Try our new research platform with insights from 80,000+ expert users

Amazon Kinesis vs Apache Kafka 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:
 

ROI

Sentiment score
7.8
Amazon Kinesis offers significant cost savings, seamless integration, improved monitoring, and reduces data ingestion costs, enhancing ROI and architecture.
Sentiment score
7.0
Apache Kafka offers substantial returns, especially in high-value applications, with enhanced data buffering, cost savings, and ease of use.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
 

Customer Service

Sentiment score
7.1
Amazon Kinesis customer support is generally quick and effective, but experiences vary in technical guidance and response times.
Sentiment score
5.8
Apache Kafka's support is community-driven, with varying user experiences and enhanced options available through paid subscriptions and consultants.
We receive prompt support from AWS solution architects or TAMs.
There is plenty of community support available online.
The Apache community provides support for the open-source version.
 

Scalability Issues

Sentiment score
7.3
Amazon Kinesis is scalable for reliable streaming, but complex processing and costs may vary with implementation and data volumes.
Sentiment score
7.8
Apache Kafka is praised for its robust scalability, efficiently handling high data throughput, with some challenges in cluster management.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
Customers have not faced issues with user growth or data streaming needs.
 

Stability Issues

Sentiment score
7.8
Amazon Kinesis is praised for stability and fault tolerance, though some users report slowdowns and capacity issues.
Sentiment score
7.7
Apache Kafka is stable and performs well with high data volumes, though some configurations may affect its reliability.
Apache Kafka is stable.
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
 

Room For Improvement

Amazon Kinesis requires improvements in throughput, automation, setup complexity, data retention, machine learning features, and user-friendly interfaces.
Enhancing Kafka involves user-friendly UI, improved monitoring, reduced ZooKeeper dependency, better documentation, flexibility, and integration with other platforms.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
We are always trying to find the best configs, which is a challenge.
I would appreciate having some kind of UI integrated into Apache Kafka for connecting to it because using code to connect it is basic, but we can use a UI.
 

Setup Cost

Amazon Kinesis is cost-effective compared to self-managed solutions, but prices can increase with high data usage and features.
Apache Kafka is free to use, but costs vary for managed services and enterprise solutions, potentially exceeding 100,000 euros annually.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
 

Valuable Features

Amazon Kinesis offers easy configuration, real-time analytics, and robust AWS integration, ideal for managing large, complex data workflows.
Apache Kafka excels in scalability, real-time streaming, and flexibility, ideal for large data volumes and event-driven architectures.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
The impact of Apache Kafka's scalability features on my organization and data processing capabilities depends on how many messages each company wants to receive.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
28
Ranking in other categories
No ranking in other categories
Apache Kafka
Ranking in Streaming Analytics
8th
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
88
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 7.9%, down from 13.0% compared to the previous year. The mindshare of Apache Kafka is 3.0%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Prabin Silwal - PeerSpot reviewer
Pipeline setup is very simple
I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs. Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.
Snehasish Das - PeerSpot reviewer
Data streaming transforms real-time data movement with impressive scalability
I worked with Apache Kafka for customers in the financial industry and OTT platforms. They use Kafka particularly for data streaming. Companies offering movie and entertainment as a service, similar to Netflix, use Kafka Apache Kafka offers unique data streaming. It allows the use of data in…
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
859,129 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
19%
Financial Services Firm
17%
Manufacturing Company
10%
Comms Service Provider
4%
Financial Services Firm
29%
Computer Software Company
12%
Manufacturing Company
7%
Retailer
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
What needs improvement with Amazon Kinesis?
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...
What are the differences between Apache Kafka and IBM MQ?
Apache Kafka is open source and can be used for free. It has very good log management and has a way to store the data used for analytics. Apache Kafka is very good if you have a high number of user...
What do you like most about Apache Kafka?
Apache Kafka is an open-source solution that can be used for messaging or event processing.
What is your experience regarding pricing and costs for Apache Kafka?
Its pricing is reasonable. It's not always about cost, but about meeting specific needs.
 

Comparisons

 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
No data available
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Uber, Netflix, Activision, Spotify, Slack, Pinterest
Find out what your peers are saying about Amazon Kinesis vs. Apache Kafka and other solutions. Updated: June 2025.
859,129 professionals have used our research since 2012.