Open-source software (OSS) is software with publicly available source code that anyone can inspect, modify, and distribute. Unlike proprietary software, which is controlled by whatever company or group is responsible for its inception, OSS is developed collaboratively, allowing programmers worldwide to contribute improvements, fix vulnerabilities, and customize features. Many foundational technologies, including many web servers, operating systems, and programming frameworks, rely on open-source components.

The same principles that define open-source software have been extended into the field of artificial intelligence. Open-source AI applies these collaborative and transparent practices to AI models, but with some differences in how openness is defined and implemented.

Open-source AI vs. Open-source software

While open-source AI is often compared to open-source software, the two are not that same in structure, accessibility, and control: 

• Open-source software  

The source code is fully accessible and written in human-readable programming languages, making it possible to inspect, modify, and redistribute. Developers can freely adapt the software, create new versions, and even split off independent projects if they disagree with the original direction. This transparency ensures that users can verify security, fix bugs, and customize functionality to their needs. 

• Open-source AI  

Instead of traditional source code, AI models are built on complex numerical representations—such as weights and parameters—which are not what one might consider ‘readable’ by human standards. While some AI models are shared openly, they often exclude critical components like training data, limiting true transparency and customization. Additionally, the training process requires massive computational resources, meaning that control over initial AI development often remains with large organizations.

Closed-source AI describes artificial intelligence models and systems whose underlying code, training data, and architecture are kept proprietary. Unlike open-source AI, which allows public access and modification, closed-source AI is completely under the control of the organization that develops it. This approach ensures that the developer maintains full authority over updates, security, and distribution, but it also limits transparency and customizability. 

Many of the most well-known AI models, such as OpenAI’s GPT-4 and Google’s Gemini, operate under a closed-source framework. While this model can provide stronger security controls and consistent performance, it also raises some concerns (such as vendor dependency, lack of insight into how the models are trained, and potential limitations on innovation).

Open-source vs. Closed-source AI 

While both open-source and closed-source AI have advantages, they differ significantly in terms of accessibility and transparency: 

• Open-source AI  

The model’s code, architecture, and (sometimes) weights are made publicly available so that users can examine or adjust it as much as they want. As a trade-off, open-source AI may lack the enterprise-level support and security measures that come with proprietary models. 

• Closed-source AI  

The code and training data remain private, ensuring that only the developer can modify or distribute the model. This provides tighter control over security and compliance, making it attractive for enterprises with strict regulatory requirements. Unfortunately, closed-source AI limits user customization and increases reliance on a single vendor, and can make it difficult to verify how the model was trained. 

Closed-source AI keeps AI capabilities under a metaphorical lock and key; open-source AI throws open the door. Users have the freedom to not only use the tool as-is, they can also dive down deep into its internal structure to better understand how and why it functions as it does. They can then use that understanding to expand the AI’s functionality, customizing its capabilities to match unique uses and needs. 

This level of freedom is certainly valuable in and of itself, but it also helps support a more ethical AI landscape. When faced with concerns related to bias, accountability, data privacy, or other issues, users are encouraged to investigate and test on their own (instead of relying on the providers). At the same time, a broader base of contributors helps keep AI advancements more widely available to the general public, preventing the technology from being monopolized by a select handful of companies. This decentralizes the technology ecosystem and fosters increased competition to promote ongoing advancement.

Benefits of open-source AI 

In addition to the broader benefits of open-source AI, there are a number of specific advantages businesses can capitalize on. Consider the following: 

• Diverse use cases  

Open-source AI is used in everything from fraud detection and medical imaging to automation and customer service. Because the models are freely available, businesses can experiment with AI in ways that proprietary systems might not allow—making it easier to apply AI to specialized or niche needs. 

• Lower barrier to use  

Without licensing fees or restrictive contracts, open-source AI makes this kind of information technology (IT) accessible to more organizations. Startups, small businesses, and independent developers can all experiment with AI without massive upfront costs. Broader participation leads to more creative and diverse applications.  

• Community engagement  

Open-source AI benefits from a global community of programmers, researchers, engineers, and dedicated enthusiasts who actively contribute to its development. Collective efforts strengthen AI models through shared expertise, and help ensure that improvements continue even after its initial contributors move on to other projects. 

• Transparency and improvement  

Because open-source AI models are publicly available, organizations can examine how they work—identifying potential problems and taking a more direct hand in data security. More eyes on the code can mean flaws are caught faster, and increased openness builds trust by letting users see exactly how decisions are made. 

