OpenVINO Reviews

Name: OpenVINO
Brand: Intel
Rating: 4.1 (4 reviews)

Vendor: Intel

4.1 out of 5

4 reviews
100% willing to recommend

What is OpenVINO?

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).

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OpenVINO is the #14 ranked solution in top AI Development Platforms. PeerSpot users give OpenVINO an average rating of 8.2 out of 10. OpenVINO is most commonly compared to Microsoft Azure Machine Learning Studio: OpenVINO vs Microsoft Azure Machine Learning Studio. OpenVINO is popular among the large enterprise segment, accounting for 75% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a manufacturing company, accounting for 44% of all views.

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Featured OpenVINO reviews

reviewer1530384

Systems and Solutions Architect at a tech services company with 1,001-5,000 employees

Generally, when you deploy edge products, it's really about latency. It's about getting that camera input, being able to process it, extracting the information you need, and getting the solution back to the person who made the request. Although I'm not necessarily saying its latency or accuracy is bad, it's always something that can be improved upon. By focusing on improving these areas, they can make the overall solution even better. At this point, the product could probably just use a greater integration with more machine learning model tools. However, that's not advice from experience per se. That's always just helpful in general. To be able to incorporate more models into the product makes it stronger. Therefore, to be clear, it's not coming from a point of a current deficiency. It's just a general comment.

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Zafar Muhammed

Machine Learning Software Developer at freelancer

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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ChristianRivera

Freelance Engineer at Autónomo

Install the latest version that already has fixes for old problems. Work with some neural network, with a few layers to test it. If you use a neural network, like a fast R-CNN, it wouldn't work because it's too complex and there are some layers that are not recognized by OpenVINO. Start small and continue growing. Make an account in the Intel OpenVINO platform so you can start testing with Jupyter Notebook and send your inference jobs to different kinds of devices. Check the performance, know the differences between the different hardware, and how you can site the project. It is a good platform. The price I have seen is not so expensive. That's my advice. OpenVINO is a good tool if you want to work not in the cloud, you have to work on the edge. If you want to adapt your models, it has a good pipeline to follow. You have to learn it well because it could take time to debug some of the errors. Some of them are not very explained and you have to go through your codes a bit blind. I would rate it a nine out of ten because it's a very good tool. It's not complete but is the best in the market right now.

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OpenVINO mindshare

As of April 2025, the mindshare of OpenVINO in the AI Development Platforms category stands at 1.6%, down from 3.0% compared to the previous year, according to calculations based on PeerSpot user engagement data.

AI Development Platforms

PeerAnalyst reports based on OpenVINO reviews

Type	Title	Date
Category	AI Development Platforms	Apr 2, 2025	Download
Product	Reviews, tips, and advice from real users	Apr 2, 2025	Download
Comparison	OpenVINO vs Azure OpenAI	Apr 2, 2025	Download
Comparison	OpenVINO vs Google Vertex AI	Apr 2, 2025	Download
Comparison	OpenVINO vs Microsoft Azure Machine Learning Studio	Apr 2, 2025	Download

Title	Rating	Mindshare	Recommending
Microsoft Azure Machine Learning Studio	3.8	7.3%	93%	61 interviews Add to research
Google Vertex AI	4.2	14.0%	100%	10 interviews Add to research

Valuable Features

OpenVINO's most valuable features include data streaming from camera inputs, compatibility with Movidius chipset, and ease of integration. The capability to convert and run custom models and the inclusion of Model Zoo are beneficial. Features for model comparison, testing, evaluation, and deployment are appreciated. It offers superior inferencing and processing capabilities, being cost-effective compared to Jetson Nano, Jetson TX2, or possibly Jetson Xavier NX. OpenVINO's open-source nature and simple initial setup are significant advantages.

"Intel's support team is very good."
"The initial setup is quite simple."
"The inferencing and processing capabilities are quite beneficial for our requirements."

Room for Improvement

OpenVINO faces challenges with complex model conversion, requiring customization for certain models, which can be difficult. Model optimization speed needs enhancement. Differentiation between vehicle types is inadequate, necessitating custom training. Improving latency and accuracy integration with machine learning tools could strengthen the offering. Greater compatibility beyond Intel chips, supporting diverse hardware platforms, would be advantageous. Incorporating these changes may enhance OpenVINO's capabilities, appealing to a broader range of users and scenarios.

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

Pricing

OpenVINO is open source and free to use, with no associated licensing costs. Enterprises find that while the software itself has no price, costs arise from necessary hardware like edge devices, often priced around $150. Solutions, inference engines, and model optimizations are available for free download, making OpenVINO a cost-effective option for enterprises seeking advanced AI capabilities.

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

Popular Use Cases

Users primarily utilize OpenVINO for machine vision and Edge IoT applications, leveraging Intel VPU and Movidius chipsets. Retail recognition models are transitioned to OpenVINO for object and retail detection. Edge processing and video co-processing are common themes. It supports deploying models on devices like PC boards, balancing accuracy and speed. Users appreciate testing hardware in Intel's online platform to make budget-conscious decisions and optimize application performance, particularly for tasks like fire and human fall detection in surveillance.

Deployment

Some found OpenVINO's initial setup time-consuming due to its complex steps, often requiring multiple attempts and extensive testing, especially when using Docker containers. For those with technical expertise, setup can be quicker; Ubuntu OS was favored for its command-line installation. Conversely, Windows' process was more challenging. Others had an easy experience, especially when it was pre-integrated with their binary image, simplifying the process significantly.

Scalability

Users find scalability with OpenVINO challenging. Managing multiple containers and connecting streams or edge devices is complex. Some engineers appreciate its capabilities, indicating a good experience, while others face difficulties in effectively increasing scale, especially with many devices. Enthusiasm exists for building paths to enhance compute potentials even if scaling isn't seamless. OpenVINO is considered a beneficial tool for Intel devices despite these concerns. Nonetheless, specific applications may require additional effort for effective scaling solutions.

Stability

OpenVINO is stable with a growing number of improvements. Users appreciate the absence of bugs or crashes and find it reliable. However, there are concerns about performance limitations with multiple cameras and occasional issues like overload in production settings. Users find Intel responsive in addressing these challenges, particularly with large model conversions. There is a desire for clearer installation and easier deployment processes to enhance utilization.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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