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Snowflake vs watsonx.ai comparison

 

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

Snowflake
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
106
Ranking in other categories
Data Warehouse (1st), Cloud Data Warehouse (1st), AI Synthetic Data (1st), Database Management Systems (DBMS) (7th), AI Software Development (9th)
watsonx.ai
Average Rating
7.0
Reviews Sentiment
5.1
Number of Reviews
1
Ranking in other categories
AI Development Platforms (27th)
 

Mindshare comparison

While both are Artificial Intelligence (AI) solutions, they serve different purposes. Snowflake is designed for Cloud Data Warehouse and holds a mindshare of 15.1%, down 18.7% compared to last year.
watsonx.ai, on the other hand, focuses on AI Development Platforms, holds 1.0% mindshare.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
Snowflake15.1%
Databricks9.7%
Teradata8.8%
Other66.4%
Cloud Data Warehouse
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
watsonx.ai1.0%
Gemini Enterprise Agent Platform8.0%
Azure OpenAI6.8%
Other84.2%
AI Development Platforms
 

Featured Reviews

SunilPatil1 - PeerSpot reviewer
Asset Builder at Genpact - Headstrong
Have prioritized security while managing multi-agent data migration and cloud adoption
We utilize Time Travel with Snowflake because this is a very useful feature. Everyone finds it crucial because in conventional data platforms, it's very difficult to handle these kinds of things. This feature is essential, though I don't have the use cases currently; it is just there for implementation. Regarding Snowflake's automated scaling and suspension features, this auto-scaling is very significant. We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.
AmerKhan - PeerSpot reviewer
Senior Director - Head of Solution Engineering at Osol tech (Private) Limited
Experience gains better customer interactions and user-friendliness but requires more efficient agent development
The development toolkit itself and the engine that supported the agent development was flexible. The features include support for RAG and support for generative AI. What we're looking to do is provide a human-like interface where natural language comes into play to serve HR data. Our users interact in a narrative fashion and get their queries answered. It has increased our serving of HR requests by 30%. It is user friendly, and our user base was able to work with it quite conveniently.

Quotes from Members

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

Pros

"It is definitely a good tool and a good database without any adoption problems."
"Since we are using Snowflake, it has improved the speed and reliability of our analytics processes, which is key to any data engineering or data warehousing project."
"It's ultra-fast at handling queries, which is what we find very convenient."
"The product offers a lot of great features; architectural-wise, it's got great architecture, it's kind of decoupled from storage and has virtual warehouses, and we like that we can travel and keep things virtual."
"This is the advanced version of the cloud version, so it's really a flexible tool. If you have it implemented at home, you can access it from anywhere."
"Snowflake is faster than on-premise systems and allows for variable compute power based on need."
"The way Snowflake has emerged in the past few years is impressive."
"Can be leveraged with respect to better performance, auto tuning and competition."
"The development toolkit itself and the engine that supported the agent development was flexible."
 

Cons

"Every product has room for improvement, although in this case, it needs some broadening of the functionality."
"Snowflake could improve if they had an Operational Data Store(ODS) space."
"Snowflake wasn't cheaper than BigQuery, and its affordability was the main issue."
"Room for improvement would be writebacks. It doesn't support extensively writing back to the database, and it doesn't support web applications effectively. Ultimately, it's a database call, so if we are building web applications using Snowflake, it isn't that effective because there is some turnaround time from the database."
"There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."
"They need to incorporate some basic OLAP capabilities in the backend or at the database level. Currently, it is purely a database. They call it purely a data warehouse for the cloud. Currently, just like any database, we have to calculate all the KPIs in the front-end tools. The same KPIs again need to be calculated in Snowflake. It would be very helpful if they can include some OLAP features. This will bring efficiency because we will be able to create the KPIs within Snowflake itself and then publish them to multiple front-end tools. We won't have to recreate the same in each project. There should be the ability to automate raised queries, which is currently not possible. There should also be something for Exception Aggregation and things like that."
"Snowflake is expensive, but when I consider what we get for that price, it's fair."
"If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial."
"The platform and toolkit that we use to develop agents could benefit from improvements from a user-friendliness perspective."
 

Pricing and Cost Advice

"Users have to pay a licensing fee for the solution, which is expensive."
"Pricing is based on usage. It is the most expensive of our data tools."
"The whole licensing system is based on credit points. You can also make a license agreement with the company so that you buy credit points and then you use them. What you do not use in one year can be carried over to the next year."
"On average, with the number of queries that we run, we pay approximately $200 USD per month."
"Snowflake goes by credits. For a financial institution where you have 5,000 employees, monthly costs may run up to maybe $5,000 to $6,000. This is actually based on the usage. It is mostly the compute cost. Your computing cost is the variable that is actually based on your usage. It is pay-per-use. In a pay-per-use case, you won't be spending more than $6,000 to $7,000 a month. It is not more than that for a small or medium enterprise, and it may come down to $100K per year. Storage is very standard, which is $23 a terabyte. It is not much for any enterprise. If you have even 20 terabytes, you are not spending more than $400 per month, which may turn out to be $2,000 to $3,000 per annum."
"It is hard to say because we're usually engaged in the transition as opposed to the long term. Their storage costs are easily within pennies of what AWS S3 would normally cost. Most of the clients I've been working with are in the financial sector, and they're relatively small. I would put them in an SMB connection. The first thing we have to bring up for people is that they're going to build this. They shouldn't store their data in S3. They should pipeline directly into Snowflake and use it on their storage. So, the cost is a big issue because these are small to medium size companies, and that is the biggest thing we had to price point for them."
"Pricing can be confusing for customers."
"Part of the problem with the pricing is that it is very difficult for businesses to get an idea of how expensive it might be until they actually start using Snowflake."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
20%
Manufacturing Company
11%
Outsourcing Company
5%
Computer Software Company
5%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business30
Midsize Enterprise20
Large Enterprise61
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Snowflake?
I am not the person who manages pricing, setup cost, and licensing. Our team is not limited in pricing. The only experience we have had in terms of running and reprocessing a large number of histor...
What needs improvement with Snowflake?
One main area for improvement in Snowflake is cost visibility and optimization; while it's flexible and scalable, costs can increase quickly if warehouses are left running unnecessarily or workload...
What is your primary use case for Snowflake?
As a Data Engineer, I primarily use Snowflake for data warehousing tasks as well as ETL processing, and sometimes I also use it for data sharing. I personally find Snowflake better than other tools...
What needs improvement with watsonx.ai?
Improving on the development toolkit would help. The platform and toolkit that we use to develop agents could benefit from improvements from a user-friendliness perspective. Making it more user-fri...
What is your primary use case for watsonx.ai?
We are looking to develop HR agents on it, HR-based bots. We integrated it with our HR system, HRIS.
What advice do you have for others considering watsonx.ai?
This solution is highly recommended. On a scale of 1-10, I rate watsonx.ai a seven out of ten.
 

Also Known As

Snowflake Computing, Snowflake Data Cloud
No data available
 

Overview

 

Sample Customers

Accordant Media, Adobe, Kixeye Inc., Revana, SOASTA, White Ops
Information Not Available
Find out what your peers are saying about Snowflake Computing, Teradata, Google and others in Cloud Data Warehouse. Updated: June 2026.
900,747 professionals have used our research since 2012.