Top AI Frameworks in 2024: Comparison of Artificial Intelligence Frameworks

The future that visionaries wrote about is here. We can enjoy recommendation engines, automate manual operations, generate content, and get incredible results from simple prompts thanks to the use of artificial intelligence (AI) and its subfields — deep learning (DL) and machine learning (ML).

In 2022, we were astonished by ChatGPT, a product by OpenAI that demonstrates sky-high performance and precision when responding to users’ queries. While ChatGPT was the first tool of its kind with this degree of capabilities, today we have various tools based on large language models (LLMs). They are extending the borders of the digital world, offering software products that are more powerful, useful, and creative than ever.

What’s even more important these days is that you can connect your software with any AI tool, boost its efficiency, improve your whole business, meet more customer needs, and achieve business goals.

In this article, we consider major types of artificial intelligence frameworks, highlight the most popular, and help you choose the right AI framework for your particular use case.

What is an AI framework?

Say you need a powerful neural network to improve the recommendation engine on your real estate platform. Does this mean you need to build and train a neural network from scratch, then develop new software for your users based on it? Not necessarily. You can use AI frameworks to strengthen your product with AI functionality.

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AI frameworks provide data scientists, AI developers, and researchers with the code components to architect, train, validate, and deploy intelligent functionality through a programming interface.

AI frameworks, which include deep learning and machine learning frameworks, are sets of components that help your development team integrate smart algorithms into your product. They simplify AI product development and speed up the product launch.

Why would you want to use an AI framework?

Developers use AI frameworks for three main reasons:

• Ready-made infrastructure. AI frameworks provide the instruments developers need to build an AI product. No need to build them from scratch.
• Code standardization. While working on the same project, developers may generate different ideas on how to solve similar tasks. Frameworks have requirements and standards that allow you to implement a unified approach to coding and decision-making and improve code quality.
• Resource optimization. Frameworks help to save developers’ time and your budget.

Popular AI frameworks have a powerful community of developers behind them. When you start using an AI framework for your product, you get access to community expertise and an opportunity to use development best practices for your application.

Common features of the most powerful AI frameworks

Before we dive into key differences between major AI frameworks, let’s have a quick look at features they share:

• Simplicity. AI frameworks provide a simple interface for working with complex mathematical operations and algorithms. This permits developers to work with AI models without needing to build them from scratch.
• Efficiency. Most AI frameworks are optimized for high-performance computing and can use GPUs (graphics processing units) and TPUs (tensor processing units) to accelerate training and inference.
• Flexibility and customization. AI frameworks are flexible, allowing you to customize and experiment with various characteristics of your AI model, including neural network architecture, loss functions (that describe the difference between predicted and real values in AI models), and optimization algorithms.
• Compatibility. Many AI frameworks are compatible with popular programming languages like Python and JavaScript, making it easy to integrate AI models into existing software systems.
• Scalability. AI frameworks can handle both small-scale and large-scale tasks; thus, along with, for example, SaaS app development, they are suitable for academic research.
• Model zoos. Many frameworks have model repositories, often called model zoos, where you can find pretrained models for various tasks. These can save a significant amount of time and computational resources.
• Interoperability. Some frameworks can work together, allowing you to combine the strengths of multiple frameworks for different aspects of your project.
• Ecosystem. Popular AI frameworks have large and active communities of developers, researchers, and users. This means that you can find extensive documentation, tutorials, and pre-trained models, making it easier to get started and troubleshoot issues.

Now, let’s find out what types of frameworks you can consider for your project, how they differ, and how to make the right choice.

Major types of frameworks you can use in AI product development

As you research AI frameworks, you may find open-source and commercial solutions, frameworks based on deep learning and machine learning algorithms, training and inference frameworks, and many others. The classifications of AI frameworks are many and complex.

In our article, we show you two major types of frameworks:

• AI frameworks are widely used for developing machine learning and deep learning models.
• LLM orchestration frameworks are used for development and training of large language models.

We’ve also decided to consider subscription-based enterprise AI toolkits, which may be confused with AI frameworks.

With the help of our article, you can enrich your knowledge of cutting-edge frameworks, tap into a source of their similarities and differences, find out about tools developed by Amazon, Meta, and IBM, and decide on the right technologies for your project.

