IBM Streams vs Upsolver comparison

Why PeerSpot?

IBM and Upsolver are both solutions in the Streaming Analytics category. IBM is ranked #23, while Upsolver is ranked #22 with an average rating of 8.7. IBM holds a 2.1% mindshare in SA, compared to Upsolver’s 1.3% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

IBM Streams

Read 5 IBM Streams reviews

1,629 Views
1,303 Comparison Views

100% willing to recommend

Upsolver

Read 4 Upsolver reviews

2,415 Views
628 Comparison Views

100% willing to recommend

IBM Streams

Upsolver

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between IBM Streams and Upsolver based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed IBM Streams vs. Upsolver Report (Updated: September 2026).

Buyer's Guide

IBM Streams vs. Upsolver

September 2026

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Helped 915,341 peers since 2012

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

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

Upsolver

Ranking in Streaming Analytics

22nd

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Integration (39th)

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of IBM Streams is 2.1%, up from 1.1% compared to the previous year. The mindshare of Upsolver is 1.3%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Upsolver	1.3%
IBM Streams	2.1%
Other	96.6%

Streaming Analytics

Featured Reviews

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

reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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

"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."

"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."

"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."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"I have saved 50 to 60% on maintaining pipelines since using Upsolver."

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

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."

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

"The development IDE sometimes crashes and freezes."

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

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

"I would say Upsolver's scalability is eight out of 10 because of pricing."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"There is room for improvement in query tuning."

Pricing and Cost Advice

Information not available

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Government

16%

Construction Company

13%

Financial Services Firm

13%

Comms Service Provider

11%

Real Estate/Law Firm

14%

Manufacturing Company

14%

Retailer

13%

Construction Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

Ask a question

Earn 20 points

What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

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What needs improvement with Upsolver?

I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.

See all answers

What is your primary use case for Upsolver?

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...

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Comparisons

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

StreamAnalytix vs IBM Streams

Compared 11% of the time

Apache NiFi vs IBM Streams

Compared 11% of the time

More IBM Streams Competitors

Databricks vs Upsolver

Compared 10% of the time

Striim vs Upsolver

Compared 10% of the time

Rivery vs Upsolver

Compared 7% of the time

SAS Access vs Upsolver

Compared 6% of the time

Alteryx Designer vs Upsolver

Compared 5% of the time

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Product Reports

Buyer's Guide

Streaming Analytics

September 2026

Download IBM Streams product report

Buyer's Guide

Data Integration

September 2026

Download Upsolver product report

Also Known As

IBM InfoSphere Streams

No data available

Overview

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

Upsolver offers a data lakehouse platform that simplifies big data processing and analytics, enabling data teams to efficiently handle large-scale datasets.

Upsolver's platform focuses on simplifying the complexities of data engineering by transforming raw data into queryable formats quickly. It allows seamless integration with cloud storage and various database systems, providing flexibility for data-driven businesses. The platform automates data preparation tasks, reducing manual coding and allowing teams to focus on extracting insights. Its scalable architecture supports real-time analytics and batch processing.

What are the key features of Upsolver?

Data Ingestion: Easily ingest data from multiple sources with minimal configuration.
Schema On-Read: Automatically interpret data schemas, simplifying data management.
Streaming SQL: Transform and analyze streaming data using a familiar SQL syntax.
Data Lake Integration: Seamlessly connect with data lakes for streamlined workflows.

What benefits and ROI should users consider?

Time Savings: Faster data processing reduces time to insights.
Cost Efficiency: Scalable architecture optimizes infrastructure costs.
Scalability: Handle growing data volumes effortlessly.
Flexibility: Supports wide data storage and processing strategies.

In the e-commerce industry, Upsolver helps businesses optimize their data pipelines for better customer segmentation and personalization, while in finance, it enhances fraud detection capabilities through real-time data analytics.

Upsolver

Sample Customers

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

Information Not Available

Buyer's Guide

IBM Streams vs. Upsolver

September 2026

Free Report: IBM Streams vs. Upsolver

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