Microsoft Azure Machine Learning Studio vs OpenVINO comparison

Why PeerSpot?

Microsoft and Intel are both solutions in the AI Development Platforms category. Microsoft is ranked #6 with an average rating of 7.8, while Intel is ranked #14 with an average rating of 8.0. Microsoft holds a 3.1% mindshare in AIDP, compared to Intel’s 1.7% mindshare. Additionally, 93% of Microsoft users are willing to recommend the solution, compared to 100% of Intel users who would recommend it.

Microsoft Azure Machine Lea...

Read 62 Microsoft Azure Machine Learning Studio reviews

7,521 Views
3,836 Comparison Views

93% willing to recommend

OpenVINO

Read 7 OpenVINO reviews

2,271 Views
2,271 Comparison Views

100% willing to recommend

Microsoft Azure Machine Lea...

OpenVINO

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 4, 2024

Microsoft Azure Machine Learning Studio and OpenVINO compete in the machine learning and AI domain. Azure Machine Learning Studio tends to have the upper hand in integrated services and scalability while OpenVINO focuses on optimizing AI models on Intel hardware.

Features: Microsoft Azure Machine Learning Studio provides an end-to-end environment for machine learning models, including a drag-and-drop interface, automated machine learning, and integrated MLOps capabilities. OpenVINO offers model optimization and deployment specifically on Intel hardware, with support for deep learning models and computer vision tasks, and features heterogeneous execution.

Ease Of Deployment and Customer Service: Microsoft Azure Machine Learning Studio simplifies deployment with cloud-based automation tools and has extensive documentation, offering a seamless deployment experience due to strong customer support. OpenVINO requires specific knowledge of Intel hardware for deployment, supported by a strong community and detailed technical resources.

Pricing and ROI: Microsoft Azure Machine Learning Studio might involve significant setup costs but offers scalable pricing options for good ROI if managed well. OpenVINO is a cost-effective solution as it is open-source, with minimal setup costs, though ROI depends on Intel-based hardware use and model optimization efficiencies. Azure often provides more predictable costs, while OpenVINO is cost-efficient for specific hardware scenarios.

To learn more, read our detailed Microsoft Azure Machine Learning Studio vs. OpenVINO Report (Updated: September 2026).

Buyer's Guide

Microsoft Azure Machine Learning Studio vs. OpenVINO

September 2026

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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

Microsoft Azure Machine Lea...

Ranking in AI Development Platforms

6th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (9th)

OpenVINO

Ranking in AI Development Platforms

14th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2026, in the AI Development Platforms category, the mindshare of Microsoft Azure Machine Learning Studio is 3.1%, down from 4.8% compared to the previous year. The mindshare of OpenVINO is 1.7%, down from 1.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Microsoft Azure Machine Learning Studio	3.1%
OpenVINO	1.7%
Other	95.2%

AI Development Platforms

Featured Reviews

reviewer2722962

Data Scientist

Platform accelerates model development, enhances collaboration, and offers efficient deployment

The best features Microsoft Azure Machine Learning Studio offers include deep integration with Python notebooks and Azure Data Lake, which allows me to import external data, and through the pipeline, I can build my models, performing what is called data injection for my model building, making that deep integration quite interesting to use. Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding. Microsoft Azure Machine Learning Studio has positively impacted my organization by reducing our project delivery times and increasing the pace at which we work, allowing us to focus on other more important tasks. Using Microsoft Azure Machine Learning Studio has reduced our model development time from approximately four hours to about two hours.

Read full review

Mahender Reddy Pokala

AI Developer at University of Chicago

Improved model deployment on edge devices, but compatibility and scalability present challenges

I found OpenVINO's ability to convert custom models into its format particularly beneficial, as businesses sometimes require unique models specific to their use cases. Utilizing OpenVINO allowed me to run these custom models on devices directly, which I found quite impressive. Additionally, the Model Zoo offered by OpenVINO added value to the product.

