Top 10 AI Frameworks You Should Consider in 2025

Ever since its inception, Artificial Intelligence (AI) took no time to become a mainstream technology that we can’t overlook in our daily lives. While some experts are still predicting what can be possible with AI, the technology has started to unfold its potential, especially in the web and mobile app development field. As the technology continues to burgeon, there has been a flood of AI frameworks, libraries, and toolkits helping businesses harness their potential. 

However, the availability of numerous options gives nothing but a state of confusion regarding which one would fit the ideal for the project. As a customer-oriented mobile app development company, we at Mtoag Technologies understand the struggle, so we have decided to make it easier for you.                       

With our years of experience and knowledge of the tech industry, our team has meticulously curated a list of top AI frameworks you can trust for your next project. To make things further easier for you, we have included the key features, advantages, and limitations of each framework. So, let’s get started!

AI in App Development Statistics: Key Facts & Figures

• 47% of businesses have reported using AI and ML in their company development and coding process. (Source: DigitalOcean) 
• 57.6% of businesses have decreased their reliance on StackOverflow since the introduction of AI. (Source: Retool)
• 38.2% reported using OpenAI’s GPT-4 for building AI applications. (Source: Retool) 
• 30.5% of businesses use GitHub Copilot for application design, engineering, and ops. (Source: Retool) 
• 13% of them reported adopting GenAI into their software development. (Source: McKinsey)

What is an AI Framework?

AI frameworks are a set of libraries, tools, and pre-built functions for building advanced software systems that can learn, adapt, and evolve using AI and ML algorithms.     

These advanced frameworks simplify developing and implementing sophisticated AI algorithms with their pre-built libraries and functions. Developers can customize AI models for their specific tasks rather than building them from scratch. 

These AI frameworks standardize the software development process, which means developers always have a consistent set of tools and practices regardless of the AI project. This uniformity helps them integrate AI functionalities into different platforms and applications.    

List of the Best AI Frameworks in 2025

So, here are some of the advanced and trending AI frameworks you can choose for your next development project to increase its efficiency, productivity, and cost-effectiveness.     

1. TensorFlow

TensorFlow tops our list because of the efficiency and advancement it brings to development. It is an open-source platform initially launched for internal Google use in research and production. It was developed by the Google Bain team. The first version of this toolset was launched under Apache License 2.0 in 2015. The framework is compatible with several programming languages, such as JavaScript, Python, Java, and C++. 

Key Features

• The most advanced and updated version, Tensor 2.18.0, was released recently in November 2024. 
• The framework has high computational power and can be used with any CPU or GPU. 
• It uses graph abstraction to build machine models and neural networks with graphs. 

Best For

• TensorFlow is best for developing large-scale ML models with support for both GPUs and CPUs. 
• The framework works seamlessly with a number of programming languages, including JavaScript, Python, Java, and C++. 
• The TensorHub provides exclusive documentation, pre-trained models, and community support. 

Limitations

• The complexity of using the platform makes it less beginner-friendly. 
• For large model development, the framework may require high computational power. 

2. PyTorch

PyTorch is a popular open-source framework known for its adaptability in integrating with various languages and systems. Developed by Facebook’s AI Research lab, it was released in January 2016. PyTorch is primarily used in deep learning, computer vision, and natural language processing applications. Another great feature of PyTorch is its compatibility with both iOS and Android platforms.

Key Features of PyTorch

• The most recent version is PyTorch 2.5.1, released on 26 October 2024.
• PyTorch supports computational graphs during runtime, making it easier to express and evaluate mathematical expressions.
• It uses debugging tools like IPDB and PDB. Developers can also use Python’s IDE PyCharm for debugging, thanks to the computational graph support during runtime.

Best For

• PyTorch is excellent for research because it supports dynamic computational graphs, which are defined during runtime.
• Its compatibility with Python debugging tools like IPDB and PDB makes it very developer-friendly.
• PyTorch integrates seamlessly with other libraries and platforms, including iOS and Android.

