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: December 2024).

Azure OpenAI vs. TensorFlow

December 2024

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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 January 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 17.5%, down from 19.2% compared to the previous year. The mindshare of TensorFlow is 4.5%, down from 8.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Anup Karade

Associate Vice President at LTIMindtree

Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions

We have created our own platform called CanvasAI. It provides machine-learning plumbing and integration with your services. So, we've integrated Azure OpenAI through Canvas. We're also looking at some hard interface modeling from AWS as well. We access Azure OpenAI solutions for our business workflow via the platform, not directly from the service provider. There are security considerations we're working through with the security team, etc. Canvas provides the control plane, which handles RBAC for user registration and model management. For internal processes, we're using OpenAI to create partner call reports, like summarizing quarterly zip code data and other financial models. We also use it for legal and contract stuff, like comparing SOWs and contracts. For user experience, we've created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions.

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

"Its ability to understand and respond well to queries, including language translation for clients, is beneficial."

"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."

"Azure OpenAI is easy to use because the endpoints are created, and we just need to pass our parameters and info."

"The most valuable features include analyzing comments and preparing requests for customers, making emails easier and faster."

"The most valuable feature is the ALM."

"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."

"Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."

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

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 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."

"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."

"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."

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

"The available documentation is extensive and helpful."

"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."

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

More TensorFlow pros

Cons

"The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. When solving complex issues, the process of interacting with Microsoft can be quite time-consuming."

"One area for improvement is providing more flexibility in configuration and connectivity with external tools."

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

"In the next release, they could enhance the product's features for even greater usability and efficiency."

"I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability."

"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."

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

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

More Azure OpenAI cons

"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."

"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."

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

"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."

"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."

"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."

"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."

More TensorFlow cons

Pricing and Cost Advice

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

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

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

"The tool costs around 20 dollars a month."

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

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

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

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

More Azure OpenAI pricing and cost advice

"The solution is free."

"TensorFlow is free."

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

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

"We are using the free version."

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

"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

14%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

Manufacturing Company

14%

Computer Software Company

12%

University

10%

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 9% of the time

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Compared 2% of the time

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Compared 24% of the time

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Compared 22% of the time

OpenVINO vs TensorFlow

Compared 20% of the time

Hugging Face vs TensorFlow

Compared 9% of the time

PyTorch vs TensorFlow

Compared 7% of the time

More TensorFlow Competitors

Product Reports

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Azure OpenAI

January 2025

Download TensorFlow product report

December 2024

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Microsoft

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.

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