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 Microsoft Azure Machine Learning Studio is the ease of use for starting projects."

"The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics."

"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."

"If you want to build a solution quickly without knowing any coding, it's pretty good to start with."

"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."

"Microsoft Azure Machine Learning Studio is a good solution that I would recommend to others, but I would like to see more support and more information available for developers."

"It's easy to deploy."

"The most valuable feature of the solution is the availability of ChatGPT in the solution."

More Microsoft Azure Machine Learning Studio pros

"The solution's ability to stream data directly from camera inputs is the most valuable aspect for us."

"Compared to Jetson Nano or Jetson TX2, or Jetson Xavier, OpenVINO is a much more cost-effective solution."

"The benefit from using OpenVINO is that NVIDIA is dominating the market of GPUs and they set the price, so if I am able to run an LLM doing inference in commodity hardware, I am saving costs."

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

"Intel's support team is very good."

"The runtime of OpenVINO is highly valuable for running different computer vision models."

"The initial setup is quite simple."

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

More OpenVINO pros

Cons

"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."

"We've found that the solution runs at a high cost."

"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."

"I would like to see better prediction and analysis."

"The regulatory requirements of the product need improvement."

"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."

"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."

"I cannot comment on specific improvements yet as we are still exploring and need more time to identify the areas that require enhancements."

More Microsoft Azure Machine Learning Studio cons

"I think that it's not properly designed for scalability. It's designed for other purposes, specifically to be able to use Intel hardware and run inference using generative models or deep learning models in Intel hardware."

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

"It would be great if OpenVINO could convert new models into its format more quickly."

"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."

"I couldn't get it to run on my Raspberry Pi 4 because the software packages to download were no longer available."

"Scalability is a challenge with OpenVINO, particularly when I try to connect multiple streams of input or run multiple edge devices consecutively."

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

Pricing and Cost Advice

"The licensing cost is very cheap. It's less than $50 a month."

"The pricing for Microsoft products can be complex due to changes and being cloud-based, so it's not straightforward. I've been familiar with it for years, but sometimes details about product licenses and distribution can be unclear. For Microsoft Azure Machine Learning Studio specifically, I would rate the price a six out of ten."

"The platform's price is low."

"In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."

"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."

"It is less expensive than one of its competitors."

"To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."

"The product is not that expensive."

More Microsoft Azure Machine Learning Studio 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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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Outsourcing Company

12%

Performing Arts

Construction Company

Manufacturing Company

17%

Comms Service Provider

14%

Outsourcing Company

10%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	23
Midsize Enterprise	6
Large Enterprise	32

No data available

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

See all answers

What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?

The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go, meaning it won't cost excessively unless specific resources are used.

See all answers

What needs improvement with Microsoft Azure Machine Learning Studio?

The initial setup can be a bit challenging for someone new, as the learning curve can be steep, but once I master the platform, I find it quite manageable. I would love to see the integration of a ...

See all answers

What is your experience regarding pricing and costs for OpenVINO?

The pricing of OpenVINO is reasonable.

See all answers

What needs improvement with OpenVINO?

I have heard good things about OpenVINO. It doesn't consume much current for external GPU usage. However, it has some downsides because I couldn't get it to run on my Raspberry Pi 4. While not spec...

See all answers

What is your primary use case for OpenVINO?

I wanted to use OpenVINO for my Raspberry Pi to analyze my sleep with a night vision camera and to improve GPU performance on my Raspberry Pi. I would have used OpenVINO's Model Optimizer feature t...

See all answers

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Compared 14% of the time

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Compared 9% of the time

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Product Reports

Buyer's Guide

Microsoft Azure Machine Learning Studio

September 2026

Microsoft Azure Machine Learning Studio report

Download Microsoft Azure Machine Learning Studio product report

Buyer's Guide

OpenVINO

September 2026

Download OpenVINO product report

Also Known As

Azure Machine Learning, MS Azure Machine Learning Studio

No data available

Overview

Microsoft Azure Machine Learning Studio offers a drag-and-drop interface, seamless integration with tools, and compatibility with multiple programming languages, making it user-friendly and efficient for developing and deploying machine-learning models.

