Spark10.1%
Spot by Flexera4.3%
Other85.6%
Compute Service
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
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.
KS
Dev Ops Engineer at a construction company with 11-50 employees
Intelligent autoscaling has cut compute costs and now manages ephemeral workloads efficiently
I think overall Spot is quite good. The UI is powerful, but it can feel a bit dense for some new users. More guided onboarding would help teams adopt advanced features faster, and deeper insights into Kubernetes resource usage would be beneficial. Overall, it is a really good platform that is quite mature and stable. I chose a rating of nine because it may sometimes be a bit overwhelming for newcomers, and there are also a few areas in which the EKS Kubernetes level granularity is a little missing. Overall, I think Spot is a really good and stable tool.
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Top Industries

By visitors reading reviews
Financial Services Firm
23%
Computer Software Company
8%
Manufacturing Company
7%
Comms Service Provider
5%
Manufacturing Company
17%
Computer Software Company
12%
Healthcare Company
8%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise32
No data available
 

Questions from the Community

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?
Apache Spark is open-source, so it doesn't incur any charges.
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...
What do you like most about Spot Ocean?
The solution helps us to manage and scale automatically whenever there is a limit to the increase in the application workflow.
What needs improvement with Spot Ocean?
I think overall Spot is quite good. The UI is powerful, but it can feel a bit dense for some new users. More guided onboarding would help teams adopt advanced features faster, and deeper insights i...
What is your primary use case for Spot Ocean?
My primary use case for Spot revolves around managing and optimizing my compute infrastructure across AWS, mainly EC2 and EKS on a daily basis, such as monitoring the workloads and ensuring that Ku...
 

Comparisons

 

Also Known As

No data available
Spot Ocean, Spot Elastigroup, Spot Eco
 

Overview

 

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
Freshworks, Zalando, Red Spark, News, Trax, ETAS, Demandbase, BeesWa, Duolingo, intel, IBM, N26, Wix, EyeEm, moovit, SAMSUNG, News UK, ticketmaster
Find out what your peers are saying about Apache Spark vs. Spot by Flexera and other solutions. Updated: March 2026.
884,012 professionals have used our research since 2012.