Amazon SageMaker vs TIBCO Data Science comparison

Amazon Web Services (AWS) and TIBCO are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while TIBCO is ranked #24. Amazon Web Services (AWS) holds a 7.5% mindshare in Data Science Platforms, compared to TIBCO’s 0.6% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of TIBCO users who would recommend it.

Amazon SageMaker

Read 36 Amazon SageMaker reviews

6,544 Views
5,235 Comparison Views

91% willing to recommend

TIBCO Data Science

Read 4 TIBCO Data Science reviews

330 Views
255 Comparison Views

100% willing to recommend

Amazon SageMaker

TIBCO Data Science

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

TIBCO Data Science and Amazon SageMaker are platforms competing in data science and machine learning. While TIBCO Data Science excels in intuitive use and support, Amazon SageMaker holds the upper hand with its robust features and integration, offering greater perceived value.

Features: TIBCO Data Science provides intuitive analytic capabilities, visual workflows, and strong enterprise integration. In contrast, Amazon SageMaker offers comprehensive model training and deployment automation, extensive machine learning tools, and seamless AWS integration.

Room for Improvement: TIBCO Data Science could enhance its scalability, improve automation in model deployment, and offer more advanced machine learning tools. Amazon SageMaker might work on reducing its learning curve, improving customer support response times, and offering more competitive pricing.

Ease of Deployment and Customer Service: TIBCO Data Science provides straightforward deployment and responsive support, simplifying entry into data science projects. Amazon SageMaker, leveraging AWS infrastructure, allows for streamlined deployment with vast resources but involves a steeper learning curve.

Pricing and ROI: TIBCO Data Science is more affordable with lower initial setup costs, delivering quick ROI through its straightforward application. Amazon SageMaker, despite the higher upfront investment, offers extensive functionality and flexibility, leading to substantial long-term ROI.

To learn more, read our detailed Amazon SageMaker vs. TIBCO Data Science Report (Updated: January 2025).

Buyer's Guide

Amazon SageMaker vs. TIBCO Data Science

January 2025

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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 SageMaker

Ranking in Data Science Platforms

3rd

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (4th)

TIBCO Data Science

Ranking in Data Science Platforms

24th

Average Rating

7.6

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

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 TIBCO Data Science is 0.6%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

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.

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Veeresh-SHRINGARI

Portfolio Manager, AI & ML at a healthcare company with 10,001+ employees

A straightforward initial setup and good reporting but needs better documentation

It would be ideal if it could be put onto the NMP where you can make more of an analysis. Right now, people don't have enough time to go through the report and make an analysis. It should provide the information of what is on the report into some kind of a dialogue form. Then, a person can ask certain questions and it could interactively give the required report. In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues. If you are doing certain operations of RDBMS, you suffer in terms of the latency of the data. That can be improved upon. Users should be able to cross tables of the web pages they are developing on Spotfire and this needs to be really easy and convenient. Right now, you need to do a lot of tweaks. The solution should be more user-friendly and require fewer tweaks, extensions, and workarounds.

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Quotes from Members

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

Pros

"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."

"The product aggregates everything we need to build and deploy machine learning models in one place."

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"The deployment is very good, where you only need to press a few buttons."

"We were able to use the product to automate processes."

"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 these models, making accessing them convenient as needed."

"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."

"The technical support of the tool was good."

More Amazon SageMaker pros

"The most valuable feature is the ease of setting up visualizations."

"The most valuable feature is the performance."

"We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost."

"The idea that you don't have to generate reports each day but they are sent automatically is great."

Cons

"The product must provide better documentation."

"The solution requires a lot of data to train the model."

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."

"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."

"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."

"The main challenge with Amazon SageMaker is the integrations."

More Amazon SageMaker cons

"I would like the visualization for the map of countries to be more easily configurable."

"The scripting for customization could be improved."

"Additional templates would help to get things moving more quickly in terms of getting the reports out."

"In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues."

Pricing and Cost Advice

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"The pricing is comparable."

"Amazon SageMaker is a very expensive product."

"SageMaker is worth the money for our use case."

"On average, customers pay about $300,000 USD per month."

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."

"There is no license required for the solution since you can use it on demand."

"The tool's pricing is reasonable."

More Amazon SageMaker pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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...

See all answers

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...

See all answers

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.

See all answers

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Comparisons

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Compared 21% of the time

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Compared 12% of the time

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Compared 9% of the time

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Domino Data Science Platform vs Amazon SageMaker

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TIBCO Statistica vs TIBCO Data Science

Compared 33% of the time

Databricks vs TIBCO Data Science

Compared 31% of the time

More TIBCO Data Science Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

March 2025

Download Amazon SageMaker product report

Buyer's Guide

Data Science Platforms

February 2025

Download TIBCO Data Science product report

Also Known As

AWS SageMaker, SageMaker

Alpine Data Chorus

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

TIBCO Spotfire Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.

TIBCO

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Havas Media, Tipping Point Community, eviCore

Buyer's Guide

Amazon SageMaker vs. TIBCO Data Science

January 2025

Free Report: Amazon SageMaker vs. TIBCO Data Science

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