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Confluent vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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

Confluent
Ranking in Streaming Analytics
5th
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
25
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
13th
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Streaming Analytics category, the mindshare of Confluent is 6.9%, down from 8.6% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.9%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Confluent6.9%
Google Cloud Dataflow3.9%
Other89.2%
Streaming Analytics
 

Featured Reviews

PavanManepalli - PeerSpot reviewer
AVP - Sr Middleware Messaging Integration Engineer at Wells Fargo
Has supported streaming use cases across data centers and simplifies fraud analytics with SQL-based processing
I recommend that Confluent should improve its solution to keep up with competitors in the market, such as Solace and other upcoming tools such as NATS. Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools. They need to improve in that direction by not only reducing costs but also providing better solutions for the problems customers face to avoid frustrations, whether through future enhancement requests or ensuring product stability. The cost should be worked on, and they should provide better solutions for customers. Solutions should focus on hierarchical topics; if a customer has different types of data and sources, they should be able to send them to the same place for analytics. Currently, Confluent requires everything to send to the same topic, which becomes very large and makes running analytics difficult. The hierarchy of topics should be improved. This part is available in MQ and other products such as Solace, but it is missing in Confluent, leading many in capital markets and trading to switch to Solace. In terms of stability, it is not the stability itself that needs improvement but rather the delivery semantics. Other products offer exactly-once delivery out of the box, whereas Confluent states it will offer this but lacks the knobs or levers for tuning configurations effectively. Confluent has hundreds of configurations that application teams must understand, which creates a gap. Users are often unaware of what values to set for better performance or to achieve exactly-once semantics, making it difficult to navigate through them. Delivery semantics also need to be worked on.
PR
Senior Data Engineer at Accruent
Enables real-time insights and efficient data preparation for machine learning
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts. For integration, we use Dataflow to extract data from different sources like APIs and flat files. We then perform data cleansing, including deduplications, schema standardizations, and filtering of invalid records. We also use it for preparing data for machine learning models, transforming data, and accelerating models.

Quotes from Members

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

Pros

"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."
"We ensure seamless management of Kafka through Confluent, allowing all of our Kafka activities to be handled by a third party."
"Their tech support is amazing; they are very good, both on and off-site."
"Confluent facilitates the messaging tasks with Kafka, streamlining our processes effectively."
"Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance."
"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."
"Some of the best features are that it's very quick to set up, very easy to have a centralized area that gives us a history of changes, and the ability to give feedback on any information placed onto the pages."
"It is also good for knowledge base management."
"The solution allows us to program in any language we desire."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"The integration within Google Cloud Platform is very good."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The service is relatively cheap compared to other batch-processing engines."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"It is a scalable solution."
 

Cons

"It would help if the knowledge based documents in the support portal could be available for public use as well."
"Areas for improvement include implementing multi-storage support to differentiate between database stores based on data age and optimizing storage costs."
"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."
"Confluent has a good monitoring tool, but it's not customizable."
"Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools."
"Confluent's price needs improvement."
"They should remove Zookeeper because of security issues."
"It requires some application specific connectors which are lacking. This needs to be added."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."
"The deployment time could also be reduced."
"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"They should do a market survey and then make improvements."
"Google Cloud Dataflow should include a little cost optimization."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
 

Pricing and Cost Advice

"Confluent is highly priced."
"It comes with a high cost."
"Confluent is an expensive solution."
"The solution is cheaper than other products."
"Confluent has a yearly license, which is a bit high because it's on a per-user basis."
"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."
"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."
"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."
"The solution is not very expensive."
"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."
"The tool is cheap."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."
"Google Cloud Dataflow is a cheap solution."
"The solution is cost-effective."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."
"Google Cloud is slightly cheaper than AWS."
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
11%
Retailer
11%
Manufacturing Company
5%
Financial Services Firm
17%
Manufacturing Company
13%
Retailer
11%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise4
Large Enterprise16
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise10
 

Questions from the Community

What do you like most about Confluent?
I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and to...
What is your experience regarding pricing and costs for Confluent?
They charge a lot for scaling, which makes it expensive.
What needs improvement with Confluent?
I recommend that Confluent should improve its solution to keep up with competitors in the market, such as Solace and other upcoming tools such as NATS. Recently, there has been a lot of buzz about ...
What is your experience regarding pricing and costs for Google Cloud Dataflow?
Pricing is normal. It is part of a package received from Google, and they are not charging us too high.
What needs improvement with Google Cloud Dataflow?
It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...
What is your primary use case for Google Cloud Dataflow?
It is used for exporting data, such as customer clicks, customer interactions with emails, and link tracking. The Google Analytics streaming data is used to establish customer behavioral patterns.
 

Also Known As

No data available
Google Dataflow
 

Overview

 

Sample Customers

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Confluent vs. Google Cloud Dataflow and other solutions. Updated: March 2026.
884,797 professionals have used our research since 2012.