Limitations of PyTorch

• PyTorch has traditionally focused more on research rather than deployment, though recent updates are addressing this gap.
• While improving, PyTorch’s documentation and community resources are not as extensive as those of TensorFlow.

3. Theano

Theano is a powerful AI framework built with Python that delivers high precision for networks needing substantial computational power. It was first released in 2007 and is primarily used for manipulating and evaluating mathematical expressions, especially those involving matrices. One of the reasons Theano is so fast is its dynamic code generation, which speeds up the evaluation of these expressions.

Key Features 

• Latest Version: Theano 18.08, updated on 3rd December 2024.
• Theano can use GPUs to execute parts of expressions, making it faster than regular Python code. It can also run on CPUs, offering flexibility.
• Known for its high accuracy, Theano performs well even with very small values, making it a preferred choice for precise calculations.

Best For

• Theano is designed for speed, utilizing GPUs for faster computations. It is highly accurate, even for complex calculations.
• It excels at evaluating complex matrix-valued expressions, making it suitable for tasks requiring high computational power.

Limitations of Theano

• Theano's active development stopped in 2017, which may make it less reliable for long-term projects.
• Theano is not as suitable for modern deep learning architectures and lacks some of the user-friendly features found in newer frameworks.

4. Microsoft CNTK

The Microsoft Cognitive Toolkit, commonly known as CNTK, is a flexible framework designed to support voice remodeling, messaging, and text processing. Released on January 25, 2016, this toolkit is powerful for both low-level and high-level network expressions, ensuring efficient use of resources for optimum performance. Compatible with both Python and C++, CNTK can work with various servers simultaneously, making processes quicker and more efficient.

Key Features 

• The most recent version is Microsoft CNTK 2.7, released on April 26, 2019.
• CNTK supports advanced neural networks such as GANs (Generative Adversarial Networks), CNNs (Convolutional Neural Networks), and RNNs (Recurrent Neural Networks). It is optimized for accuracy, scalability, and high-level integration.
• It provides faster evaluation of machine learning models, offering a scalable environment for large-scale computations.

Best For

• Ideal for complex AI models, CNTK supports sophisticated networks like GANs, CNNs, and RNNs.
• Designed for distributed computing, CNTK is fast and scalable, making it suitable for handling large datasets efficiently.

Limitations of Microsoft CNTK

• Setting up Microsoft CNTK can be challenging, especially in non-Windows environments.
• CNTK has fewer community resources and support than more popular frameworks like TensorFlow.

5. Scikit-learn

Scikit-learn is a powerful Python-based framework that offers a variety of supervised and unsupervised algorithms. It is widely used for evaluating the accuracy of supervised models on new data and extracting features from images and text.

Key Features 

• The latest version is scikit-learn 1.6.0, released in December 2024.
• It allows for the reduction of attributes in data, helping with feature selection and summarization.
• Scikit-learn offers many supervised learning algorithms, such as Support Vector Machines and Decision Trees.
• It also provides unsupervised learning algorithms, including cluster analysis, factoring, and unsupervised neural networks.

Best For

• Scikit-learn is excellent for implementing a wide range of machine learning algorithms, both supervised and unsupervised.
• It simplifies tasks like feature extraction, dimensionality reduction, and data preprocessing, making it easier to work with large datasets.

Limitations of Scikit-learn

• Scikit-learn focuses mainly on machine learning rather than deep learning, which limits its use for neural networks.
• It is not optimized for GPU usage, which can make it less efficient for very large datasets.

6. Apache Mahout

Apache Mahout is a widely used open-source framework designed for scalable data analysis. Launched in 2007, it excels at processing large volumes of data efficiently. Mahout leverages distributed computing capabilities, making it ideal for evolutionary programming. It is developed in Java and Scala, providing robust performance for complex data tasks.

Key Features 

• The most recent version is Apache Mahout 14.1, released in October 2020.
• Mahout offers a ready-to-use framework that simplifies data mining on large datasets, making it very developer-friendly.
• Using Scala DSL, Mahout allows for highly expressive mathematical computations, enabling a broad range of ideas and implementations.

