AI Development Tools and Frameworks You Need to Know

The future of AI is here. AI is everywhere, be it home appliances, smartphones, automobiles, solar systems, or sports.

Recent statistics suggest that the global AI market size is forecast to reach $757.58 billion in 2025, giving rise to futuristic AI models at a rapid rate.

In today’s ever-evolving tech landscape, every web and mobile app development project gives the touch of AI to enhance efficiency and capabilities. Being tech-savvy, choosing the right AI framework is a must to give your business ideas a life.

In this article, we will discuss the top popular AI development tools for your next AI business ideas. You will also learn how to choose the right AI framework to make the best of your AI model’s capabilities.

What Are AI Development Tools and Frameworks?

AI frameworks and tools are the backbone of AI models. They help them learn, adapt, and evolve. These frameworks allow AI development companies to create sophisticated AI software to enhance business operations and go beyond and above human capabilities.

They are a collection of libraries to develop and implement AI algorithms. There are two types of AI frameworks:

• Open-source AI frameworks
• Proprietary AI frameworks

Types of AI Frameworks

Open-source AI Frameworks:

As the name suggests, open-source AI frameworks are free software for the public to use. Any developer from any part of the world has access to source code, model architecture, and training datasets. As a result, developers access a collaborative environment to build new AI models, share ideas, and take innovations to a whole new level.

Examples: TensorFlow, PyTorch, Scikit-learn, Hugging Face, OpenCV, etc.

Proprietary AI Frameworks:

The proprietary AI frameworks, however, require commercial licenses. Developers and businesses need to purchase the license to use them due to their restricted access. These frameworks provide excellent performance, unique capabilities, and advanced functionalities with limited customization abilities, making them best suitable for large-scale projects.

Examples: IBM Watson, Microsoft CNTK, Microsoft Azure AI, etc.

How to Choose the Right AI Development Framework?

The right AI framework can effectively ease the AI model development. Look at the following key factors to consider before selecting the framework and starting your next AI app development.

Project Requirements

What is your project goal? Do you wish to develop a chatbot? Are you planning to develop an image generation tool or something else? Will your AI model be multi-functional? Answer these questions, and you will know which tool is the best for your project.

For example, TensorFlow provides ease of customization, and Scikit-learn offers simplicity, saving developers’ time and effort. However, if your project goal is to provide high performance, select a proprietary AI framework.

You can also discuss your project with your AI development company and get the best guidance from industry experts.

Performance

If high performance is the requirement, proprietary AI frameworks trump open-source. However, for proprietary AI frameworks, AI development companies need to invest heavily to gain licenses and access to advanced functions.

Meanwhile, the open-source AI frameworks are free and offer vast community support, flexibility, transparency, and scalability with optimal performance.

For example, Stable Diffusion is a generative AI model developed by Stability AI for video and animation generation. It is a high-performing AI model developed using an open-source AI framework.

Complexity

Some AI frameworks—both open and proprietary—are simple to learn, while others require considerable time to master. Some AI frameworks provide excellent flexibility with other technologies and can handle complex computing. It also depends on the project complexity and other algorithms used.

For example, the LiteRT or TensorFlow Lite library is an excellent choice for deploying machine learning models on mobile devices.

Scalability

With the rise of web and mobile software development, scalability can become an issue in the future if not considered. A right AI framework with excellent community support, robust API support, excellent cross-platform compatibility, etc., can help upscale the project’s capabilities in the future.

For example, Apache Mahout and Microsoft CNTK are two of the most preferred AI frameworks to choose for scalability, data processing, and accuracy.

Existing Tech Stack

You must also consider which AI frameworks work best with the existing tech stack for AI model development. Nowadays, most AI development companies opt for a framework that works on both CPU and GPU.

These factors can help you choose the right AI framework and leave no room for any confusion. You can also discuss with your web and mobile app development agency for the best guidance in the field.

Now, we will discuss the top 10 AI development tools you must watch out for in 2025.

Read on to learn about the best AI app development company for iOS and Android

Top 10 AI Frameworks to Consider in 2025

Though there is no shortage of AI frameworks in the tech industry, still only a few notable are able to grab your attention. Here, we will look at the top 10 most efficient AI frameworks for your next AI model development.