AI frameworks

Among the abundance of open-source AI frameworks, we’ve picked the five most widely used: TensorFlow, PyTorch, Keras, scikit-learn, and spaCy.

To select the top AI frameworks, we:

• Used our AI development engineers’ experiences and insights from real use cases to evaluate and describe each particular framework
• Analyzed GitHub data and chose the five most used frameworks based on three main factors: GitHub stars, number of contributors, and number of projects that use a particular framework in their repositories (as of January 2024)
• Reviewed Google trends over the previous two years

Get to know each of these frameworks, discover products built with them, and learn about their advantages and disadvantages in this section.

TensorFlow

TensorFlow, a widely used open-source machine learning library, is a free tool coded in Python. It is purpose-built for training and prediction tasks with neural networks. TensorFlow is based on deep learning inference, which involves pretrained deep neural network models to make predictions on new data.

TensorFlow empowers developers to create expansive neural networks with multiple layers using data flow graphs. Its superpower is flexibility: the framework enables easy deployment across a variety of platforms (CPUs, GPUs, TPUs) with minimal or no code changes. TensorFlow processes data in the form of multidimensional arrays, housing vectors, and matrices known as tensors.

What products benefit from TensorFlow? Google released the initial version of TensorFlow in 2015 with the goal of bringing machine learning to everyone. Almost nine years later, TensorFlow may be handy for a variety of products and industries. It solves challenges related to the development of AI healthcare solutions, social networks, and eCommerce apps. Online marketplaces may also benefit from TensorFlow: for example, Airbnb uses the TensorFlow framework for image classification.

You may use TensorFlow to:

• Build and retrain deep, complex, large-scale neural networks
• Develop projects based on natural language processing (NLP)
• Create functionality for complex calculations

When deciding on TensorFlow for your next project, get ready for a steep learning curve. To make it work for your business, extensive programming and ML knowledge is required. If you are familiar with other Google engineering products, you may notice another common disadvantage: they are too complex, with lots of unnecessary modules and overly complicated components.

Another thing worth mentioning is that Google is working on a big AI framework called JAX (“Just After eXecution”), and chances are, the team is investing more effort in JAX, which may lead to TensorFlow’s possible obsolescence in the future.

PyTorch

On lists of top AI frameworks, TensorFlow and PyTorch go side by side. With an extensive list of tools and libraries, PyTorch is a leader in the AI development league.

PyTorch is an open-source machine learning framework developed by Facebook’s AI Research lab. Known for its dynamic computation graph mode in development environments, PyTorch is widely used for academic purposes.

PyTorch stands out for data parallelism — scenarios where operations take place concurrently on different elements of the system, distributing computational work across multiple CPU or GPU cores.

In 2023, Meta released PyTorch 2.0, which they describe as “faster, more Pythonic and as dynamic as ever.” This new version of the framework demonstrates better performance than its predecessor. The development team claims to have lowered the barrier to entry for AI developers and reached better flexibility in dynamic shapes.

What products benefit from PyTorch? PyTorch serves the needs of numerous industries, including advertising and marketing, finance, and healthcare. One of the most fascinating use cases is by Tesla, which uses PyTorch for self-driving capabilities. This framework is also great for technology and academic projects. PyTorch is the tool that enables AI on mobile devices — for example, Instagram uses it to create engaging experiences and interactive filters. Stanford University uses PyTorch capabilities to explore new algorithmic methods.

You may use PyTorch to:

• Research and experiment with prototypes
• Develop systems that require data parallelism
• Create NLP-based apps

PyTorch is a powerful Python-based AI framework suitable for research tasks. It allows you to view changes in code on the go, yet it has some disadvantages. Compared to TensorFlow, PyTorch is praised for its ease of use. However, the community and ecosystem are slightly smaller, and the framework may be less convenient to deploy models in production environments. Still, TensorFlow and PyTorch share multiple features, and both may fit your project’s needs.

Keras

Keras is an open-source machine learning framework that offers an intuitive interface for building and training deep learning models with a focus on simplicity and consistency. It requires a powerful AI back end and creates a powerful duo with TensorFlow.

When it comes to the most developer-oriented API, Keras may be an absolute leader. The official website claims that Keras was built for human beings, not machines; it minimizes the number of developers' actions and provides good documentation and clear error messages.

Keras streamlines AI model development by offering optimization algorithms, loss functions, and evaluation metrics for seamless integration into the training process. Keras also simplifies and accelerates model training. Compared to PyTorch and TensorFlow, Keras allows developers to build a smaller, more elegant, and more readable codebase that is easier to support and extend.

Developers can integrate Keras with TensorFlow or PyTorch, which may increase software efficiency and performance.

Keras 3.0 was launched in 2023. The new version provides several critical benefits:

• Higher AI model performance compared to the previous version
• Model and data parallelism – a feature that allows for splitting models and data across multiple GPUs
• Ability to use data from any source

What products benefit from Keras? Keras is used for a variety of tasks, including image classification and segmentation, object detection, NLP, and data generation. For example, Revy — a shared micromobility company — uses Keras to forecast the customer conversion rate.

You may use Keras to:

• Strengthen your existing TensorFlow-based model
• Experiment with different model architectures and hyperparameters

In a nutshell, Keras is more like an interface that should be used on top of a powerful AI back end.

You may notice Keras’s limitations the moment you start looking for use cases. Compared to PyTorch and TensorFlow, fewer projects use Keras. The community is smaller, too. The framework demonstrates limited flexibility for complex models, provides less control over the training process, and may be less suitable for research purposes. Besides, if you use any framework other than TensorFlow, it may be challenging for you to integrate it into your Keras project. At the same time, Keras relies heavily on a backend framework, so limitations of the back end impact Keras’s performance as well.

Scikit-learn

Back in 2018, scikit-learn was used by almost 40% of GitHub projects. These days, it’s still quite popular and keeps extending its capabilities.

Scikit-learn is a Python-based ML library with a focus on predictive data analysis. Some core algorithms behind this library are written in Cython – a type of Python that can reach levels of code performance comparable to C. Thus, the performance of scikit-learn is high.

Scikit-learn uses three key libraries:

• NumPy for working with data arrays and mathematical algorithms
• SciPy for scientific computing
• Matplotlib for data visualization

Scikit-learn provides developers with numerous APIs for data clustering, probability calculations, feature selection and extraction, and more.

What products benefit from scikit-learn? As with most ML frameworks, scikit-learn is used to solve tasks related to image classification, sentiment analysis, recommendation system development, and so on. Spotify uses this library for music recommendations, and according to Erik Bernhardsson, Engineering Manager at Spotify, it’s the most well-designed ML package so far.

You may use scikit-learn to:

• Start your experience with AI project development
• Build fraud detection functionality
• Implement predictive analytics in your product

Parallel processing operations with scikit-learn are not as efficient as with other top AI frameworks. It’s not suited for complex tasks and heavy calculations.

spaCy

“I think small companies are terrible at natural language processing,” said Matthew Honnibal, founder of Explosion AI, in 2015 when introducing spaCy. More than eight years ago, Matthew focused on building a simple NLP framework for founders of small and medium companies, and to this day, spaCy serves the needs of businesses worldwide.

In 2021, Explosion AI rolled out spaCy 3.0. Based on the transformer architecture and trained on 73 languages, spaCy demonstrates high performance and efficiency – the tool is perfect for processing large volumes of text data.

What products benefit from spaCy? Any project that has to do with NLP and text analysis may benefit from this tool. On their blog, Explosion shares the use case of a reviews analysis system based on spaCy.

You may use spaCy to:

• Get started with NLP
• Try different model architectures

According to the Explosion development team, spaCy may not be the right choice for research purposes, and it’s not designed for chatbots.

Talking about spaCy, we’ve come closer to the concept of a transformer architecture: a DL encoder–decoder architecture based on the attention mechanism. This architecture is the basis of ChatGPT, LLaMA, Mistral, Claude, and other large language models.

Inspired by amazing use cases and the human-like intelligence of LLMs, you may want to connect one to your software product. Let’s see what LLM orchestration frameworks you can use to make your whole business more intelligent.

LLM orchestration frameworks

With LLM integration, you can strengthen your product with numerous capabilities like data analysis and content generation, usage pattern identification, image recognition, and prediction analysis. This is why we decided to add this section to our article and increase your awareness of LLM orchestration frameworks that can help you train a language model on your data from scratch.

Orchestration frameworks simplify the development and deployment of applications built around LLMs, enhancing their performance and reliability.

LangChain

With LangChain, you can craft autonomous agents capable of intelligent decision-making, develop personal assistants that understand and respond to user queries, create chatbots for interactive communication, and implement robust systems that understand code.

LangChain offers an open-source library with prebuilt components and chains your development team may use to train your system on relevant data.

LangChain has one of the biggest contributor communities on GitHub and includes integrations with major cloud providers: Amazon, Google, and Azure.

What products benefit from LangChain? Instacart, Zapier, Dropbox, and numerous other leading companies use LangChain in their AI-driven products. For example, Adyen, an eCommerce company, uses LangChain capabilities for boosting support team efficiency. SaaS startups benefit with this framework, too.

You may use LangChain to:

• Develop intelligent decision-making agents
• Create user-responsive virtual assistants and chatbots
• Enhance code analysis and development processes
• Improve your applications with advanced language model capabilities
• Tap into generative AI capabilities

What about the drawbacks? According to Max Woolf, Data Scientist at Buzzfeed, “the problem with LangChain is that it makes simple things relatively complex, and with that unnecessary complexity creates a tribalism which hurts the up-and-coming AI ecosystem as a whole.”

LlamaIndex

LlamaIndex is an open-source orchestration framework tailored for connecting an LLM to original data sources. It shares common features with LangChain, including a distributed architecture and robust LLM management capabilities. LlamaIndex is a versatile tool that connects various data sources, such as documents, databases, and websites, making them accessible and searchable by LLMs.

There are two key features of LlamaIndex:

• Distributed querying: LlamaIndex excels in distributed querying of LLMs, optimizing query processing across a software system.
• Efficient indexing: LlamaIndex offers the ability to index LLMs, enhancing their search efficiency.

What products benefit from LlamaIndex? This framework may help to create knowledgeable agents — AI chatbots trained on your corporate data, both structured and unstructured.

You may use LlamaIndex to:

• Get answers from unstructured data sources like PDFs and web pages, legal documents, and research papers
• Create chatbots that tap into your knowledge database for personalized responses
• Structure data with natural language for insights

LlamaIndex is good only at tasks related to text processing. Another feature is that the quality of the output depends heavily on the quality of the input, meaning the quality of responses you get with LlamaIndex depends on your embeddings.

Haystack

Haystack covers many similar tasks: answering questions, searching through documents, extracting data, etc. Haystack is an open-source framework for building and deploying search and question-answering systems powered by large language models. The framework works with multiple platforms (including tools provided by OpenAI and Hugging Face, which we’ll review later), offers integrations with popular vector databases, and provides the Haystack Rest API — an opportunity to deploy your system as an API and access it via web or mobile apps.

What products benefit from Haystack? On their website, the Haystack team claims this framework may help users build search systems, data extraction tools, and FAQ software.

You may use Haystack to:

• Build custom search engines
• Create question-answering systems
• Extract specific information from text documents
• Summarize lengthy content

Developers mention that one of Haystack’s disadvantages is the lack of scalability. As you extend your application and your data system grows, Haystack may fail to provide the expected performance and quality.

Alternative enterprise toolkits to handle your business tasks

Open-source frameworks are not your only choice. There are whole packages of solutions with powerful features available right out of the box; just choose a subscription plan and get the functionality you need. Let’s start with the most impressive and affordable one.

Hugging Face

Hugging Face is an entire AI community, both a platform and a framework. It provides AI models, spaces, and datasets for creating ML applications with great functionality and performance.

Hugging Face provides a comprehensive library of tools for working with pretrained NLP models, making it an essential resource for developers and researchers in the AI and NLP fields. The Hugging Face Transformers library is widely used for building, fine-tuning, and deploying transformer-based models, including those for text classification, language generation, and other NLP tasks.

HuggingFace provides free access to hosting space, orgs, repos, and open-source tools, and it has paid plans with more advanced features and support.

What products benefit from Hugging Face? More than 50,000 organizations use Hugging Face. Grammarly, Microsoft, and Meta are among the most well-known users.

