Azure OpenAI vs PyTorch comparison

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Azure OpenAI

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Executive SummaryUpdated on Dec 4, 2024

PyTorch and Azure OpenAI compete in the AI services category. PyTorch is preferred for research and development due to its flexible usage, while Azure OpenAI is favored for enterprise AI applications owing to its extensive integrations.

Features: PyTorch is known for its dynamic computation graphs, strong community support, and ease in custom model building. Azure OpenAI is recognized for seamless integration with Azure services, strong data security, and customizable machine learning models.

Room for Improvement: PyTorch could improve in offering broader deployment capabilities without relying on third-party tools, enhancing its enterprise integration, and providing more extensive official support. Azure OpenAI has room to expand the pricing options for smaller enterprises, offer more localized support, and refine the user experience in its cloud service portal.

Ease of Deployment and Customer Service: PyTorch offers straightforward deployment for local environments but often requires additional tools for comprehensive cloud deployment. Azure OpenAI benefits from Microsoft's rich support and detailed documentation, making its deployment process within the Azure ecosystem comprehensive and user-friendly.

Pricing and ROI: PyTorch, being open-source, is cost-effective for startups and research with no licensing fees. Its ROI relies heavily on internal resources. Azure OpenAI incurs service costs but offers ROI through its scalability and robust features suitable for larger businesses requiring strong AI capabilities.

To learn more, read our detailed Azure OpenAI vs. PyTorch Report (Updated: June 2025).

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Azure OpenAI vs. PyTorch

June 2025

Helped 856,873 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

Azure OpenAI

Ranking in AI Development Platforms

1st

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

PyTorch

Ranking in AI Development Platforms

7th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 11.6%, down from 20.5% compared to the previous year. The mindshare of PyTorch is 1.6%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Viswanath Barenkala

Associate Vice President at State Street

Offers tools to moderate generated content and guidance to safely design applications, but it is not consistently accessible

Instead of a feature, the GPT-4 model has been most beneficial for automating tasks. We transitioned from GPT-3.5 to GPT-4 and actively use it. However, we face limitations due to geographic availability, subscription constraints, and rate limiting, which we are currently negotiating and working towards optimizing. While we haven't formally benchmarked Azure OpenAI's language understanding against industry standards, we find it performs well about 70-80% of the time. Occasionally, we need to refine our queries and adapt our systems accordingly to improve accuracy and effectiveness.

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Rohan Sharma

AI/ML Co-Lead at Developer Student Clubs - GGV

Enabled creation of innovative projects through developer-friendly features

The aspect I like most about PyTorch is that it is really developer-friendly. Developers can constantly create new things, and everyone around the world can use it for free because it's an open-source product. What I personally like is that PyTorch has enabled users to use Apple's M1 chip natively for GPU users. Unlike other libraries using CUDA, PyTorch utilizes Metal Performance Shaders (MPS) to enable GPU usage on M1 chips.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Two aspects I appreciate are the turnaround time and ease of use. As it's a managed service, the quick turnaround is beneficial, and the simple interface makes it easy to work with. Performance and scalability are also strong points since you can scale as needed."

"It is easy to integrate and develop a solution. Most customers are concerned about the security of their data and how cost-effective it is. We have developed some methodologies so that our customers will not be charged too much for these OpenAI services but will still get the same kind of performance and results. It's all developed on Azure, so customers also see its benefit."

"My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context."

"Its versatility makes it incredibly useful for technical problem-solving, content creation, data analytics, and more."

"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."

"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."

"The most valuable feature is the ALM."

"Azure's integration with OpenAI's GPT is beneficial as well, especially for text generation tasks."

More Azure OpenAI pros

"The framework of the solution is valuable."

"We use PyTorch libraries, which are working well. It's very easy."

"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."

"It's been pretty scalable in terms of using multiple GPUs."

"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."

"PyTorch is developer-friendly, allowing developers to continuously create new projects."

"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."

"The tool is very user-friendly."

More PyTorch pros

Cons

"The UI could be a little easier."

"The product must improve its dashboards."

"Latency performance is a major part. And I'm seeking support for multiple models that handle text, images, videos, and voice. I'm from India, and I'm looking for more support in Indian languages. There are 18 official languages and many more unofficial. We need support for these languages, especially in voice moderation, which is not yet available."

"Our customers are worried about data management, ethical, and security issues."

"Azure could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements."

"Deployment was slightly complex for me to understand."

"The dialogue manager needs to be improved."

"Sometimes, the responses are repetitive."

