Amazon SageMaker vs TensorFlow comparison

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Executive SummaryUpdated on Dec 4, 2024

TensorFlow and Amazon SageMaker compete in the field of machine learning, with TensorFlow being an open-source framework and SageMaker offering a fully managed service. TensorFlow stands out in ease of use and flexibility, while Amazon SageMaker is praised for integrated tools and a robust service ecosystem.

Features: TensorFlow provides deep learning support, flexibility for customization, and a large community backing for problem-solving. SageMaker offers automatic model tuning, integrated Jupyter notebooks, and built-in algorithms for quick model deployment.

Room for Improvement: TensorFlow could enhance its deployment processes to ease infrastructure management, improve documentation for user-friendliness, and streamline community support channels. SageMaker might reduce the complexity of transitioning for users not deeply rooted in AWS, optimize cost management tools, and expand beyond existing AWS dependencies for wider appeal.

Ease of Deployment and Customer Service: TensorFlow demands users to manage their infrastructure, which adds complexity for less technical users. Amazon SageMaker simplifies deployment through AWS with pre-configured instances and comprehensive documentation, combined with responsive AWS customer support.

Pricing and ROI: TensorFlow, being open-source, offers low initial setup costs and favorable ROI when leveraging existing resources. SageMaker involves costs linked to AWS resources, possibly leading to higher upfront expenses, but its managed services can result in long-term savings by lowering operational overhead.

To learn more, read our detailed Amazon SageMaker vs. TensorFlow Report (Updated: June 2025).

Amazon SageMaker vs. TensorFlow

June 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 AI Development Platforms

5th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (2nd)

TensorFlow

Ranking in AI Development Platforms

6th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.3%, down from 8.1% compared to the previous year. The mindshare of TensorFlow is 3.9%, down from 6.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

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Dan Bryant

CEO at II4Tech

A strong solution for providing insight into machine learning strategies

I'm not a professional with machine learning. Early on, I was working with data scientists and built a platform for some old-school data scientists to turn around their models faster, and they were focused on electric prices. Based on that experience and my understanding of our value, I'm researching all the machine learning tools. I realized I would have to be a specialist in any of them, and my main skillset is in systems engineering and data engines. I look forward to being an analytics specialist. In real life, I would be better off hiring a professional because when I decide which tool I want to use for what job, I could hire that professional. They would be valuable to me across the whole of what we do. It's kinda of what I do when I build hardware and new products or do version upgrades. I hire a team just for production that are experts in their particular field, so I get production-quality pieces. At that point, my internal team can add the necessary analytics or automation. Hopefully, anyone getting the solution already knows what they will use it for. If they're starting from scratch, I strongly recommend hiring a consultant. I rate TensorFlow an eight out of ten because, for my intents and purposes, I don't know what else one can use to get into the machine learning game if you're going to export models.

Read full review

Quotes from Members

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

Pros

"The most valuable feature of Amazon SageMaker is SageMaker Studio."

"It's user-friendly for business teams as they can understand many aspects through the AWS interface."

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

"The few projects we have done have been promising."

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

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

"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."

More Amazon SageMaker pros

"It's got quite a big community, which is useful."

"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."

"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."

"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."

"TensorFlow is a framework that makes it really easy to use for deep learning."

"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."

"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."

"It is easy to use and learn."

More TensorFlow pros

Cons

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

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."

"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."

"One area for improvement is the pricing, which can be quite high."

"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

"Lacking in some machine learning pipelines."

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."

More Amazon SageMaker cons

"The process of creating models could be more user-friendly."

"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."

"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."

"We encountered version mismatch errors while using the product."

"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."

"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."

"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."

"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."

More TensorFlow cons

Pricing and Cost Advice

"The pricing is comparable."

"The tool's pricing is reasonable."

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

"The pricing could be better, especially for querying. The per-query model feels expensive."

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

"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."

"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."

More Amazon SageMaker pricing and cost advice

"I am using the open-source version of TensorFlow and it is free."

"The solution is free."

"We are using the free version."

"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."

"I did not require a license for this solution. It a free open-source solution."

"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."

"TensorFlow is free."

"I rate TensorFlow's pricing a five out of ten."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Manufacturing Company

14%

Computer Software Company

12%

Financial Services Firm

University

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?

The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...

What do you like most about TensorFlow?

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

What is your experience regarding pricing and costs for TensorFlow?

I am not familiar with the pricing setup cost and licensing.

What needs improvement with TensorFlow?

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libr...

Databricks vs Amazon SageMaker

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Product Reports

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Amazon SageMaker

June 2025

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May 2025

Also Known As

AWS SageMaker, SageMaker

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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)

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

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