Best For

• Mahout is perfect for large-scale data mining and machine learning tasks, especially when integrated with Apache Hadoop.
• With support for both Java and Scala, Mahout is well-suited for big data applications running on the Java Virtual Machine (JVM).

Limitations of Apache Mahout

• Compared to more popular frameworks, Mahout has a smaller developer community, which may affect long-term support and development.
• Setting up and configuring Mahout can be challenging, especially for those not familiar with Java and Hadoop.

7. Amazon Machine Learning

Amazon Machine Learning (AML) is a relatively new entrant in the AI space but has quickly gained popularity due to its user-friendly features. Initially released on November 29, 2017, AML focuses on fundamental operations like data analysis, model training, and evaluation. Its customized tools cater to users of all experience levels, making it an excellent option for developers and analysts alike.

Key Features

• The most recent version is 14.1, released in October 2019.
• AML is popular among developers, data scientists, and machine learning researchers. It helps discover patterns in user data through algorithms and builds mathematical models based on these patterns.
• The framework is more abstract, which may reduce versatility but simplifies the process for users.
• The AI-powered framework and APIs require less coding. This allows business analysts to develop ML models and make accurate predictions without needing extensive ML expertise.

Best For

• AML is designed for those with limited machine learning expertise, offering easy-to-use tools and automated features.
• It integrates seamlessly with other AWS services, making it a convenient choice for applications already using the AWS ecosystem.

Limitations of Amazon Machine Learning

• The abstracted nature of AML can limit flexibility, making it harder to customize or build complex models.
• AWS services can be expensive, particularly for high-volume use, which might be a barrier for smaller teams.

8. PaddlePaddle

PaddlePaddle, also known simply as Paddle, is an AI framework developed by Baidu that's gaining traction for its versatility and robust toolset. It's designed to be useful for both research and practical applications, offering a range of pre-made models and tools for tasks like language understanding and image processing.

Key Features 

• The most recent version is PaddlePaddle 2.9.1, released in October 2024.
• PaddlePaddle is designed to be accessible to developers of all experience levels, making it user-friendly.
• This framework supports the entire AI development process, from data processing to model training and deployment.
• Although the community is still expanding, PaddlePaddle's continuous growth and development make it an appealing choice.

Best For

• PaddlePaddle includes pre-built models, making it suitable for a wide range of applications, both in research and real-world scenarios.
• The framework simplifies the AI development pipeline, providing tools for data processing, model training, and deployment.

Limitations of PaddlePaddle

• While expanding, PaddlePaddle's community is still smaller compared to other frameworks like TensorFlow or PyTorch, which can affect the availability of resources and support.
• The documentation for PaddlePaddle is less comprehensive, which could make it more challenging for users to learn and implement projects.

9. Jax

Jax is a versatile and high-performance framework developed by Google Research. It's designed to excel in tasks that require intensive numerical computations. This makes it especially valuable for scientific research, machine learning experiments, and neural network development. Despite having a learning curve, Jax is appreciated for its ability to balance flexibility and speed, making it a favored choice for Python developers.

Key Features 

• The most recent version is Jax 0.4.38, released in December 2024.
• Jax is recognized for its flexible and robust support for high-performance numerical computing, allowing users to handle complex calculations efficiently.
• It is particularly well-suited for scientific tasks, machine learning research, and neural network experimentation.
• While it may take some time to get accustomed to, Jax is becoming popular due to its unique ability to offer both flexibility and high performance.

Best For

• Jax is ideal for advanced mathematical tasks and scientific research, thanks to its high-speed computations.
• Its compatibility with Python makes it accessible and easy to use for developers who are familiar with Python.

Limitations of Jax

• Jax requires some time to master, which can be challenging for those new to high-performance computing.
• It is primarily used for research rather than production, which may limit its scalability for long-term deployed applications.

10. Caffe

Caffe is a deep learning framework renowned for its clarity, speed, and scalability. Developed by the Berkeley Vision and Learning Center (BVLC) with community contributions, Caffe has been a popular choice for various deep-learning applications. Notably, Google’s DeepDream utilizes the Caffe framework.