1. TensorFlow

The Google Brain team is the mastermind behind the most successful AI development framework, TensorFlow. This AI framework is an open-source AI development framework that supports deep learning, machine learning, and neural networks. It is widely used by Google, Netflix, PayPal, Sophia Genetics, Novo Nordisk, Here technologies, Veritone, Vail systems, etc.

Developers can get access to its comprehensive documentation, tutorials, and vast community when building and developing AI models. TensorFlow is highly compatible with CPU, GPU (Graphic Processing Unit), and mobile devices. It is widely known for its high adaptability, scalability, complex numerical computation capabilities, interactive user interface (UI), and more.

2. PyTorch

PyTorch is another open-source machine learning library widely used for research, scientific, and numerical operations. It was developed by Meta AI. It has a rich ecosystem of online support and libraries, ensuring precise development in computer vision and natural language processing.

Most cloud platforms support it, including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, providing smooth development and high scalability. A few organizations that use the PyTorch ML framework include Stanford University, Salesforce, and Amazon Advertising.

3. Microsoft Cognitive Toolkit (CNTK)

Microsoft CNTK is also a powerful open-source deep learning toolkit used to develop machine learning prediction models. It can effectively build and train deep neural networks in C++, C#, Python, and Java. This machine learning framework is highly compatible with other machine learning frameworks, such as TensorFlow and PyTorch.

Microsoft CNTK is preferred for enterprise-level applications due to its high computational power, scalability, high performance, and ease of integration. It also enables developers and researchers to develop and work with sophisticated large-scale models.

4. Caffe

When modularity, expression, and speed are in the talks, Caffe comes to mind first. This open-source deep learning framework was developed by Berkeley Vision and Learning Center in 2017. It is best known for high speed, especially on GPUs for image processing.

It is widely used for research projects, startups, and large-scale industrial applications in the fields of speech, multimedia, and vision. However, when it comes to non-visual tasks and applications, it offers limited flexibility. Caffe works well with convolutional neural networks but is ineffective for recurrent neural networks.

5. Keras

It is an open-source library providing a Python interface for artificial neural networks. It’s API can easily integrate with Jax, PyTorch, TensorFlow, and OpenVINO, simplifying development workflow and enhancing debuggability.

Due to its clear and readable structure, developers find it easier to code and develop an easily maintainable and uncomplicated codebase. It’s safe to say that Keras is an excellent tool for advanced research in neural networks, including sentiment analysis, text classification, recurrent neural networks, language translation, etc.

6. LangChain

The LangChain library, developed by Harrison Chase, is widely used in prompt engineering to develop data-responsive applications. It enables developers to debug, collaborate, test, and manage LLM apps in LangSmith. This MIT-licensed AI development framework is free to use.

LangChain offers flexibility, scalability, vase community support, and an extensive library of 600+ pluggable integrations. The real-world use cases of LangChain include synthetic data generation, conversational AI model development, document analysis and summarization, developing memory capabilities in AI systems, etc.

7. CrewAI

CrewAI is the most promising multi-agent platform, supporting multiple Large Language Models (LLMs), including Gemini, Claude, Mistral’s AI, and OpenAI’s GPT. Developers can use cloud services to access it or deploy crew.ai on their system for flexible and scalable operations.

The CrewAI community has built various applications with this framework. Top CrewAI examples are marketing strategy, stock market analysis, AI-driven cloud solutions, task automation, data enrichment, automated financial reporting, etc.

8. LlamaIndex

LlamaIndex is an open-source orchestration framework for LLM applications. It enables data integration from various data sources, embeds them as vectors, and stores that vectorized data in a vector database.

The LlamaIndex-based applications present solutions for generative AI needs and integrate private and public data. As a result, it provides tools with data ingestion, indexing, and queuing. LlamaIndex provides hundreds of data loaders to connect custom data sources to LLMs, including pre-built solutions such as Airtable, Jira, Salesforce, etc.

9. OpenCV

OpenCV is free and open-source software under the Apache license. It was originally developed by Intel in June 2000. This AI development framework provides a real-time optimized computer vision library, hardware, and tools.

OpenCV is highly suitable for developing cross-platform applications on Windows, Linux, Android, iOS, etc. It supports vision-based application development due to its powerful image processing capabilities. Computer vision tools play a significant role in surveillance, robotics, and medical image diagnosis.