You may use Hugging Face to:

• Handle text analysis, sentiment analysis, language translation, text generation, and other NLP tasks
• Fine-tune models for your unique applications
• Develop chatbots and virtual assistants that require natural language understanding and generation
• Generate content including articles, reports, and product descriptions
• Categorize text data
• Develop and deploy entire AI systems

As you browse through Hugging Face offerings, you may notice the Enterprise Hub — a subscription plan that allows you to access the platform’s features along with complete data security, dedicated customer support, and simple access controls. You can create your private environment on the AI platform, train your models on the provided infrastructure, and deploy your solution to production in just several clicks.

Watsonx

Watsonx by IBM is an expansive collection of AI and machine learning services. IBM’s long history in the IT field makes Watsonx worth mentioning in top AI frameworks lists.

The Watsonx collection includes three major components: a new model development studio, a data store, and an AI toolkit. This suite equips developers and organizations with a diverse set of tools for constructing and launching AI-driven applications, encompassing natural language processing, computer vision, and predictive analytics.

One of the advantages of this tool set is its seamless integration with IBM Cloud infrastructure, which enables a straightforward app deployment process.

You can access numerous Watsonx features with the free tier, or consider the Standard plan that provides model hosting, prompt tuning, infrastructure management, third-party integrations, and other services.

What products benefit from Watsonx? Watsonx is popular among midsize businesses and enterprises. NatWest, Eviden, Samsung SDS, Deloitte, and multiple other famous companies use IBM AI tools.

You may use Watsonx to:

• Perform medical research
• Create enterprise chatbots and virtual assistants
• Get insights from your organization’s unstructured data
• Detect financial fraud
• Assess and mitigate business risks

Amazon SageMaker

Amazon SageMaker is a cloud-based machine learning infrastructure provided by Amazon Web Services (AWS). It offers an easy-to-use interface for:

• building an ML model from scratch
• preparing training data with minimum coding effort
• training models
• deploying models to production
• automating ML workflows
• automatically generating ML-based predictions

Your team can scale SageMaker to handle large datasets and integrate it with other AWS services. Amazon ML tools may be useful for several specialists within your organization:

• Data scientists who know how to code
• Data scientists who work with low-code or no-code solutions
• AI engineers who develop your products
• Business analysts

Amazon SageMaker pricing depends on the resources you use, and you can estimate your approximate costs using the AWS Pricing Calculator.

What products benefit from AWS SageMaker? Numerous famous companies use AWS machine learning tools to streamline their business operations and transform organizations with the power of AI. Workday, Salesforce, Wix, Canva, and others use SageMaker for a variety of needs. Chris Hausler, head of AI at Zendesk, says that with SageMaker MME, the team built a multi-tenant, SaaS-friendly inference capability to host multiple models per endpoint, reducing inference cost by 90% compared to dedicated endpoints.

You may use SageMaker to:

• Boost an existing AWS-based product with AI functionality
• Process large datasets and workloads
• Detect fraud in real time

OpenAI tools

OpenAI is the legendary team that released Generative Pre-trained Transformers (GPT) models and Chat GPT; this team also stands behind DALL-E and a multitude of AI research papers, guides, and reports.

OpenAI is a phenomenon in the IT industry and was one of the most discussed AI companies in December 2023. The company’s incredibly talented team has forced the AI breakthrough we’re now experiencing.

OpenAI provides API services, allowing developers and businesses to harness the power of their AI models in various applications. The company offers the OpenAI API based on GPT-4 to solve your business tasks and ChatGPT subscription plans to enhance your product with fantastic capabilities.

OpenAI actively engages in AI research and is known for its commitment to ethics and safety in AI development. They emphasize the responsible and transparent advancement of AI and publish research papers to support and educate the AI community.

What products benefit from OpenAI APIs: Two million developers, 92% of Fortune 500 companies, and over 100 million people use ChatGPT weekly. The capabilities of OpenAI products can hardly be exaggerated: by integrating your product with OpenAI APIs, you can boost every business process, improve every product feature, and take a mile-long step towards improving your business. OpenAI products serve a variety of purposes, including creating NLP applications, generating different types of content, analyzing data, and building chatbots, wikis, and search recommendations. OpenAI products also allow you to experiment with the latest web technologies to discover the best use cases by yourself. Duolingo, Stripe, Wix, and numerous healthcare and government projects benefit from OpenAI solutions.