More Azure OpenAI cons

"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."

"I do not have any complaints."

"PyTorch needs improvement in working on ARM-based chips. They have unified memory for GPU and RAM, however, current GPUs used for processing are slow."

"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."

"PyTorch needs improvement in working on ARM-based chips. They have unified memory for GPU and RAM, however, current GPUs used for processing are slow."

"I would like to see better learning documents."

"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."

"The training of the models could be faster."

More PyTorch cons

Pricing and Cost Advice

"The cost is pretty high. Even by US standards, you would find it high."

"The tool costs around 20 dollars a month."

"I'm uncertain about the licensing, specifically the pricing. This falls under the purview of other teams, particularly the sales teams. I am not informed about the pricing details."

"It's a token-based system, so you pay per token used by the model."

"I rate the product pricing six out of ten."

"Regarding pricing and licensing, it's a bit complex due to the minimum purchase requirement for PTO units. We're evaluating the best approach between PTE and pay-as-you-go models. Our organization is cautious about committing to PTE due to the fixed bandwidth reservation, while pay-as-you-go doesn't offer enough flexibility. We're discussing these matters with legal teams to ensure compliance and data security."

"The licensing is interaction-based, meaning transactional. It's reasonably priced for now."

"Azure OpenAI is a bit more expensive than other services."

More Azure OpenAI pricing and cost advice

"PyTorch is an open-source solution."

"PyTorch is open source."

"It is free."

"PyTorch is open-sourced."

"It is free."

"The solution is affordable."

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856,873 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

14%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

Manufacturing Company

30%

Financial Services Firm

University

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Azure OpenAI?

The product is easy to integrate with our IT workflow.

What is your experience regarding pricing and costs for Azure OpenAI?

In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user ...

What needs improvement with Azure OpenAI?

Azure ( /products/microsoft-azure-reviews ) could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements....

What do you like most about PyTorch?

The tool is very user-friendly.

What is your experience regarding pricing and costs for PyTorch?

I haven't gone for a paid plan yet. I've just been using the free trial or open-source version.

What needs improvement with PyTorch?

PyTorch needs improvement in working on ARM-based chips. Although they have unified memory for GPU and RAM, they are unable to utilize these GPUs for processing efficiently. They take so much time....

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Compared 11% of the time

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Compared 8% of the time

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Compared 6% of the time

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Compared 45% of the time

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Compared 18% of the time

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Compared 15% of the time

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Compared 11% of the time

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Compared 5% of the time

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Product Reports

Download Azure OpenAI product report

Azure OpenAI

June 2025

Download PyTorch product report

May 2025

Overview

Azure OpenAI integrates advanced language models with robust security for precise information extraction and task automation. Its seamless Azure integration and drag-and-drop interface simplify implementation and enhance accessibility.

Azure OpenAI offers a comprehensive suite of features designed for efficient data processing and task automation. It provides high precision in extracting information and strong conversational capabilities, crucial for developing chatbots and customer support systems. Its integration with Azure ensures seamless data handling and security, addressing key enterprise requirements. Users can employ its versatile GPT models for diverse applications such as predictive analytics, summarizing large documents, and competitive benchmarking. Despite its strengths, it faces challenges like latency, inadequate regional support, and limited integration of new technologies. Improvements in model fine-tuning and more flexible configuration are desired by users.

What features make Azure OpenAI a reliable choice?

High Precision: Ensures accurate data extraction suitable for critical applications.
Conversational Capabilities: Facilitates the creation of sophisticated chatbots.
Azure Integration: Seamlessly connects with Azure services for enhanced functionality.
Automation Support: Automates repetitive tasks, improving efficiency.
Security Features: Offers robust protection for sensitive data.
Drag-and-Drop Environment: Simplifies user interaction and deployment.

What benefits and ROI should users consider?

Natural Language Processing: Provides superior language model performance.
Quick Implementation: Reduces time to deploy applications.
Scalability: Supports growing business needs with ease.
Versatile GPT Models: Applicable across different industry workflows.

Azure OpenAI is implemented across industries like healthcare, finance, and education for tasks like invoice processing, digitalizing records, and language translation. It enhances policy management, document assimilation, and customer support with predictive analytics and keyword extraction. Organizations in such sectors benefit from streamlined workflows and task automation.

Microsoft

We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.

In this course, we'll be covering the concepts behind deep learning and how to build deep learning models using PyTorch. We've included a lot of hands-on exercises so by the end of the course, you'll be defining and training your own state-of-the-art deep learning models.