Open Source AI: Definition and 10 Platforms to Know

The open source approach of freely distributing software for the public to use, modify and improve upon allowed foundational technology like the internet and cloud computing to be made possible. Now, a similar strategy is fueling the development of artificial intelligence.

By making essential components like source code, model architecture and training datasets open and accessible, developers, researchers and curious newcomers can all explore how these systems work and adapt them for real-world applications. The result is a collaborative environment in which individuals can build on each other’s contributions and advance the artificial intelligence industry as a whole.

“The modern AI world is built on the power of an open community, where innovation is open and we share ideas and riff off each other and build on each other and make things,” Jonathan Frankle, chief AI scientist at Databricks, told Built In. “This community has probably been the largest driver of innovation in the field.”

What Is Open Source AI?

The term “open source” originates from a long-standing practice of developing and sharing technology that anyone can access, distribute and build upon. Open source AI applies those practices to the development of artificial intelligence, where essential components like training data, source code and the models themselves are freely available for public use and modification under open source licenses.

• Training data is all of the information an AI model learns from. When it is openly available, users can find out where this data came from, what it consists of and how it was collected and cleaned, enabling them to verify its quality and identify any biases.  
• Source code is the software used to train and run an AI model. When it is openly available, users can inspect the code to understand how the model works and tweak it to suit specific applications.
• Models are the computer programs that drive AI systems, enabling them to recognize patterns and make decisions. When models are open, details about their training process and structure are publicly available. The models themselves are also often shared, allowing users to freely deploy, examine and build upon them.

Exactly how “open” these components need to be for an AI system to qualify as “open source” is widely debated. In traditional software, the distinction between open and closed source is a binary issue based on licensing restrictions. But openness in AI exists on a spectrum — with models like OpenAI’s GPT-4o (which offers limited modifications through its API and no access to its innerworkings) on one end, and models like Allen Institute for AI’s Multimodal Open Language Model (whose training data, code and underlying architecture are all fully available) on the other. 

Somewhere in the middle are models like Meta’s Llama, Google’s Gemma and xAI’s Grok-1, which operate under quasi-open licenses that restrict user actions and only offer access to their weights (the parameters that guide their decision-making and outputs). Having open weights lets developers use, analyze and modify the model, but without the full source code or training data, they can’t fully understand why the model behaves the way it does.

“When people talk about open source AI, nine times out of ten they’re talking about open weights,” James Wang, director of product marketing at Cerebras, a chip company that develops its own open source models, told Built In.

Related ReadingWhy AI Should Be Open Source

How to Access Open Source AI

Open source AI models can run on your personal computer, assuming you have sufficient computing power (typically a decent GPU or other type of AI chip). You can either download pre-trained models from platforms like Hugging Face, or use frameworks like LangChain and Transformers to integrate them directly into your applications. You can also build and deploy your own AI models using the open source resources available on libraries like PyTorch and TensorFlow.

10 Top Open Source AI Platforms

Hugging Face

Hugging Face is a platform and community that helps users build machine learning models by providing the infrastructure to train, run and deploy AI applications.

• Maintains a library of more than a million open source models, which support tasks ranging from natural language generation to computer vision.
• Has a library of more than 200,000 sets of text, image, video, audio and even geospatial data.
• Developed its own open source language model called BLOOM, which primarily handles cross-lingual content creation and translation tasks.
• Makes it easy for users to share resources, models and research openly, helping to reduce model training time and resources.

TensorFlow

TensorFlow is an open source software library created by Google that helps developers build and deploy machine learning models on desktop, mobile, web, cloud and IoT devices.

• Offers a selection of pre-trained and research models that users can fine-tune and customize with additional data to perform new tasks.
• Offers several tools to gather, clean and process data at scale, including standard datasets for initial training, data pipelines for loading data, and tools to validate and transform large datasets.
• Supports multiple coding languages, including Python and JavaScript.
• Offers free tutorials, courses and certifications to help people learn the basics of AI development.

PyTorch

PyTorch is a framework based on the Python programming language and Torch library that is used for training neural networks. It was originally developed by Meta AI and is now part of the Linux Foundation, a non-profit that supports open source software projects.

• Has an extensive ecosystem of tools and models like TorchVision (for computer vision tasks), TorchText (for natural language processing tasks) and TorchAudio (for audio processing tasks).
• Uses Tensors (specialized data structures that run on GPUs) to encode the inputs, outputs and parameters of a model, helping to accelerate the computing process.
• Has its own automatic differentiation engine called “torch.autograd” to power neural network training, enabling it to have some of the fastest training times among machine learning frameworks.
• Supported by all major cloud providers.