Key Features 

• The most recent version of Caffe is 1.0, released in 2017.
• Caffe’s core is written in C++, but it also provides a Python interface, making it flexible and adaptable for deep learning tasks.
• Caffe is particularly known for its high-speed performance, especially in image processing tasks on Graphics Processing Units (GPUs).
• It supports various deep learning architectures, making it versatile for tasks like image classification and segmentation.

Best For

• Caffe is highly efficient and fast, making it ideal for image classification tasks on GPUs.
• It is optimized for vision-related tasks with a clear, structured architecture.

Limitations of Caffe

• Caffe is less flexible for non-vision applications or for building complex, non-standard networks.
• The last update for Caffe was in 2017, and it lacks many features and community support found in newer frameworks.

How to Choose the Best AI Framework for Your Project?

When you are confused about deciding the best AI framework for your development project, here are some tips that will help you greatly. 

Performance

Performance is at the top of every criterion when choosing between the best AI frameworks. It is always advisable to choose the one that can handle the data efficiently and offer faster training and inference times. You can evaluate the performance of the framework through benchmarks and real-world use cases. 

Community support

Active and helpful community support is what we call an absolute necessity for developing complex AI systems. Choose the one that offers unlimited access to resources, community-driven plugins, and tutorials with support for implementing annoying updates to the framework.            

Flexibility

Flexibility is needed when working on AI development projects, and it should be treated similarly. The best AI framework allows the ability to experiment with various algorithms. Additionally, the framework should be highly adaptive to adapt to different data types, including images, text, and even audio. It should be seamlessly integrated with other technologies without any issues, making AI systems more accurate, effective, and efficient. 

Ease of Learning

 It is crucial to choose an AI framework that aligns with your current skill level. Look for frameworks that are beginner-friendly, offering a more detailed guide and tutorials. Consider the resources and learning curve when choosing between the top AI frameworks. 

Conclusion

AI frameworks are the basics of the advancement of artificial intelligence, serving as a crucial aspect in software development. These frameworks seamlessly integrate with multiple programming languages and provide solutions to real-world challenges through predictive analysis, computer vision, and NLP. As the technology continues to progress, integrating quantum and edge computing will shape the future of AI in mobile app development and beyond. 

So, we are wrapping up this topic! Hope you find it helpful in understanding the best AI frameworks. If you still have doubts or need assistance with your existing AI development project, you can contact the best AI development company, i.e., Mtoag Technologies. We boast years of experience working with advanced AI systems and have so far helped numerous firms achieve their AI goals. 

FAQs        

What Framework is Used for AI? 

AI frameworks are software libraries or tools that provide pre-built functions and models to develop, train, and deploy machine learning algorithms. Popular frameworks include TensorFlow, PyTorch, Keras, and Scikit-Learn.

How Does the AI Framework Work?

An AI framework works by providing a set of tools, libraries, and interfaces for building and training machine learning models. It typically includes modules for data preprocessing, model architecture, training, evaluation, and deployment. Developers can leverage these resources to streamline the development process and improve model performance.                                                                                                                          

What are the Best 5 AI Agent Frameworks? 

Five notable AI agent frameworks are:

• OpenAI Gym
• Microsoft Bot Framework
• Rasa
• TensorFlow Agents
• DeepPavlov

Which is the Best AI Framework? 

The best AI framework depends on the specific use case and requirements. TensorFlow and PyTorch are widely regarded as top choices for deep learning due to their robustness and versatility. Keras and Scikit-Learn are often preferred for simpler tasks or rapid prototyping.

What are the Top NLP Frameworks? 

Leading NLP frameworks include:

• spaCy
• NLTK (Natural Language Toolkit)
• BERT (Bidirectional Encoder Representations from Transformers)
• GPT-3 (Generative Pre-trained Transformer 3)
• Hugging Face Transformers

Each of these frameworks excels in different aspects of NLP, from text processing to advanced language modeling.