Amazon SageMaker vs OpenVINO comparison

Amazon Web Services (AWS) and Intel are both solutions in the AI Development Platforms category. Amazon Web Services (AWS) is ranked #4 with an average rating of 7.6, while Intel is ranked #13. Amazon Web Services (AWS) holds a 8.1% mindshare in AIDP, compared to Intel’s 2.3% mindshare. Additionally, 89% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Intel users who would recommend it.

Amazon SageMaker

Read 29 Amazon SageMaker reviews

4,257 Views
3,388 Comparison Views

89% willing to recommend

OpenVINO

Read 3 OpenVINO reviews

2,322 Views
1,352 Comparison Views

100% willing to recommend

Amazon SageMaker

OpenVINO

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon SageMaker and OpenVINO based on real PeerSpot user reviews.

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon SageMaker vs. OpenVINO Report (Updated: October 2024).

Buyer's Guide

Amazon SageMaker vs. OpenVINO

October 2024

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Categories and Ranking

Amazon SageMaker

Ranking in AI Development Platforms

4th

Average Rating

7.8

Reviews Sentiment

9.1

Number of Reviews

Ranking in other categories

Data Science Platforms (5th)

OpenVINO

Ranking in AI Development Platforms

13th

Average Rating

8.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 8.1%, down from 8.8% compared to the previous year. The mindshare of OpenVINO is 2.3%, down from 4.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Natu Lauchande

Tech Lead - Sanlam Fintech Cluster - Data,ML,AI Eng. at Sanlam

Easy to use and manage, but the documentation does not have a lot of information

SageMaker Studio sounds very interesting. Feature Store and data pipeline features are very interesting. The product is a one-stop shop. It allows people without much engineering knowledge to try out and deploy models in environments similar to the production environments. The tool makes our ML model development a bit more efficient because everything is in one environment. It is easy to manage compared to when things were in different components of AWS. Amazon SageMaker is in AWS, so I need not pay two bills. It is one less system to manage, so it is easier.

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Zafar Muhammed

Machine Learning Software Developer at freelancer

A free toolkit providing improved neural network performance

The model optimization is a little bit slow — it could be improved. They should introduce some type of deep learning accelerator, like Jetson Xavier NX. There is a lacking in vehicle recognition — types of vehicles. Differentiating between cars, SUVs and different types of light, heavy, and medium trucks can be tricky. We have to train such models ourselves and then transfer them onto OpenVINO.

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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 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."

"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."

"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."

"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."

"SageMaker is a comprehensive platform where I can perform all machine learning activities."

"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."

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

"The tool makes our ML model development a bit more efficient because everything is in one environment."

More Amazon SageMaker pros

"The features for model comparison, the feature for model testing, evaluation, and deployment are very nice. It can work almost with all the models."

"The initial setup is quite simple."

"The inferencing and processing capabilities are quite beneficial for our requirements."

Cons

"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."

"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."

"Amazon might need to emphasize its capabilities in generative models more effectively."

"The product must provide better documentation."

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."

"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."

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

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

More Amazon SageMaker cons

"At this point, the product could probably just use a greater integration with more machine learning model tools."

"The model optimization is a little bit slow — it could be improved."

"It has some disadvantages because when you're working with very complex models, neural networks if OpenVINO cannot convert them automatically and you have to do a custom layer and later add it to the model. It is difficult."

Pricing and Cost Advice

"The support costs are 10% of the Amazon fees and it comes by default."

"The solution is relatively cheaper."

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

"The tool's pricing is reasonable."

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

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

"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

"We didn't have to pay for any licensing with Intel OpenVINO. Everything is available on their site and easily downloadable for free."

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816,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

47%

University

Computer Software Company

Financial Services Firm

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...

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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...

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What is your experience regarding pricing and costs for Amazon SageMaker?

The pricing is based on usage, and I find it reasonable for what we use it for.

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Comparisons

Databricks vs Amazon SageMaker

Compared 27% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Google Vertex AI vs Amazon SageMaker

Compared 11% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

Dataiku vs Amazon SageMaker

Compared 6% of the time

More Amazon SageMaker Competitors

TensorFlow vs OpenVINO

Compared 53% of the time

PyTorch vs OpenVINO

Compared 20% of the time

Azure OpenAI vs OpenVINO

Compared 12% of the time

Google Vertex AI vs OpenVINO

Compared 8% of the time

Google Cloud AI Platform vs OpenVINO

Compared 7% of the time

More OpenVINO Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

November 2024

Download Amazon SageMaker product report

Buyer's Guide

OpenVINO

November 2024

Download OpenVINO product report

Also Known As

AWS SageMaker, SageMaker

No data available

Learn More

Amazon Web Services (AWS)

Video not available

Intel

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.

OpenVINO toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel hardware, maximizing performance. The OpenVINO toolkit includes the Deep Learning Deployment Toolkit (DLDT).

Sample Customers

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

Information Not Available

Buyer's Guide

Amazon SageMaker vs. OpenVINO

October 2024

Free Report: Amazon SageMaker vs. OpenVINO

Find out what your peers are saying about Amazon SageMaker vs. OpenVINO and other solutions. Updated: October 2024.

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816,406 professionals have used our research since 2012.

See our Amazon SageMaker vs. OpenVINO report.

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We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.