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 7.9, while TIBCO is ranked #24. Amazon Web Services (AWS) holds a 7.6% 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

7,315 Views
5,868 Comparison Views

91% willing to recommend

TIBCO Data Science

Read 4 TIBCO Data Science reviews

346 Views
279 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 February 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.6%, 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

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

"Allows you to create API endpoints."

"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."

"The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."

"I recommend SageMaker for ML projects if you need to build models from scratch."

"I have seen a return on investment, probably a factor of four or five."

"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 tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."

More Amazon SageMaker pros

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

"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."

"The most valuable feature is the performance."

Cons

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

"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."

"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."

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

"The solution needs to be cheaper since it now charges per document for extraction."

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

"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."

"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

More Amazon SageMaker cons

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

"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."

"The scripting for customization could be improved."

Pricing and Cost Advice

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

"The solution is relatively cheaper."

"Amazon SageMaker is a very expensive product."

"The tool's pricing is reasonable."

"The pricing is comparable."

"Databricks solution is less costly than Amazon SageMaker."

"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 product is expensive."

More Amazon SageMaker pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

15%

Computer Software Company

11%

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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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...

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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.

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Comparisons

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Compared 25% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

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Compared 9% of the time

Dataiku vs Amazon SageMaker

Compared 8% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

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TIBCO Statistica vs TIBCO Data Science

Compared 39% of the time

Dataiku vs TIBCO Data Science

Compared 24% of the time

More TIBCO Data Science Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

February 2025

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Buyer's Guide

Data Science Platforms

January 2025

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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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