Amazon Kinesis vs IBM Streams comparison

Why PeerSpot?

Amazon Web Services (AWS) and IBM are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #12 with an average rating of 8.3, while IBM is ranked #23. Amazon Web Services (AWS) holds a 3.9% mindshare in SA, compared to IBM’s 2.1% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Amazon Kinesis

Read 29 Amazon Kinesis reviews

3,471 Views
3,471 Comparison Views

90% willing to recommend

IBM Streams

Read 5 IBM Streams reviews

1,487 Views
1,219 Comparison Views

100% willing to recommend

Amazon Kinesis

IBM Streams

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

IBM Streams and Amazon Kinesis compete in the real-time data stream processing category. While both have unique strengths, Amazon Kinesis offers an edge in scalability and AWS ecosystem integration.

Features: IBM Streams offers complex event processing, deep integration within IBM's ecosystem, and supports comprehensive real-time analytics, ideal for organizations invested in IBM technology. Amazon Kinesis provides easy scalability, native integration with AWS services, and robust cloud processing capabilities suitable for various data applications.

Room for Improvement: IBM Streams could improve by simplifying its deployment process and lowering its reliance on IBM-specific expertise. Amazon Kinesis may benefit from enhancements in cost management, fine-tuning data processing times, and improving scalability beyond AWS boundaries for more versatile data workload handling.

Ease of Deployment and Customer Service: IBM Streams requires expertise in IBM configurations, adding complexity for new users, though it supports extensive use cases. Amazon Kinesis offers straightforward cloud deployments with the support of AWS's technical network, attracting organizations seeking rapid and scalable implementation.

Pricing and ROI: IBM Streams tends to involve higher initial setup costs due to its specialized infrastructure and IBM ecosystem integration. Over time, its ROI can be substantial for IBM-reliant businesses. In contrast, Amazon Kinesis benefits from a pay-as-you-go pricing model, providing immediate cost savings and scalable ROI for growing cloud-based projects.

To learn more, read our detailed Amazon Kinesis vs. IBM Streams Report (Updated: August 2026).

Buyer's Guide

Amazon Kinesis vs. IBM Streams

August 2026

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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

Amazon Kinesis

Ranking in Streaming Analytics

12th

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

IBM Streams

Ranking in Streaming Analytics

23rd

Average Rating

8.2

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 3.9%, down from 7.0% compared to the previous year. The mindshare of IBM Streams is 2.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Amazon Kinesis	3.9%
IBM Streams	2.1%
Other	94.0%

Streaming Analytics

Featured Reviews

reviewer1480695

Director of Software Development at a tech vendor with 10,001+ employees

Has enabled real-time processing of critical event streams with seamless cloud integration

We are contemplating moving away from Amazon Kinesis primarily because of the cost. It is very useful, but if we write our own analytics and data processing pipeline, it would be much cheaper for us. The cost is a primary hindrance. That's why we are not using it widely. For our critical pipeline we are using it, but after that we are putting it in an S3 bucket. Other pipelines directly put the events in an S3 bucket and then process from there. There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete. The cost aspect is what we are really concerned about.

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Ahmed_Emad

Territory Sales Leader at Sumerge

A solution for data pipelines but has connector limitations

We have used Kafka for seven years. IBM streams gives you many OOTB features that can boost the time-to-market, especially when it comes to reporting and monitoring for example. Confluent is recognized as one of the leaders in this space and the main reason for this is related to the complete vision of the platform also the large number of connectors. This gives the edge and competitive advatnage.

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

"We have been able to drop our costs for ingesting data by about 60 to 70%."

"We have seen a return on our investment with Amazon Kinesis, as we are able to process data without any issue and it is our solution for ingesting data in other databases, such as Snowflake."

"Amazon Kinesis also provides us with plenty of flexibility."

"I find data Firehose and data streams are much more intelligent than other streaming solutions."

"Kinesis replaced a whole tier of servers, so we didn't need to have a server to catch the data and then send the data somewhere else, because Kinesis was the input port for very large amounts of data."

"The management and analytics are valuable features."

"Kinesis has the best of Amazon: data streaming, building processes, data analytics, data in real-time are very good."

"With AWS, you don't have to invest in any kind of infrastructure."

More Amazon Kinesis pros

"The OEM Solution (Excel-medical.com) running on top of IBM Streams provides real-time clinical algorithms that can give better insight into the patient's acuity, thus cutting off time to discharge patients and inversely making sure that sick patients don't get discharged until ready."

"As a result, the TELCO company was able to cut down the time it took to respond to customer needs and there were fewer complaints."

"Easy development and deployment, Java implementation features, and the real time analyser and alarm function are the most valuable features for us."

"The product has enabled us to create solutions to client problems that would have either been impossible or very expensive/difficult using other technologies."

Cons

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

"There could be valid data in Kinesis that is not being processed, which affects stability. Although it rarely happens, this issue has been observed in many deployments, making it not completely stable."

"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."

"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."

"Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes."