• Vendor neutrality  

Businesses using open-source AI are not tied to a single provider. They have the freedom to choose the tools and infrastructure that best meet their needs. This counters the risks of vendor lock-in, sudden pricing changes, and ineffective support. 

• Customization  

Generally speaking, one-size-fits-all solutions don’t fit anyone perfectly. Access to the source code empowers organizations to tailor AI to their specific needs—adjusting outputs to align with company policies and unique operational requirements. This level of flexibility is not typically available in closed-source alternatives. 

Challenges of open-source AI 

There are potential downsides to opening up AI algorithms to public users. These might include: 

• Risk of misalignment and failure  

Without clear objectives, open-source AI projects can become unfocused, leading to wasted resources and unsuccessful outcomes. Organizations should establish evaluation metrics and ensure they have the right expertise before committing to AI development. Structured project management and iterative testing can likewise help keep efforts on track.  

• Additional potential for bias  

Just as a wider set of users can help identify bias, they can also unintentionally introduce it. Since training datasets are often incomplete or skewed, organizations should rigorously audit AI outputs and retrain models with more representative data. Regular bias testing and input from a diverse group of stakeholders can improve the AI’s fairness and reliability.  

• Security concerns  

The open nature of these models means that it is possible for unscrupulous users to manipulate or misuse them for harmful purposes—generating misinformation, creating harmful content, or even automating attacks. To counteract this, organizations should monitor usage and apply ethical safeguards when deploying AI solutions. The open-source community can also play a role by developing and sharing security measures to help reduce risks. 

• Data-related problems  

Issues such as mislabeled data, data drift, and unreliable sources can degrade AI performance over time. Rigorous validation processes can be applied to maintain data quality. 

• Issues with outsourcing  

Businesses using open-source AI from third parties must be cautious; if an external project is abandoned or fails to meet security standards, companies relying on it may face disruptions. In-house expertise is always recommended, but where outsourcing is the better option, ensure that all contractors are fully vetted.

• Difficulty monetizing  

Developing open-source AI is expensive, and giving it away for free makes direct monetization difficult. The companies that develop these tools may choose to address this concern by offering paid enterprise versions, support services, or additional proprietary tools built around open-source models.

• Loss of control  

Once an AI model is released under an open-source license, the original developers lose control over how it is used or modified. While this decentralization is a core benefit of open-source AI, it also means that models can be repurposed in ways that go against their creators' intentions. To balance openness with responsible use, organizations can establish ethical guidelines and continue to participate in the broader AI community to help shape responsible development.

Using open-source AI starts with selecting the right model. Pre-trained options are available on platforms like Hugging Face, allowing users to download and deploy them with minimal setup. Developers can integrate these models into applications using frameworks like LangChain and Transformers, which simplify interaction with AI-powered tools. For those looking to build or fine-tune models, libraries such as PyTorch and TensorFlow provide extensive resources for training, optimization, and deployment. 

Just be aware that running open-source AI effectively requires sufficient computing resources. While some smaller models can operate on personal devices, more complex systems often need powerful hardware or cloud-based infrastructure. Organizations can choose to run AI locally for greater control and security—or take advantage of remote computing options to handle larger workloads efficiently. 

Open-source AI represents unrestricted possibilities, but only if organizations have access to the right tools to harness its potential. The ServiceNow Platform^®—ServiceNow’s award-winning solution—delivers a unified AI-powered foundation designed to help businesses integrate, automate, and optimize all their workflows. Organizations can connect open-source models to enterprise processes, automate repetitive tasks, and enhance decision-making with AI-backed insights. And, with more than 20 years of experience in the workflow automation space, ServiceNow is the most reliable choice for improving efficiency across departments. 

Perhaps best of all, ServiceNow also contributes directly to open-source AI innovation with StarCoder2, a family of open-access large language models (LLMs) developed in collaboration with Hugging Face and NVIDIA. These models support code generation, workflow automation, and text summarization, helping businesses accelerate software development and AI-enhanced decision making. Organizations can fine-tune these models with industry-specific data, ensuring AI aligns with their unique requirements. These and other advanced capabilities are among the many reasons why 85% of Fortune 500 companies use ServiceNow.  

Establish your business at the forefront of open-source AI—schedule a demo today to see how!