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: June 2025).

Azure OpenAI vs. TensorFlow

June 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 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 TensorFlow is 3.9%, down from 6.3% 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 product's initial setup phase was pretty easy."

"You just have to write accurate prompts according to your requirements, and the solution gives very good results."

"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."

"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."

"The most valuable feature is the ALM."

"We have many use cases for the solution, such as digitalizing records, a chatbot looking at records, and being able to use generative AI on them."

"OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions."

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

More Azure OpenAI pros

"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."

"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 improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."

"It is easy to use and learn."

"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."

"The available documentation is extensive and helpful."

"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."

"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."

More TensorFlow pros

Cons

"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."

"One major drawback of Azure OpenAI is its availability, as it's not consistently accessible for effective use."

"The solution needs to accommodate smaller companies."

"The solution's response is a bit slow sometimes."

"Azure needs to work on its own model development and improve the integration of voice-to-text services, particularly for right-to-left languages such as Arabic and Urdu. The accuracy in these languages requires improvement."

"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."

"The product must improve its dashboards."

"The dialogue manager needs to be improved."

More Azure OpenAI cons

"TensorFlow Lite only outputs to C."

"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."

"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."

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

"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."

"The solution is hard to integrate with the GPUs."

"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."

"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."

More TensorFlow cons

Pricing and Cost Advice

"Cost-wise, the product's price is a bit on the higher side."

"We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution."

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

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

"The pricing is acceptable, and it's delivering good value for the results and outcomes we need."

"The cost is quite high and fixed."

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

"The platform offers a flexible pricing model which depends on the features and capabilities we utilize."

More Azure OpenAI pricing and cost advice

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

"The solution is free."

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

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

"It is an open-source solution, so anyone can use it free of charge."

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

"TensorFlow is free."

"We are using the free version."

More TensorFlow pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

Manufacturing Company

15%

Computer Software Company

12%

Financial Services Firm

University

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 13% of the time

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Product Reports

Download Azure OpenAI product report

Azure OpenAI

June 2025

Download TensorFlow 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

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