Azure OpenAI vs TensorFlow comparison

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Azure OpenAI

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Executive SummaryUpdated on Dec 4, 2024

TensorFlow and Azure OpenAI compete in the AI and machine learning category, providing frameworks for model development and deployment. TensorFlow has an advantage in integration and cost-effectiveness, while Azure OpenAI is favored for advanced capabilities.

Features: TensorFlow's open-source nature provides flexibility and adaptability, allowing developers to create custom solutions efficiently. Its vast library support and active community enhance development experiences. Additionally, TensorFlow’s integration with CoLab facilitates easier execution of machine learning tasks on GPUs. Azure OpenAI offers state-of-the-art AI models with seamless Microsoft cloud integration, providing scalability and superior AI functionalities, including deep learning and conversational capabilities.

Room for Improvement: TensorFlow may require enhancements in integration procedures for diverse enterprise environments and simplification of its steep learning curve for new users. Expanding comprehensive tutorials for beginners could further improve accessibility. Additionally, enhancing support for lesser-known languages would broaden usage opportunities. Azure OpenAI could improve its geographic availability and address rate limiting issues with its GPT models. Users also note the need for better subscription packages to optimize usage costs and availability of newer models across more regions. Security and compliance processes should also continue to be streamlined for enterprise adoption.

Ease of Deployment and Customer Service: TensorFlow benefits from extensive community support with detailed documentation and forums aiding deployment processes. User resources are highly collaborative, assisting in faster problem resolution. Azure OpenAI offers robust deployment support via Microsoft’s tailored consultations, providing a structured and guided integration experience within Azure’s ecosystem, which can be highly advantageous for existing Microsoft clients.

Pricing and ROI: TensorFlow’s open-source platform results in reduced initial costs, making it economically advantageous for startups and research applications, suggesting strong ROI potential. While Azure OpenAI has higher associated costs, its comprehensive features and seamless Azure service integration present significant ROI potential through enhanced capabilities and enterprise-level scalability.

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

Azure OpenAI vs. TensorFlow

April 2025

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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

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

TensorFlow

Ranking in AI Development Platforms

6th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 12.0%, down from 20.8% compared to the previous year. The mindshare of TensorFlow is 3.8%, down from 6.7% 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.

Read full review

Ashish Upadhyay

Founder at BlockMosiac

A robust tools for model visualization and debugging with superior scalability and stability, and an intuitive user-friendly interface

The one feature we find most valuable at our company is its robust and flexible machine-learning capabilities. It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions. The ability to develop and fine-tune models, such as risk assessment for detection and market protection, as well as the creation of recommendation systems, is paramount. This versatility extends to providing personalized identity-relevant applications for our enterprise clients, delivering valuable insights to the market. Its exceptional support for deep learning and its efficient resource utilization enable us to undertake complex financial and data analyses. The flexibility it provides is crucial for meeting industrial requirements and crafting solutions tailored to our client's specific needs.

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 document intelligence feature has significantly aided in our operations, facilitating the creation of descriptive content."

"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."

"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."

"The most valuable feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users."

"Generative AI or GenAI seems to be the best part of the solution."

"I would rate it a nine out of ten."

"Azure OpenAI is used as chat services, allowing me to replace human tasks with analytical capabilities."

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

More Azure OpenAI pros

"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."

"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."

"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."

"It's got quite a big community, which is useful."

"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."

"TensorFlow is an efficient product for building neural networks."

"TensorFlow is a framework that makes it really easy to use for deep learning."

"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."

More TensorFlow pros

Cons

"There are no available updates of information that are currently provided."

"Sometimes, the responses are repetitive."

"The solution needs to accommodate smaller companies."

"Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources."

"There are certain shortcomings with the product's scalability and support team where improvements are required."

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

"The product must improve its dashboards."

"Sometimes, it gives answers in English, even when the request is in Polish."

More Azure OpenAI cons

"Personally, I find it to be a bit too much AI-oriented."

"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."

"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."

"We encountered version mismatch errors while using the product."

"Enhancements could include increasing use cases and improving the accuracy of previously built models in TensorFlow. For instance, when we run certain models, the computing power of laptops becomes high."

"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."

"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."

"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."

More TensorFlow cons

Pricing and Cost Advice

"The tool costs around 20 dollars a month."

"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."

"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."

"The cost is quite high and fixed."

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

"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."

"If you consider the long-term aspect of any project, Azure OpenAI is a costly solution."

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

More Azure OpenAI pricing and cost advice

"TensorFlow is free."

"We are using the free version."

"I am using the open-source version of TensorFlow and it is free."

"I did not require a license for this solution. It a free open-source solution."

"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."

"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."

"The solution is free."

"I rate TensorFlow's pricing a five out of ten."

More TensorFlow pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

Manufacturing Company

14%

Computer Software Company

13%

University

Financial Services Firm

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 TensorFlow?

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

What is your experience regarding pricing and costs for TensorFlow?

I am not familiar with the pricing setup cost and licensing.

What needs improvement with TensorFlow?

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libr...

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Compared 2% of the time

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Compared 24% of the time

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Compared 17% of the time

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Compared 16% of the time

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Compared 13% of the time

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Compared 4% of the time

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Product Reports

Download Azure OpenAI product report

Azure OpenAI

May 2025

Download TensorFlow product report

April 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

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

Sample Customers

Information Not Available

Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm