10 Leading Deep Learning Frameworks for Building AI Models

You need a deep learning framework to train and deploy a neural network without wasting too much time on writing gradient calculations, memory management, and GPU code by hand. But, with many frameworks out there, how can you pick a suitable one for your project?

This guide ranks the 10 deep learning frameworks worth using today by what each one is actually built for, and provides a decision framework for you to pick the right one.

What is a Deep Learning Framework?

A deep learning framework is a software library that provides the building blocks for designing, training, and deploying neural networks. It handles the underlying math, memory management, and hardware acceleration so a developer can define a model in a few lines of code instead of implementing backpropagation from scratch.

Every major framework provides the same core set of tools:

• Pre-built layers for common network types (convolutional, recurrent, transformer), 
• Automatic differentiation for computing gradients,
• Support for running computation on GPUs or TPUs.

Frameworks differ in how they expose these tools, which programming languages they support, and which use cases they optimize for.

Benefits of Using a Deep Learning Framework

Frameworks provide benefits over a custom-built pipeline:

• Faster development: Pre-built layers, optimizers, and training loops mean a developer writes a model definition instead of the underlying math. So, infrastructure work take just days, not weeks.
• Automatic hardware acceleration: A framework compiles the same code to run on a CPU, GPU, or TPU without the developer writing separate code paths for each. A model thus scales to more powerful hardware without a rewrite.
• Access to pre-trained models: Most frameworks maintain a public library of pre-trained architectures for tasks such as image classification, object detection, and text generation. A project can start from a model that already understands general features instead of training from zero.
• Simplified experimentation: Changing a layer, optimizer, or hyperparameter takes a line of code instead of reworking custom infrastructure, which makes it practical to test several model designs before committing to one.
• Visualization and debugging tools: TensorBoard and equivalent tools show training loss, accuracy, and gradient behavior in real time, which makes it possible to catch a broken training run early instead of after it finishes.

10 Deep Learning Frameworks with Reviews and Pricing

The table below compares all 10 frameworks covered in this guide. Each framework is explained in more detail after the table.

TensorFlow

TensorFlow is Google's open-source framework for building and deploying machine learning models at production scale. Google released TensorFlow in November 2015 under the Apache 2.0 license, and it remains the framework most teams reach for when a model needs to run reliably in production.

TensorFlow costs nothing to license, and the only real expense is the cloud or on-premise compute it runs on.

TensorFlow's biggest advantage is its deployment ecosystem, including:

• TensorFlow Serving handles production inference,
• TensorFlow Lite runs models on mobile and embedded devices,
• TensorFlow.js runs models directly in a browser.

The tradeoff is a steeper learning curve than PyTorch for developers who are new to deep learning, though the built-in Keras API narrows that gap considerably.

Real-world use cases:

• Google: Uses TensorFlow internally for tasks such as Google Photos image search.
• Airbnb: Built a TensorFlow model to automatically categorize its listing photos, a project the company presented publicly at Google's TensorFlow Dev Summit.

>> Read more: Why Should Python Be Used For Machine Learning?

PyTorch

PyTorch is Meta AI's open-source framework, released in September 2016 under the BSD-3-Clause license, and it has become the default choice for machine learning research. Its dynamic, define-by-run computational graph makes debugging and rapid iteration easier than TensorFlow's original static-graph design.

Multiple industry surveys and academic paper trackers report that most newly published machine learning research now uses PyTorch, while TensorFlow retains a stronger footprint in production systems built before 2020. PyTorch closed much of its earlier production gap with TorchServe for model serving and PyTorch Mobile for on-device inference.

Developers use PyTorch for production work as well as research. This framework is free to use, with no licensing cost at any scale.

Real-world use cases:

• Tesla: Trains its Autopilot and Full Self-Driving computer vision models in PyTorch, a choice its engineering team has discussed publicly.
• OpenAI: Announced in 2020 that it would standardize its research on PyTorch, the framework it has used to train the GPT model family since.

