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Amazon SageMaker vs Dremio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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 SageMaker
Ranking in Data Science Platforms
3rd
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
36
Ranking in other categories
AI Development Platforms (4th)
Dremio
Ranking in Data Science Platforms
8th
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
8
Ranking in other categories
Cloud Data Warehouse (9th)
 

Mindshare comparison

As of March 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.5%, down from 9.9% compared to the previous year. The mindshare of Dremio is 4.3%, up from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Hemant Paralkar - PeerSpot reviewer
Improves team collaboration with advanced feature sharing but needs a better user experience
Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.
KamleshPant - PeerSpot reviewer
Solution offers quick data connection with an edge in computation
It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS. It is a similar experience between the based application and cloud-based application. You just get the source, connect the data, get visualization, get connected, and do whatever you want. They say data reflection is one way where they do the caching and all that. Starburst also does the caching. In Starburst, you have a data product. Here, the data product comes from a reflection perspective. The y are working on a columnar memory map, columnar computation. That will have some edge in computation.

Quotes from Members

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

Pros

"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The most valuable feature of Amazon SageMaker is SageMaker Studio."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
"Allows you to create API endpoints."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"I have seen a return on investment, probably a factor of four or five."
"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio is very easy to use for building queries."
"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Dremio allows querying the files I have on my block storage or object storage."
"We primarily use Dremio to create a data framework and a data queue."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."
 

Cons

"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."
"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"Lacking in some machine learning pipelines."
"The main challenge with Amazon SageMaker is the integrations."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."
"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."
"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"It shows errors sometimes."
"There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it on single nodes using a community version."
"They need to have multiple connectors."
"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."
 

Pricing and Cost Advice

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"I would rate the solution's price a ten out of ten since it is very high."
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"SageMaker is worth the money for our use case."
"On average, customers pay about $300,000 USD per month."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."
"The tool's pricing is reasonable."
"Databricks solution is less costly than Amazon SageMaker."
"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
"Dremio is less costly competitively to Snowflake or any other tool."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Educational Organization
14%
Computer Software Company
11%
Manufacturing Company
9%
Financial Services Firm
31%
Computer Software Company
10%
Manufacturing Company
7%
Healthcare Company
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
What do you like most about Amazon SageMaker?
We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...
What is your experience regarding pricing and costs for Amazon SageMaker?
Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.
What do you like most about Dremio?
Dremio allows querying the files I have on my block storage or object storage.
What is your experience regarding pricing and costs for Dremio?
The licensing is very expensive. We need a license to scale as we are currently using the community version.
What needs improvement with Dremio?
They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today. They don't have Salesforce connectivity. However, Starburst do...
 

Comparisons

 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
UBS, TransUnion, Quantium, Daimler, OVH
Find out what your peers are saying about Amazon SageMaker vs. Dremio and other solutions. Updated: January 2025.
841,164 professionals have used our research since 2012.