Keras

Keras is a Python-based neural network library focused on building and training deep learning models.

• Supports convolutional neural networks and recurrent networks, as well as common utility layers like dropout, batch normalization and pooling.
• Runs on top of various frameworks, including TensorFlow, PyTorch and JAX.
• Can be used as a cross-framework language to develop custom components, such as layers, models or metrics.
• Offers dozens of deep learning models, along with pre-trained weights, that can be used for prediction, feature extraction and fine-tuning. 

Scikit-learn

Scikit-learn is an open source Python library designed for machine learning, predictive analytics and statistical modeling.

• Features various classification, regression and clustering algorithms, including support vector machines, random forests, and K-means.
• Provides various tools for model fitting, data preprocessing, model selection, model evaluation and more.
• Designed to interoperate with Python numerical and scientific libraries, including Pandas, NumPy and SciPy.
• Has an active community and extensive learning resources.

Together AI

Together AI provides a range of open source research, models and datasets. Its decentralized cloud services help developers, researchers and organizations to train, fine-tune and deploy generative AI models faster and cheaper.

• Provides access to more than 200 open models through serverless endpoints, including Llama 3, Stable Diffusion XL and Mixtral 8x22B, allowing users to fine-tune them.
• Builds custom models from scratch, starting from data collection all the way through to evaluating model performance against popular benchmarks.
• Offers high-end compute clusters for training and fine-tuning, which include Nvidia’s H100, H200 and A100 GPUs.
• Allows teams to easily share fine-tuned models, enabling them to collaborate on testing, analyze usage and set up API keys for each stage of the development process.

H2O.ai

H2O.ai is a fully open source platform that offers a range of algorithms and automated tools tailored for tasks like data preprocessing, feature engineering and model selection.

• Has a library of machine learning algorithms, including supervised and unsupervised learning.
• Operates its own generative AI tool to help users analyze documents, summarize content and generate new content.
• Offers a tool that can automatically label users’ data, as well as a tool to extract information from unstructured text data using intelligence character recognition and natural language processing.
• Has built-in intelligence to anticipate schemas of incoming datasets.

OpenCV

OpenCV (Open Source Computer Vision Library) is a library of AI algorithms with comprehensive computer vision capabilities.

• Offers thousands of algorithms for tasks like object detection, facial recognition, video analysis and more.
• Primarily designed in C++, along with wrappers in Java and Python.
• Runs on desktop operating systems like Windows, macOS and Linux, as well as mobile operating systems like Android, iOS and Maemo.
• Runs a community forum and offers several free courses.

LangChain

LangChain is an open source framework for building applications based on large language models, providing tools to improve their customization, accuracy and relevancy.

• Its use cases largely overlap with those of language models in general, including document analysis and summarization, conversational AI and synthetic data generation.
• Provides APIs with which developers can interface with both open and proprietary models.
• Enables the architecting of RAG systems, offering tools to transform, store, search and retrieve information that refine a model’s responses.
• Allows developers to include memory capabilities in their systems, including simple memory of recent inputs and complex memory to analyze historical messages to return the most relevant results.

ClearML

ClearML is an open source platform designed to automate, monitor and orchestrate machine learning development, from research to production.

• Allows users to integrate any machine learning, deep learning or language model, on any large dataset in any architecture with their existing AI framework or stack.
• Comes with optional commercial add-ons such as priority support, managed services, permission management and well-defined SLAs.
• Vendor and cloud agnostic.
• Supports on-premise, air-gapped, cloud and hybrid environments.

Related ReadingPyTorch vs. TensorFlow: Key Differences to Know for Deep Learning

Advantages of Open Source AI

Offers More Control 

Open source AI gives users full control over the model, allowing them to essentially own it and modify it on their terms forever. Closed models are more limited, and sometimes they go away completely. For example, OpenAI has stopped advancing its GPT-3.5 model, affecting the quality of all of the projects built on top of the model.

Open source models also offer greater control over how and where a model is deployed, which can enhance data privacy. By running models locally or on private cloud infrastructure, organizations can secure sensitive data without relying on third-party cloud services, as is often required with closed models, Frankle said. 