Keras

Keras is a high-level API for building neural networks with minimal code, created by François Chollet and first released in March 2015 under the MIT license. Keras 3, released in 2023, rewrote the library to run on three different backends, including TensorFlow, PyTorch, and JAX.  So, a developer can write one model definition and choose which underlying framework executes it.

Keras is the fastest way to prototype a standard model architecture, since common layers, optimizers, and training loops need only a few lines of code. It remains less suited to novel research architectures that require custom, low-level control over the training loop. Like the frameworks it sits on top of, Keras is free.

Many teams validate an idea in Keras, then rewrite the finished architecture in lower-level TensorFlow or PyTorch code once it moves toward production.

JAX

JAX is Google's framework for high-performance numerical computing and machine learning research, built on top of the XLA compiler and released publicly in 2018 under the Apache 2.0 license. It exposes a NumPy-compatible API and adds automatic differentiation and just-in-time compilation. So, JAX is well suited to research that needs to run at TPU scale.

JAX is the framework behind much of Google DeepMind's research output. Its functional programming style appeals to teams that want precise control over how computation gets parallelized across accelerators.

However, JAX has a steeper learning curve than PyTorch or TensorFlow for developers unfamiliar with functional programming patterns, and its production tooling is less mature than TensorFlow's.

AlphaFold, a DeepMind's protein structure prediction model, was built using JAX along with Haiku, DeepMind's JAX-based neural network library.

Hugging Face Transformers

Hugging Face Transformers provides pre-trained transformer models for natural language processing, computer vision, and audio tasks, released by Hugging Face in 2018 under the Apache 2.0 license. It works as a layer on top of PyTorch, TensorFlow, or JAX rather than as a standalone framework, and connects to the Hugging Face Hub, one of the largest public repositories of pre-trained models available today.

Hugging Face Transformers itself is free, and so is downloading and using most models on the Hub. But, it charges separately for hosted infrastructure, with:

• A Pro account at $9 a month,
• Team plans at $20 per user per month,
• Inference Endpoints starting around $0.033 an hour.

All above are optional and only needed once a team wants Hugging Face to host training, inference, or private storage rather than running it themselves.

For any team working with large language models or pre-trained NLP architectures, Transformers is the standard starting point since it removes the need to train a model like BERT or a GPT-style architecture from scratch.

The main limitation of Transformers is it's built around existing pre-trained architectures rather than novel model design, so research into new architectures still happens directly in PyTorch or JAX.

Here are some common use cases of Hugging Face Transformers:

• Customer-support chatbots: Fine-tuned on a company's own support history and documentation.
• Document summarization: Adjust long internal or customer-facing documents into a short summary.
• Semantic search: Matches a query to relevant content by meaning rather than exact keyword.

>> Explore further:

• Top 17 Best AI Apps For Individuals and Businesses
• Top 12 Best Free AI Chatbots for Businesses

ONNX Runtime

ONNX, short for Open Neural Network Exchange, is an open format that lets a model trained in one framework run in another. Microsoft and Meta introduced ONNX in 2017, and it now operates under the Linux Foundation's open governance model. ONNX Runtime is the cross-platform engine that executes ONNX models for inference, under the MIT license.

ONNX is flexible in deployment: a team can train a model in PyTorch, export it to the ONNX format, and run it in a C++, Java, or C# production environment without depending on Python or the original training framework. This matters most for teams deploying to environments where Python isn't a good fit, such as mobile apps or embedded systems.

Real-world use cases:

• Windows ML: Microsoft uses ONNX Runtime as the inference engine behind this on-device AI runtime built into Windows 11.
• Azure Machine Learning: Microsoft uses ONNX Runtime for model deployment on Azure ML, letting a team train in one framework and deploy through ONNX.

PaddlePaddle

Released in 2016 under the Apache 2.0 license, PaddlePaddle is Baidu's open-source deep learning framework with dedicated toolkits for OCR, NLP, and object detection. It has a strong presence in industrial and mobile deployment, particularly in the Chinese market where Baidu has invested heavily in adoption and documentation.