For example, modern smartphones with face recognition functionalities ensure robust security. They enable features like face recognition, access control, and payment authentication.

10. Theano

Theano is widely popular among developers and researchers for its high computational power. Developers use this open-source Python library for evaluating and analyzing mathematical expressions, specifically for equations containing multi-dimensional arrays. It’s built on the NumPy—another Python library used for large and multi-dimensional arrays and matrices.

Since its official release in 2007, it has been widely used to compute mathematical equations and has become popular for its high accuracy ratio. Hence, if you wish to develop an AI model showcasing precision and to calculate complex matrix-valued expressions, Theano is the best choice.

We’ve covered top AI frameworks demonstrating specific qualities in different areas, including deep learning, machine learning, computer vision, LLMs, NLPs, educational purposes, research, etc. You can now choose the right AI development framework and ensure your project’s success.

Know what your AI project needs the most in terms of flexibility, scalability, precision, accuracy, performance, versatile functionality, and efficiency. Being a business owner, you need to select budget-friendly AI development services so you can have access to required AI tools and seasoned developers.

Real-World Applications and Future Trends

Let’s see how AI plays a pivotal role in various industries and its impact on day-to-day functions.

1. AI in Manufacturing

AI has been transforming the manufacturing industry by encouraging more sustainable and adaptive practices. It helps businesses streamline the production process, take up repetitive tasks to improve labor efficiency, automate planning and scheduling tasks, and more. As a result, businesses can reduce additional costs, minimize errors, and optimize workflow.

2. AI in Finance

The fintech industry has always been the first to embrace new technology. This industry has coexisted with the tech industry, whether it’s AI, bitcoin, blockchain, or machine learning.

A recent study showcases that the market share for AI in the fintech industry is $42.83 billion worldwide. Moreover, the generative AI market share alone makes up about $1.39 billion, displaying remarkable growth.

AI applications in the fintech industry include personalized wealth management, risk assessments, fraud detection, routine task automation, etc.

3. AI in Marketing

Marketers use AI for several reasons, such as better content research, uncovering crucial insights, faster decision-making, quick content creation, and more.

A study shows that about 32% of marketing organizations have fully implemented AI, while about 43% of marketing organizations have been experimenting with it. It is safe to say that AI has a stronghold in the marketing sector, bringing transformative changes.

4. AI in Sports and Gaming

AI has transformed the sports industry, be it online gaming or streamlining the live telecast. The global AI market, which was just $2.2 billion in 2022, is expected to reach $29.7 billion by 2032.

AI software can predict a player’s performance, contribute to enhancing sports equipment, and improve reporting. AI models also help improve fan engagement and give more transparent decisions.

Undoubtedly, AI will become an integral part of the sports industry in the future, playing a crucial role in decision-making, navigating performance metrics, and more.

5. AI in Automobiles

AI has contributed to the rapid growth of autonomous vehicle technology. The study suggests that the automotive artificial intelligence industry market is expected to reach $7 billion by 2027.

Automobile companies have introduced more advanced features with the help of AI, including advanced driver assistance systems that give rise to safety, reduce driver fatigue, and avoid accidents.

6. AI in Data Security

New AI applications bring convenience and comfort, but there is always a risk of people’s data falling into the wrong hands. Many organizations have already started witnessing the rise of AI-generated bot attacks, credential theft, and many more.

To counter these attacks, many cybersecurity companies are taking advantage of AI to fight these crimes. According to statistics, about 70% of cybersecurity professionals use AI to detect threats, and about 67% use AI to create security rules reflecting patterns and indicators.

Besides, AI is making a lasting impact in other sectors, such as healthcare, logistics, retail, research, etc., with better data analysis and effective customer services.

Conclusion

Choosing the right AI development framework is a pivotal decision. It sets a strong foundation and shapes the future of your business. We have covered the top 10 AI frameworks and tools that can help you choose the right one for your next project.

As a business owner, if you are looking to develop next-gen AI models, hire a trusted web and mobile app development company. You can share your unique AI business ideas, requirements, and project goals to choose the best AI development tools. Having said that, you can get access to a one-on-one session with industry experts and make informed decisions.

Frequently Asked Questions