Microsoft Azure Machine Learning Studio vs OpenVINO comparison

Microsoft Azure Machine Learning Studio vs. OpenVINO

Download the complete report

Helped 892,287 peers since 2012

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

5th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (6th)

OpenVINO

Ranking in AI Development Platforms

15th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2026, in the AI Development Platforms category, the mindshare of Microsoft Azure Machine Learning Studio is 3.4%, down from 7.3% compared to the previous year. The mindshare of OpenVINO is 1.8%, up from 1.6% 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	3.4%
OpenVINO	1.8%
Other	94.8%

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

Juan Huertas

Senior Data Scientist /Ai Engineer at Zantaz Data Resources

Empowers cost-effective model deployment on widely accessible hardware while needing cross-platform enhancements

What could be improved in OpenVINO is making the product more cross-platform. I know they are working with third-party plugins to extend the toolkit, and in this way, I can use it with NVIDIA GPUs or with other hardware because now it's primarily working in all Intel hardware. CPU, GPUs, TPUs, but only from Intel. If they make more cross-platform functionality, it would be great. It's difficult to make it work faster than the NVIDIA toolkit in their own GPUs. At least having the possibility and making it work faster than now in other hardware that is not from Intel provided would be beneficial.

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 solution is very fast and simple for a data science solution."

"Visualisation, and the possibility of sharing functions are key features."

"Thanks to the model I designed, the productivity of processing invoices has increased by over 11%, because the team members only verify invoices that are discrepancy-free now."

"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."

"The solution facilitates our production."

"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."

"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML, which is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon, and it has the most sophisticated set of categories of parameters with good data encodings, options, and the most detailed settings for specific models."

"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."

More Microsoft Azure Machine Learning Studio pros

"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."

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

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

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

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

More OpenVINO pros

Cons

"Machine Learning Studio is more dependent on legacy Machine Learning algorithms. It would be beneficial for them to incorporate more services required for LLMs or LLM evaluation."

"The interface is a bit overloaded."

"Enable creating ensemble models easier, adding more machine learning algorithms."

"The speed of deployment should be faster, as should testing."

"It would be nice if the product offered more accessibility in general."

"The data cleaning functionality is something that could be better and needs to be improved."

"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."

"They should have a desktop version to work on the platform."

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."

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

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

"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."

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

"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."

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

More OpenVINO cons

Pricing and Cost Advice

"There is a lack of certainty with the solution's pricing."

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

"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."

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

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

"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."

"The platform's price is low."

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

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."

See which vendors are best for you

Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.

See recommendations

892,287 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

13%

Manufacturing Company

Performing Arts

Computer Software Company

Manufacturing Company

29%

Financial Services Firm

11%

Comms Service Provider

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	23
Midsize Enterprise	6
Large Enterprise	30

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 do you like most about Microsoft Azure Machine Learning Studio?

The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.

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 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...

Gemini Enterprise Agent Platform vs Microsoft Azure Machine Learning Studio

Comparisons

Compared 16% of the time

KNIME Business Hub vs Microsoft Azure Machine Learning Studio

Compared 10% of the time

Databricks vs Microsoft Azure Machine Learning Studio

Compared 6% of the time

TensorFlow vs Microsoft Azure Machine Learning Studio

Compared 4% of the time

Dataiku vs Microsoft Azure Machine Learning Studio

Compared 4% of the time

More Microsoft Azure Machine Learning Studio Competitors

TensorFlow vs OpenVINO

Compared 32% of the time

PyTorch vs OpenVINO

Compared 19% of the time

Gemini Enterprise Agent Platform vs OpenVINO

Compared 10% of the time

Azure OpenAI vs OpenVINO

Compared 7% of the time

Lyzr AI vs OpenVINO

Compared 6% of the time

More OpenVINO Competitors

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