The execution engine of PaddlePaddle is optimized for both training and inference on resource-constrained hardware, which makes it a practical option for embedded and mobile deployment. However, community support and English-language documentation are less extensive than for TensorFlow or PyTorch, which is the main tradeoff for teams outside its core market.

With PaddlePaddle, Baidu built the PaddleOCR, open-source optical character recognition toolkit. It has become one of the more widely used open-source OCR toolkits for document- and industrial text-recognition applications, including outside China.

MATLAB

MATLAB is MathWorks' commercial numerical computing environment with a Deep Learning Toolbox added for neural network design, training, and visualization.

Unlike every other framework in this list, MATLAB requires a paid license. MathWorks lists a standard individual license at $940 a year, which bundles MATLAB with Simulink and a set of add-on products, and prices the Deep Learning Toolbox and organizational or commercial licenses separately through its sales team.

MATLAB integrates well with Simulink, MathWorks' simulation environment for control systems and signal processing. So, engineering teams that already rely on Simulink for hardware modeling often choose MATLAB. Teams without an existing MATLAB investment rarely choose it as an entry point into deep learning, given the licensing cost and the strength of free alternatives.

Some use cases of MATLAB, from MathWorks' own published customer case studies, often built alongside Simulink for the surrounding control or monitoring system:

• Automated visual inspection: Detects defects or classifies parts on a production line.
• Predictive maintenance: Flags equipment likely to fail before it does, based on sensor data.
• Signal classification: Identifies patterns in sensor or measurement data, common in fault detection.

Deeplearning4j (DL4J)

Deeplearning4j is a deep learning library for the Java Virtual Machine, supporting Java, Scala, and Kotlin, distributed under the Apache 2.0 license and maintained under the Eclipse Foundation. It integrates directly with Apache Spark and Hadoop.

DL4J suits enterprise teams that need to add deep learning to an existing Java-based data infrastructure without introducing a Python dependency. Its community and pre-trained model ecosystem are far smaller than Python-based frameworks, which limits it mainly to teams with a specific JVM integration requirement.

Fraud and anomaly detection is a common use case of DL4J, built directly into an existing Java-based data pipeline, such as one already running on Apache Spark for real-time transaction scoring.

DeepSpeed

DeepSpeed is Microsoft's open-source library for training and running large-scale models. It provides the Zero Redundancy Optimizer (ZeRO), which reduces the GPU memory required to train very large models by splitting optimizer state, gradients, and parameters across multiple devices instead of duplicating them on every GPU.

DeepSpeed does not replace PyTorch or TensorFlow. Instead, it extends these frameworks, with most current implementations built on PyTorch.

When a model is too large to fit on a single GPU or small cluster, DeepSpeed distributes the training workload across multiple GPUs and manages memory more efficiently. For projects at this scale, teams also use comparable frameworks such as Megatron-LM.

Microsoft and NVIDIA used DeepSpeed with Megatron-LM to train the 530B-parameter Megatron-Turing NLG model, which was one of the largest language models at the time. The project shows how DeepSpeed helps teams train extremely large models across many GPUs, and the framework remains a common option for large-scale model training today.

>> Read more: ​​Big Data E-commerce: Definitions & Best Practices

How to Choose the Right Deep Learning Framework?

Consider the following factors when choosing a framework:

• Project type: Research and rapid experimentation favor PyTorch or JAX because of their dynamic graphs. Production systems that need a mature deployment pipeline favor TensorFlow or a PyTorch-plus-ONNX combination.
• Task domain: NLP and LLM work benefit from starting with Hugging Face Transformers. Computer vision work runs well on either TensorFlow or PyTorch, both of which have mature pre-trained vision model libraries.
• Team language: Python-based teams have the widest choice of frameworks. Java or Scala teams with existing JVM infrastructure get the most direct integration from DL4J.
• Deployment target: Mobile and edge deployment point toward TensorFlow Lite, PyTorch Mobile, or ONNX Runtime. Browser-based deployment points toward TensorFlow.js.

