OpenVINO Reviews

Name: OpenVINO
Brand: Intel
Rating: 4.1 (5 reviews)

Vendor: Intel

4.1 out of 5

5 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 Google Vertex AI: OpenVINO vs Google Vertex AI. OpenVINO is popular among the large enterprise segment, accounting for 74% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a manufacturing company, accounting for 42% of all views.

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Featured OpenVINO reviews

Mahender Reddy Pokala

AI Developer at University of Chicago

I found OpenVINO ( /products/openvino-reviews )'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.

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Dmitrii Sergeichuk

Computer Vision Engineer at Ivideon

I am a computer vision developer who uses OpenVINO to build video analytics systems for commercial purposes The runtime of OpenVINO is highly valuable for running different computer vision models. I use it primarily for this purpose. Additionally, I sometimes use the quantizer to make the models…

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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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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

PeerResearch reports based on OpenVINO reviews

Type	Title	Date
Category	AI Development Platforms	Apr 25, 2025	Download
Product	Reviews, tips, and advice from real users	Apr 25, 2025	Download
Comparison	OpenVINO vs Azure OpenAI	Apr 25, 2025	Download
Comparison	OpenVINO vs Google Vertex AI	Apr 25, 2025	Download
Comparison	OpenVINO vs Microsoft Azure Machine Learning Studio	Apr 25, 2025	Download

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

Valuable Features

OpenVINO offers features for model comparison, testing, and deployment. Its inferencing capabilities are cost-effective compared to Jetson devices. Streaming from camera inputs is crucial, especially with Movidius chipsets. Simple integration and open-source nature are highlighted. Users appreciate converting custom models for direct device execution, aided by the Model Zoo. Its runtime is valuable for computer vision models, with quantization for speed. Cross-platform support, including MacOS, is significant.

"The runtime of OpenVINO is highly valuable for running different computer vision models."
"Intel's support team is very good."
"The initial setup is quite simple."

Room for Improvement

OpenVINO faces challenges with complex models requiring custom layers, slow model optimization, and limited vehicle recognition capabilities. Latency improvements and greater machine learning model integration would enhance its functionality. Users find OpenVINO too dependent on Intel processors, highlighting a need for broader hardware compatibility. Speedier model conversions and improved support for Apple silicon are also sought after, with comparisons to PyTorch's wider model hub selection.

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

Pricing

OpenVINO is open-source and available for free download, with no licensing fees required. Users praise its accessibility on Intel's website, where solutions and inference engines are easily obtained at no cost. Although the software itself incurs no fees, users highlight that some related hardware, such as edge devices like cameras, can be expensive, around $150. This makes it important for enterprises to weigh hardware costs within their budgeting.

"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

OpenVINO users primarily employ it for edge processing in machine vision, leveraging Intel VPU and Movidius chipsets for IoT. Many implement quantization in cameras for surveillance analysis and deploy models on platforms like PC boards for retail detection or video analytics. While budget-friendly, OpenVINO demands trade-offs between accuracy and speed, often necessitating heavy hardware. Its sandbox environment allows testing for optimal hardware, guiding decisions based on preferences and cost considerations.

Deployment

Some users found OpenVINO's setup time-consuming due to multiple steps and errors, with installation taking hours and requiring retries. Others found it straightforward, especially when it was pre-integrated with hardware. Technical expertise eased the process, particularly on Ubuntu, while Windows required more steps. OpenVINO's documentation aided in speeding up the setup.

Scalability

OpenVINO's scalability presents challenges, particularly in managing numerous containers and connecting multiple streams or devices. Some report difficulties with scaling effectively, while others find it scalable and highly rated for use on powerful servers, especially with CPUs. Insights from multiple teams indicate mixed experiences; some express satisfaction with its capabilities, while others seek to enhance computing power by building multiple configurations. Its compatibility with Intel devices is consistently recognized.

Stability

OpenVINO is reliable for many users, with no bugs or crashes reported. While some encounter issues in production environments due to overloads or memory errors, Intel's team aids in resolutions. Model conversion can present challenges, but updates enhance stability, especially on x86 processors. Installation and usage could be clearer, and there are limitations in scalability when adding more cameras. It remains stable and continues to improve with fast-growing features and fixes for existing concerns.

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

Download our OpenVINO Buyer's Guide for additional reliable information.