Azure OpenAI vs TensorFlow comparison

Read 19 TensorFlow reviews

3,647 Views
3,647 Comparison Views

100% willing to recommend

Azure OpenAI

Comparison Buyer's Guide

Download the report

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: December 2025).

Azure OpenAI vs. TensorFlow

December 2025

Download the complete report

Helped 881,707 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

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

TensorFlow

Ranking in AI Development Platforms

8th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the AI Development Platforms category, the mindshare of Azure OpenAI is 6.5%, down from 15.0% compared to the previous year. The mindshare of TensorFlow is 5.4%, up from 3.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Azure OpenAI	6.5%
TensorFlow	5.4%
Other	88.1%

AI Development Platforms

Featured Reviews

Rudra Chand

AI Engineering Manager at a tech vendor with 10,001+ employees

Empowerment in regulatory content generation marred by inconsistency and hallucination issues

While it is good, we sometimes encounter hallucination issues, which is a significant concern. We are changing the prompt and fine-tuning it, but we still face some inconsistent behavior. We have specific instructions and keep the temperature very low to avoid overly generative responses, ensuring we receive specific answers from the particular source document without deviation; however, the results can sometimes vary. The main issue with Azure OpenAI is the inconsistency in output. We have a set template instruction, and it should generate within those parameters without any creativity because it's meant for regulatory authoring documents. The business provides the template instructions, and it should generate accordingly. While we have different prompts for various needs, sometimes it generates the correct results, and sometimes it does not, leading to inconsistency. For stability, based on the current model I am using, I would rate Azure OpenAI a 7 due to the ongoing hallucination issues.

Read full review

Ton Jacobs

Owner at Go knowledge

Has good stability, but the process of creating models could be more user-friendly

The platform integrates well with other tools, especially Python, which we use to create models. These models can be deployed on mobile devices, which perfectly suits our requirements. It supports our AI-driven initiatives very well by producing AI models, which is its primary function. I recommend it for those seeking specialized scripting. However, it's important to consider other options as well. It is better suited for specialists in the field and is less user-friendly than general tools like Excel. I rate it overall at six out of ten. While it is a powerful tool, other software options are slightly simpler for training models.

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

"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."

"The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment."

"The product's initial setup phase was pretty easy."

"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."

"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."

"The AI search functionality is particularly effective, as it creates summaries from data."

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

"We can use the solution to implement our tasks and models quickly."

More Azure OpenAI pros

"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."

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

"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."

"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."

"TensorFlow is easy to implement and offers inbuilt functions for various tasks."

"The available documentation is extensive and helpful."

"TensorFlow provides Insights into both data and machine learning strategies."

More TensorFlow pros

Cons

"We encountered challenges related to question understanding."

"I would like to see in the future that Azure OpenAI brings non-OpenAI models into their service because they currently do not have independent models and follow the OpenAI roadmap."

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

"Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer."

"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."

"Sometimes, the responses are repetitive."

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

"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."

More Azure OpenAI cons

"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."

"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."

"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."

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

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

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

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

"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."

More TensorFlow cons

Pricing and Cost Advice

"The cost structure depends on the volume of data processed and the computational resources required."

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

"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 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."

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

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

More Azure OpenAI pricing and cost advice

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

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

"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."

"TensorFlow is free."

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

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

"We are using the free version."

See which vendors are best for you

Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.

See recommendations

881,707 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

11%

Manufacturing Company

10%

Comms Service Provider

Manufacturing Company

15%

Comms Service Provider

University

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	17
Midsize Enterprise	1
Large Enterprise	19

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	3
Large Enterprise	3

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 terms of pricing for Azure OpenAI, I would rate it as average compared to Gemini. Currently, Gemini is becoming increasingly popular, which prompts leadership to consider a switch primarily due ...

What needs improvement with Azure OpenAI?

I have not thought about how Azure OpenAI can be improved. I have not explored AI model customization in Azure OpenAI, but it's not a very common use case to do model customization. There are certa...

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...

What is your primary use case for TensorFlow?

I've used TensorFlow for image classification tasks, object detection tasks, and OCR.

Google Vertex AI vs Azure OpenAI

Comparisons

Compared 33% of the time

Hugging Face vs Azure OpenAI

Compared 10% of the time

Google Cloud AI Platform vs Azure OpenAI

Compared 5% of the time

Amazon SageMaker vs Azure OpenAI

Compared 5% of the time

GroqCloud Platform vs Azure OpenAI

Compared 5% of the time

More Azure OpenAI Competitors

PyTorch vs TensorFlow

Compared 35% of the time

Hugging Face vs TensorFlow

Compared 10% of the time

OpenVINO vs TensorFlow

Compared 10% of the time

Google Vertex AI vs TensorFlow

Compared 7% of the time

Microsoft Azure Machine Learning Studio vs TensorFlow

Compared 6% of the time

More TensorFlow Competitors

Product Reports

Download Azure OpenAI product report

Azure OpenAI

February 2026

Download TensorFlow product report

January 2026

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