This kind of control extends to cost, too. Organizations have the flexibility to optimize open source models in ways that aren’t possible with closed APIs, Waseem Alshikh, chief technology officer at Writer, told Built In. They can “quantize the model,” making it more computationally efficient without sacrificing on accuracy. They can distill knowledge from a large model to a smaller one. “If you have the weights you can figure something out,” Alshikh continued. “It’s way cheaper with open source.”

Allows for Greater Customization

Open source allows users to tailor their models for specific use cases and industry needs. Companies can adjust their parameters, fine-tune them with additional data and optimize them for set tasks like predictive analytics, business automation and language translation.

Enhances Model Transparency

Open models let users inspect the code, training data and model structures that define how they work. But with closed models, this information is hidden in what is known as a “black box,” making it difficult for users to fully understand their inner workings, detect their biases or identify potential weaknesses. By opening up these inner workings, open source AI models make it easier for users to understand them.

“When things happen behind a set of closed doors and you rely on a commercial vendor, you actually can’t see and you don’t know what’s happening,” Bryan Castle, director of AI engineering at consulting firm Booz Allen Hamilton, told Built In. “The transparency that you get from open source [models] gives you a much greater degree of understanding about what they’re doing, what they’re good at, what their vulnerabilities could be.” 

This transparency doesn’t necessarily make open models more explainable, though. Even if all the code, data and weights are completely available for scrutiny, it can still be difficult, if not impossible, to comprehend exactly how and why a model behaves the way it does.

“It’s as if I cracked open your brain, peeked inside and mapped out every single neuron and looked at all the connections,” Frankle said. “Just because I’ve opened up your brain and looked at each of the neurons doesn’t mean I know you any better.”

Makes AI Development More Accessible

Open source AI helps make AI development more accessible, providing freely available resources and tools that reduce barriers to entry. 

With open source models, libraries and frameworks, virtually anyone — from hobbyists or trained AI engineers — can create their own AI products without significant financial investment or specialized expertise. And platforms like Hugging Face and TensorFlow offer robust documentation and community support, enabling newcomers to learn and experiment with AI at their own pace. 

“It provides equitable access for everybody,” Sophie Lebrecht, chief operating officer at Allen Institute for AI, told Built In. “You don’t need to be one of the few people that gets a high-paying job in one of these closed source companies. You can be self-taught, you can build your expertise.”

Promotes Community-Driven Innovation

Community plays a vital role in open source AI, fueling innovation through collaboration and collective problem-solving. When you use open source AI, you’re effectively tapping into a large, diverse network of developers who constantly contribute to the ongoing improvement of these tools, sharing information and building on each others’ work. 

“Having a community and ecosystem where people are learning with each other and building on top of each other is huge,” Lebrecht said. “Work done in the open — with a scientific approach, within an open community — leads to innovations and breakthroughs.”

Related ReadingWhy an Open Source Future Can Make AI Work for Creatives

Disadvantages of Open Source AI

Difficult to Monetize

For the companies making them, turning a profit on open source AI models can be challenging. Developing these models is costly — often reaching hundreds of millions of dollars — and it can be difficult to recoup those expenses since the models are typically offered for free.

To address the monetization challenges, some companies sell business-grade services and applications in addition to their open models, charging customers for enterprise features and support, ready-to-use apps like chatbots and custom development services. Others (like French AI company Mistral) offer a mix of free open models and more powerful closed models that require a fee or paid subscription to access.

Leads to a Loss of Control

While opening up models provides users with greater control, it effectively reduces the amount of control the organizations creating these models have over them. Completely open source licensing allows users to modify and redistribute models freely, making it impossible for companies to enforce restrictions on usage.

“Once you’ve put those weights out there, they’re out there, and it’s very hard to put the genie back in the bottle,” Frankle said. “Giving other people control increases their ability to do harmful things and decreases your ability to stop it.”

Potential for Misuse

Releasing powerful large language models and multimodal models to the public is not without risk. These models can be more easily adapted for nefarious purposes, such as generating misleading information, creating deepfakes and automating phishing attacks.

However, the open source AI community may also be a solution to this problem. By fostering collaboration and transparency, Lebrecht argues that this community can rapidly respond to emerging threats — perhaps even faster than the people working on proprietary models at big tech companies.

“This technology is out there. Now, the question is: Where do we want to be in a situation where something bad happens? We want to have a whole community of experts that can jump on that, and that can be building safeguards and solutions as fast as the bad actors are building,” she said. “We’re going to be in a better position if we can very quickly address any misuse.”

Frequently Asked Questions