Microsoft Azure Machine Learning Studio vs OpenVINO comparison

Microsoft Azure Machine Learning Studio vs. OpenVINO

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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

"It is a solution that can cover all the processes from data preparation to mobilization data while serving the clients and production."

"It's easy to deploy. It has many features which help the person avoid delving into more technical things."

"The most valuable feature is data normalization."

"It's a great option if you are fairly new and don't want to write too much code."

"Microsoft Azure Machine Learning Studio offers a hands-on experience with lots of flexibility."

"Microsoft Azure Machine Learning Studio is easy to use and deploy."

"The product enables quick data preparation and data processing pipeline as well as modeling work and it's all part of Azure Machine Learning."

"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use."

More Microsoft Azure Machine Learning Studio pros

"Intel's support team is very good."

"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."

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

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

"One positive aspect about OpenVINO is that it supports more frameworks than the Google Coral TPU."

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

More OpenVINO pros

Cons

"The regulatory requirements of the product need improvement."

"The product must improve its documentation."

"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."

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

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

"When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers."

"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."

"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."

More Microsoft Azure Machine Learning Studio cons

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

"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."

"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."

"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."

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

Pricing and Cost Advice

"There is a license required for this solution."

"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."

"ML Studio's pricing becomes a numbers game."

"There isn’t any such expensive costs and only a standard license is required."

"The solution operates on a pay-per-use model."

"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 am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."

"The platform's price is low."

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

10%

Construction Company

Performing Arts

Manufacturing Company

20%

Comms Service Provider

12%

Outsourcing Company

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 ...

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.

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 ...

What is your experience regarding pricing and costs for OpenVINO?

The pricing of OpenVINO is reasonable.

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...

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...

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Compared 10% of the time

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Compared 9% of the time

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Product Reports

Microsoft Azure Machine Learning Studio

Download Microsoft Azure Machine Learning Studio product report

Microsoft Azure Machine Learning Studio report

OpenVINO

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

Microsoft Azure Machine Learning Studio vs. OpenVINO