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Hevo vs Upsolver comparison

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Comparison Buyer's Guide

Executive Summary

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

Hevo
Ranking in Data Integration
47th
Average Rating
8.0
Reviews Sentiment
4.4
Number of Reviews
3
Ranking in other categories
No ranking in other categories
Upsolver
Ranking in Data Integration
41st
Average Rating
8.6
Reviews Sentiment
7.6
Number of Reviews
4
Ranking in other categories
Streaming Analytics (22nd)
 

Mindshare comparison

As of November 2026, in the Data Integration category, the mindshare of Hevo is 0.5%, up from 0.4% compared to the previous year. The mindshare of Upsolver is 0.7%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Mindshare Distribution
ProductMindshare (%)
Upsolver0.7%
Hevo0.5%
Other98.8%
Data Integration
 

Featured Reviews

JF
Senior Database Administrator at a healthcare company with 10,001+ employees
Data pipelines have saved time and improve accuracy for daily operations and projects
I believe Hevo can be improved by preparing some guidelines or a manual that any database engineer or related professional can utilize for day-to-day activity. If the support is connected or easily accessible, that could be an improvement. In addition to the points I mentioned, I think AI compatibility is crucial, as everything is becoming more advanced these days, so if that feature has been advanced or upgraded, it would be beneficial. I find the accuracy of Hevo's output to be better, though it can still be improved. I don't have any other improvements needed for Hevo beyond the documentation, support access, and advanced AI capabilities I've mentioned.
reviewer2784462 - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"Hevo positively impacted my organization by allowing us to move data quickly and easily, enabling us to focus on our real challenge: performing analytics and reporting out of Snowflake with all customer data centralized despite using various applications."
"Hevo has positively impacted my organization by ensuring excellent usage of collected data, which is utilized in various workflows, including sales and marketing and customer relationship management, ultimately helping me serve clients better."
"My advice for anyone in database roles, such as administrators, data engineers, or analysts, is to start using Hevo to save time."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."
"I have saved 50 to 60% on maintaining pipelines since using Upsolver."
"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 find the accuracy of Hevo's output to be better, though it can still be improved."
"There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-established."
"Hevo could be improved as we hit limits, particularly when it couldn't keep up with the high number of databases we wanted to sync frequently—ideally updating everything hourly for near real-time analytics rather than a day behind."
"There is room for improvement in query tuning."
"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."
"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."
"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."
 

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
Comms Service Provider
15%
Construction Company
11%
Security Firm
11%
Retailer
9%
Real Estate/Law Firm
13%
Manufacturing Company
13%
Retailer
12%
Comms Service Provider
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Questions from the Community

What needs improvement with Hevo?
There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-esta...
What is your primary use case for Hevo?
Hevo is mainly used for data integration purposes, and I use it to form a pipeline or a gateway where data is collected from various data collection platforms or sources. Hevo is a data integration...
What advice do you have for others considering Hevo?
I would rate this product highly based on my experience with it.
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.
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.
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,...
 

Comparisons

 

Overview

 

Sample Customers

Deliverr, Meesho, Byju's, Envato, NZXT, Kazoo, Webedia, Airwallex, Cuvva, Opploans
Information Not Available
Find out what your peers are saying about Hevo vs. Upsolver and other solutions. Updated: November 2026.
921,523 professionals have used our research since 2012.