FAQs

Is PyTorch or TensorFlow better?

Neither framework is better in every case, but each fits a different stage of a project. PyTorch is the stronger choice for research, NLP work, and fast iteration, while TensorFlow is the stronger choice when a project needs its production deployment tools, such as TensorFlow Serving or TensorFlow Lite.

Which deep learning framework should beginners start with?

Keras is the most direct starting point, since its high-level API handles most of the boilerplate involved in building and training a model. PyTorch is a close second and is worth learning early for anyone planning to move into research or NLP work, since most tutorials, courses, and pre-trained models in that space assume PyTorch.

Can a project use more than one deep learning framework at the same time?

Yes. ONNX exists specifically to let a model trained in one framework run in another. Also, Keras 3 runs the same model code on TensorFlow, PyTorch, or JAX depending on which backend is selected.

Combining frameworks is common in production systems, where a team trains in one framework and deploys through a runtime built for a different target environment.

What framework do most large language models use?

Most current large language model training runs on PyTorch, often extended with a distributed training library such as DeepSpeed or Megatron-LM to handle the memory and compute requirements of models too large for a single GPU. JAX is also widely used for LLM research and training at organizations built around Google's TPU infrastructure.

Do these frameworks require a GPU?

No framework in this list requires a GPU to run. All of them work on a CPU for small models, testing, and learning. A GPU or TPU becomes necessary once a project moves to training on a real dataset, since CPU-only training on anything beyond a small model is usually too slow to be practical.

Getting Started with Deep Learning Frameworks

Online Courses and Tutorials

Numerous online portals provide complete courses and tutorials specifically geared to teaching deep learning frameworks. Popular choices include:

• Coursera provides several courses on deep learning foundations and specialized frameworks. Their courses are changing over time, it is better to check their websites for the latest course offerings.
• Udacity offers Nanodegree programs that include hands-on projects to help students obtain real experience using deep learning frameworks.
• Kaggle Learn offers free interactive courses and tutorials that apply deep learning to real-world challenges.

Interactive Coding Notebooks

Platforms such as Google Colab and Kaggle enable you to experiment with deep learning frameworks right in your web browser. These notebooks are preloaded with libraries and datasets, removing the requirement for local setup. This is an excellent method to test the waters and become acquainted with the framework's syntax before venturing deeper.

Community Forums and Resources

The deep-learning community is extremely active and supportive. Joining online forums like as Stack Overflow or framework-specific groups can be quite helpful. Here, you may ask questions, learn from experienced developers, and keep up with the latest developments.

Remember, the goal is to begin with the fundamentals and progressively expand your knowledge. Utilize these tools, try out alternative frameworks, and don't be scared to make mistakes. With dedication and practice, you'll be on your way to mastering deep learning frameworks and developing cutting-edge AI applications.

>> Read more:

• Top 9 Machine Learning Platforms for Developers
• Roadmap To Become A Machine Learning Engineer
• 12+ Machine Learning Project Ideas From Beginner to Advanced
• Unlock the Power of Machine Learning as a Service (MLaaS)

Conclusion

The 10 frameworks covered in this guide cover the vast majority of deep learning projects a team will build today, each suited to a different mix of task, deployment target, and team language.

A handful of other frameworks used to be common choices and are no longer under active development, including:

• Microsoft Cognitive Toolkit (CNTK): last released in April 2019.
• Caffe: unsupported since 2018, when its intended successor, Caffe2, was merged into PyTorch.
• Apache MXNet: formally retired to the Apache Attic in 2023.
• Theano: major development ended after its 1.0 release in 2017.
• Chainer: discontinued in 2019, when Preferred Networks moved its own development effort to PyTorch.

They're not bad frameworks, and code already built on them keeps running. But a team working with these inactive frameworks can face some troubles: fewer current tutorials and forum answers, no fixes for newer GPU drivers or CUDA versions, and no further security patches.

>>> Follow and Contact Relia Software for more information!