You may use OpenAI tools to:

• Develop chatbots, virtual assistants, translation apps, and recommendation engines
• Generate text, images, video, and audio content
• Convert text to speech, images, and video
• Extract insights from corporate data
• Experiment with cutting-edge technologies
• Enhance your routine business operations with automation, creativity, and machine intelligence

These AI toolkits share a common feature: you need a subscription to use them. Most companies provide free plans, yet these are only suitable to test the waters and will not be enough to handle your business tasks. More advanced plans may impact your overall development budget. It may be easier for you to use off-the-shelf tools than to train a model from scratch, but still, you need to cooperate with an AI development team to use the capabilities of enterprise frameworks.

How to choose an AI framework for your needs

What AI framework may be best for your business?

To answer this question, you should:

• Collect information about your current business needs. Without a detailed picture of your product idea, no one can advise you on the best framework.
• Consult with an AI app development team. The market is evolving rapidly, and it’s hard to keep track of all the latest technologies while running your business. Development service providers may help you choose and implement the most suitable framework.

As an alternative, you may consider CTO as a service providers to get an in-depth consultation on most powerful tools.

We analyzed our previous experience with AI app development for our clients to determine the optimized flow for selecting the right framework:

1. Our project discovery team reviews your initial requirements.
2. A business analyst prepares essential project documentation.
3. A software architect develops an architectural approach for your app and suggests the right technologies for it, including the AI framework.
4. The discovery team composes a software requirements specification highlighting solutions tailored to your current needs.

We take these steps during the project discovery phase at the very start of the product development process.

Conclusion

Choosing an AI framework for your project may be frustrating. There are many tools you may use, and not that many explanations on when to choose a particular technology. The number of AI technologies, libraries, and solutions is growing so fast that one can hardly find the time to explore and try all of them.

In the meantime, the choice of AI framework is critical.

• The right choice of technology may help you validate your idea quickly, implement AI functionality in your product, and start attracting new customers and investors faster than competitors.
• The wrong framework may fail to meet your project’s needs. It may lack flexibility or be too complex for your tasks. After implementing it, you may notice the limitations and decide to go with another solution, but doing so will cost you additional time and money.

In the AI market, it’s essential to test ideas quickly, iterate fast, and make the right decisions to get desirable business outcomes.

With the right framework, you get one step closer to your goals.

Sources

The state of AI in 2023 by McKinsey.

Google Cloud TPUs for AI project development. Introduction to TPUs by Google.

What Is a GPU? Introduction to GPUs by Intel.

GitHub global development platform.

TensorFlow – the most widely used ML library.

Airbnb Engineering – how the global rental marketplace uses TensorFlow to categorize photos.

Google JAX – an ML framework based on autograd and TensorFlow’s XLA.

Google bets on JAX as Meta’s PyTorch outperforms TensorFlow.

PyTorch framework and community insights.

PyTorch at Tesla: Andrej Karpathy shares fascinating insights on how tech giants utilize the power of PyTorch.

PyTorch brings the power of AI to computers and smartphones.

Keras – a multi-backend AI framework.

The Reby team shares details about predicting ride conversion rate with Keras.

Scikit-learn – an AI framework for predictive data analysis.

Spacy – an NLP framework by Explosion AI based on Python and Cython.

Use case: exploring health supplement effects with Spacy.

Attention is All You Need: a paper on Transformer architecture

Langchain – a framework for developing LLM applications from scratch.

The state of open source and rise of AI in 2023 by GitHub.

Research on generative models by Open AI.

The Problem With LangChain by Max Woolf.

LlamaIndex – a framework that allows you to connect an LLM to a data source.

Haystack – an LLM framework for AI application development.

HuggingFace – a platform that provides AI models, datasets, and applications, and connects the AI community.

Watsonx – an AI platform by IBM.

Machine Learning on AWS. Insights, tools, and best practices for AI development shared by the Amazon team.

Open AI, its insights and products: the most powerful source of AI development best practices by one of the most innovative companies in the AI market.