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: September 2026).

Buyer's Guide

Amazon Kinesis vs. IBM Streams

September 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 October 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

"The scalability is pretty good."

"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."

"The most valuable feature of Amazon Kinesis is real-time data streaming."

"Compared to what we were doing with Kafka, which was taking about 30% just to keep things together, with Kinesis I think we're probably saving tens of thousands, if not $100,000 per year."

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

"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."

"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."

"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."

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

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

"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."

Cons

"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."

"If there were better documentation on optimal sharding strategies then it would be helpful."

"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."

"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."

"When we had some of those slow downs, we used AWS support and I can't say that we had a great experience and they resolved the issues, but they looked into some of the flow downs and ultimately we just decided there was nothing we could do."

"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."

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

"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."

More Amazon Kinesis cons

"The development IDE sometimes crashes and freezes."

"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."

"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."

Pricing and Cost Advice

"The product falls on a bit of an expensive side."

"Under $1,000 per month."

"The tool's pricing is cheap."

"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."

"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."

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

"Amazon Kinesis is an expensive solution."

More Amazon Kinesis pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

Manufacturing Company

Construction Company

Government

17%

Construction Company

13%

Financial Services Firm

13%

Comms Service Provider

10%

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

Confluent vs Amazon Kinesis

Compared 15% of the time

Azure Stream Analytics vs Amazon Kinesis

Compared 15% of the time

Apache Flink vs Amazon Kinesis

Compared 12% of the time

Redpanda vs Amazon Kinesis

Compared 9% of the time

Amazon MSK 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 19% of the time

Apache Flink vs IBM Streams

Compared 12% of the time

Apache NiFi vs IBM Streams

Compared 11% of the time

StreamAnalytix vs IBM Streams

Compared 11% of the time

More IBM Streams Competitors

Product Reports

Buyer's Guide

Amazon Kinesis

September 2026

Download Amazon Kinesis product report

Buyer's Guide

Streaming Analytics

September 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

September 2026

Free Report: Amazon Kinesis vs. IBM Streams

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