"The default limit that they have, which at the moment is 5,000 records per second seems too low."

"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."

"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."

More Amazon Kinesis cons

"I’d like to see a tool kit specifically targeted at incremental machine learning. It’s already great for scoring previously trained models, but dynamically updating models is currently more of a 'grow your own' kind of thing."

"The price and versatility of this product need to improve - it is not inexpensive."

"We had some stability issues where we used embedded Zookeeper in production."

"The development IDE sometimes crashes and freezes."

Pricing and Cost Advice

"The solution's pricing is fair."

"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."

"I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool."

"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

"The fee is based on the number of hours the service is running."

"The tool's entry price is cheap. However, pricing increases with data volume."

"It was actually a fairly high volume we were spending. We were spending about 150 a month."

More Amazon Kinesis pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

10%

Manufacturing Company

Outsourcing Company

Government

18%

Financial Services Firm

14%

Construction Company

14%

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	10
Large Enterprise	10

No data available

Questions from the Community

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.

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What needs improvement with Amazon Kinesis?

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What is your primary use case for Amazon Kinesis?

We use Amazon Kinesis for stream processing. We get events from on-premise devices to the cloud. We get many device events and we have to process these events that are coming from the devices. To p...

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Comparisons

Azure Stream Analytics vs Amazon Kinesis

Compared 16% of the time

Confluent vs Amazon Kinesis

Compared 15% of the time

Apache Flink vs Amazon Kinesis

Compared 13% of the time

Amazon MSK vs Amazon Kinesis

Compared 9% of the time

Redpanda vs Amazon Kinesis

Compared 9% of the time

More Amazon Kinesis Competitors

Azure Stream Analytics vs IBM Streams

Compared 24% of the time

Confluent vs IBM Streams

Compared 21% of the time

Apache Flink vs IBM Streams

Compared 13% of the time

StreamAnalytix vs IBM Streams

Compared 11% of the time

Apache NiFi vs IBM Streams

Compared 11% of the time

More IBM Streams Competitors

Product Reports

Buyer's Guide

Amazon Kinesis

August 2026

Download Amazon Kinesis product report

Buyer's Guide

Streaming Analytics

August 2026

Download IBM Streams product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

IBM InfoSphere Streams

Overview

Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.

Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.

What are the key features of Amazon Kinesis?

Real-time Data Streaming: Enables live data processing and analytics.
Seamless AWS Integration: Ensures smooth operation with AWS services.
High Throughput: Consistent 2Mbps rate supports large volumes of data.
Auto-scaling: Automatically adjusts to data load.
Fault Tolerance: Maintains system reliability and data integrity.
Lambda Function Processing: Processes data efficiently using Lambda.

What benefits or ROI should users expect?

Scalable Data Management: Handles data influx effortlessly.
Efficiency: Streamlines data flows without impacting performance.
Cost-effectiveness: Delivers value for budget-conscious users.
Low Latency: Provides real-time insights and reactions.
Multi-language Support: Accommodates various development needs.

In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.

Amazon Web Services (AWS)

IBM Streams is a real-time analytics platform providing enhanced data processing capabilities for large-scale data sets, enabling enterprises to swiftly analyze and act on data-in-motion.

IBM Streams offers a robust infrastructure for processing high-velocity data, enabling the analysis and monitoring of streaming data in real time. It supports the development of applications that handle massive volumes of data with low latency. It seamlessly integrates into existing ecosystems, ensuring real-time insights are accessible across various channels. IBM Streams is especially suited for industries requiring dynamic data management capabilities.

What are the key features of IBM Streams?

Real-Time Analytics: Provides immediate data processing to offer live insights.
Scalability: Designed for handling growing data needs efficiently.
Integration: Works with other enterprise systems for data harmony.
Development Flexibility: Offers support for multiple programming languages.
Visualization Tools: Uses sophisticated tools to visualize data insights.

What benefits and ROI should users review?

Improved Decision Making: Enables quick, informed decisions based on real-time data.
Cost Efficiency: Reduces infrastructure costs by optimizing data processing.
Enhanced Data Utilization: Leverages data streams for more comprehensive insights.
Business Agility: Supports responsive business strategies through live data analysis.

In finance, IBM Streams is used for monitoring trading activities and fraud detection, ensuring compliance and reducing risk. In healthcare, it analyzes patient data streams for immediate decision-making. Retailers utilize it for inventory management and customer behavior analytics, aligning offers in real-time with customer interests.

IBM

Sample Customers

Zillow, Netflix, Sonos

Globo TV, All England Lawn Tennis Club, CenterPoint Energy, Consolidated Communications Holdings, Darwin Ecosystem, Emory University Hospital, ICICI Securities, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Living Roads, Mobileum, Optibus, Southern Ontario Smart Computing Innovation Platform (SOSCIP), University of Alberta, University of Montana, University of Ontario Institute of Technology, Wimbledon 2015

Buyer's Guide

Amazon Kinesis vs. IBM Streams

August 2026

Free Report: Amazon Kinesis vs. IBM Streams

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