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Confluent vs Google Cloud Dataflow comparison

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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
9th
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
12th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
15
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Confluent is 6.2%, down from 8.5% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.5%, down from 5.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Confluent6.2%
Google Cloud Dataflow3.5%
Other90.3%
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.
Mohammed Aaqibuddin - PeerSpot reviewer
Senior Cdp Specialist at DigitasLBi
Unified user personas have improved data workflows and support detailed monitoring and logging
Google Cloud has many streams and products. In Google Cloud, everything is translated in the backend, so we do not have to use services such as Apache Beam. When you want to use Google Cloud Functions, you write the code, and the backend talks to all the libraries or Apache, so we do not need to be concerned about those. We just need to use our functions that translate and have many tools and services readily available. Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment. For example, if I am using Google Cloud Functions, I can easily see what changes I have done and trace it properly. I can see what is happening with this script, how many users are affected, whether the script is working, what is failing, and how we can rectify issues with proper monitoring.

Quotes from Members

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

Pros

"We primarily use Confluent for service desk and task management, and it is also good for knowledge base management."
"To date, we have seen improvements in performance and scalability, so we recommend this solution."
"The client APIs are the most valuable feature."
"The monitoring module is impressive."
"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."
"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."
"The documentation process is fast with the tool."
"We mostly use the solution's message queues and event-driven architecture."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."
"The support team is good and it's easy to use."
"It is a scalable solution."
"I would rate the overall solution a ten out of ten."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"The solution allows us to program in any language we desire."
"Google's support team is good at resolving issues, especially with large data."
 

Cons

"I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team."
"It requires some application specific connectors which are lacking. This needs to be added."
"In Confluent, there could be a few more VPN options."
"Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools."
"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."
"Confluence could improve the server version of the solution. However, most companies are going to the cloud."
"There is no local support team in Saudi Arabia."
"It could be improved by including a feature that automatically creates a new topic and puts failed messages."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
"The deployment time could also be reduced."
"The technical support is very hard to reach."
"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."
"Google Cloud Dataflow should include a little cost optimization."
"Currently, not all error logs are available to users and this could make debugging failed jobs very difficult."
"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."
"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."
 

Pricing and Cost Advice

"Confluent is an expensive solution."
"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."
"Confluent is highly priced."
"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."
"It comes with a high cost."
"Confluent has a yearly license, which is a bit high because it's on a per-user basis."
"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."
"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."
"The solution is cost-effective."
"The solution is not very expensive."
"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."
"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."
"Google Cloud is slightly cheaper than AWS."
"Google Cloud Dataflow is a cheap solution."
"The tool is cheap."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Retailer
12%
Manufacturing Company
7%
Construction Company
6%
Financial Services Firm
18%
Manufacturing Company
9%
Comms Service Provider
7%
Construction Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise4
Large Enterprise17
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise12
 

Questions from the Community

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 primary use case for Confluent?
The main use cases for Confluent are log aggregation and streaming. I'm familiar with Confluent stream processing with KSQL. KSQL helps in terms of data analytics strategies because if we are the d...
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?
I feel there could be something that they can introduce, such as when we have data in the tables, a feature that creates a unique persona of the user automatically, so we do not have to do that man...
What is your primary use case for Google Cloud Dataflow?
The primary use case for Google Cloud Dataflow is when a brand has a lot of data and wants to store it in their warehouse. They can use BigQuery to store their data or use big data solutions to sto...
 

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: September 2026.
917,403 professionals have used our research since 2012.