This platform supports Python, R, and more, with automation features like AutoML, scalable resources, and cognitive services enhance data normalization and deployment. Users can easily create models, integrate with Azure services, and accelerate data science projects using its comprehensive library. Despite its benefits, improvements are suggested for cross-platform integration, enhanced data preparation, and clearer pricing strategies. Users also look for better deployment flexibility, more algorithm options, and examples. Expanded AI features, increased accessibility, and DevOps integration would further benefit its users.

What are the main features of Microsoft Azure Machine Learning Studio?

Drag-and-Drop Interface: Simplifies model creation and experimentation.
Integration with Tools: Works seamlessly with Azure services.
Compatibility with Languages: Supports Python, R, among others.
AutoML: Automates machine learning processes.
Scalable Resources: Provides adaptability in modeling.
Cognitive Services: Enhances data processing capabilities.

What benefits and ROI should users look for?

User-Friendly Design: Encourages accessibility for non-experts.
Seamless Deployment: Eases transition from model development to deployment.
Comprehensive Library: Accelerates data science processes.
Predictive Analytics: Aids in forecasting trends and behaviors.
Fraud Detection: Enhances security measures effectively.

In healthcare, finance, and retail, users apply Microsoft Azure Machine Learning Studio for developing machine learning models, predictive analytics, and deploying models efficiently. Its low-code interface supports experimentation for customer behavior prediction, data analytics, fraud detection, and automated machine learning, facilitating their business and research applications.

Microsoft

OpenVINO offers comprehensive tools for computer vision tasks, widely appreciating its compatibility with multiple hardware and frameworks. It facilitates seamless integration and supports direct camera streaming, making it versatile for device deployment and optimization.

OpenVINO is a powerhouse for machine learning enthusiasts, providing support for Intel CPUs and non-NVIDIA GPUs. Its compatibility spans multiple platforms, enhancing the deployment of models on diverse hardware. Users can efficiently convert and deploy models using OpenVINO's Model Zoo, coupled with support for frameworks like PyTorch and TensorFlow. Despite its strengths, there's room for improvement in conversion speed and better compatibility beyond Intel. It proves valuable for IoT applications, optimizing models efficiently for edge devices.

What are the standout features of OpenVINO?

Runtime for computer vision models: Offers reliable runtime support for various AI models.
Support for Apple silicon: Extends compatibility to Apple hardware.
Ease of integration: Simplifies the incorporation into existing workflows.
Direct camera streaming: Allows direct integration with camera systems.
Model conversion feature: Facilitates seamless model adaptation and deployment.
Model Zoo: Provides access to a collection of pre-trained models.
Compatibility with frameworks: Integrates with PyTorch, TensorFlow.

What benefits should users look for?

Hardware cost savings: Reduces dependency on NVIDIA, offering inexpensive alternatives.
Custom deployment: Enables tailored model deployment.
Versatility: Supports numerous frameworks, enhancing flexibility.
Efficient IoT processing: Optimizes performance on edge devices.

OpenVINO's applications span multiple industries, notably in video analytics and IoT. Users utilize its capabilities to build sophisticated systems for real-time analytics, model optimization for low-power devices, and intelligent edge processing. Projects like sleep analysis on Raspberry Pi or surveillance systems showcase its diverse implementations, highlighting its capacity to enhance industry-specific solutions.

Intel

Sample Customers

Walgreens Boots Alliance, Schneider Electric, BP

Information Not Available

Buyer's Guide

Microsoft Azure Machine Learning Studio vs. OpenVINO

September 2026

Free Report: Microsoft Azure Machine Learning Studio vs. OpenVINO

Find out what your peers are saying about Microsoft Azure Machine Learning Studio vs. OpenVINO and other solutions. Updated: September 2026.

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