Azure OpenAI vs TensorFlow comparison

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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: March 2025).

Azure OpenAI vs. TensorFlow

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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

8.0

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 March 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 13.9%, down from 20.9% compared to the previous year. The mindshare of TensorFlow is 3.8%, down from 7.8% 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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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

"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 high precision of information extraction is the most valuable feature."

"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."

"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."

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

"The product saves a lot of time."

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

"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."

More Azure OpenAI pros

"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."

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

"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."

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

"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."

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

"I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that."

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

More TensorFlow pros

Cons

"We are awaiting the new updates like multi-model capabilities."

"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."

"There is room for improvement in their support services."

"The product must improve its dashboards."

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

"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."

"I noticed there are no instructional videos or guides on the network portal for initial configurations. There is limited information available, and this is a concern for me. I would like to see more resources and guides to address these issues."

"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."

More Azure OpenAI cons

"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."

"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."

"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."

"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."

"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."

"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."

"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."

More TensorFlow cons

Pricing and Cost Advice

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

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

"The cost is quite high and fixed."

"The solution's pricing is normal worldwide but expensive in Turkey because Turkey's currency is different."

"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."

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

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

"According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution."

More Azure OpenAI pricing and cost advice

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

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

"The solution is free."

"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."

"We are using the free version."

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

"TensorFlow is free."

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

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

Educational Organization

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?

The pricing is very good for handling various kinds of jobs. While small jobs are manageable, more complex jobs require a higher model, which is a bit challenging.

What needs improvement with Azure OpenAI?

Maybe with the next release, the response will be more precise and more human-like.

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 22% of the time

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Compared 21% of the time

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Compared 17% of the time

PyTorch vs TensorFlow

Compared 11% of the time

IBM Watson Machine Learning vs TensorFlow

Compared 4% of the time

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Product Reports

Azure OpenAI

Download Azure OpenAI product report

Download TensorFlow product report

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

Azure OpenAI vs. TensorFlow