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How open source AI models benefit developer innovation

By Stephen J. Bigelow

Few concepts inspire innovation in software development like open source. The open source paradigm empowers developers at all levels to participate, collaborate, create, refine and support their ideas in an open forum. The resulting open source software is often more useful, versatile, robust and effective than comparable proprietary projects.

Consider the effect of open source projects like Linux, Kubernetes and Docker on modern software development and operations. It's unsurprising that the open source approach extends to machine learning (ML) and AI. Open source projects such as TensorFlow and PyTorch have long provided vital tools to accelerate ML. Now, open source efforts embrace AI models like Llama, Phi-4, Mixtral and others.

The use of open source AI models is widespread across the AI technology stack. A 2025 report from McKinsey and Company said 63% of 703 responding organizations use open source AI models. Among the reasons open source AI is so popular are lower costs, faster development time and time-to-market, greater customization and innovation, and freedom from vendor lock-in. These benefits explain why organizations are turning to this technology.

Benefits of open source AI models

Simply put, an AI model is software that uses algorithms trained with enormous data sets. When the model's trained algorithms process production data, the model can identify patterns, spot anomalies and make decisions with little to no human intervention. The difference is that open source AI models provide publicly available code, parameters and architectural details that developers can freely use, modify and redistribute.

The specific benefits of open source AI models include the following:

• Lower entry costs. Open source AI models are typically free. This lets developers and businesses try different models or test innovative new ideas with less financial risk than proprietary models.
• Code integrity. Public access to the model's underlying source code, model weights, architecture and parameters enables close examination. This helps developers identify bugs, such as software defects, bias in the algorithms and missing functionality, like weak security, more easily than with proprietary AI models. It also offers greater accountability and trust, letting organizations strengthen adherence to ethical standards and prevailing regulations for AI use.
• Collective effort. Diverse global communities of developers and experts back open source AI models. This collaboration and diversity create better, more fully functional AI models faster -- often with faster updates and enhancements -- than proprietary AI models.
• Flexibility. Developers can modify and optimize open source AI models for the specific needs of individual AI projects and data sets. This enables better model performance and superior decision-making from each resulting AI system.
• Independence. The availability of multiple open source AI models lets developers try different open source models, tools and platforms without worrying about vendor lock-in. For example, open source users aren't tied to a specific vendor's software stack, feature set or future roadmap.

Overcome challenges to open source AI implementation

Although open source AI models offer many benefits, businesses and technology leaders must consider the technical challenges involved in these AI models. Some of these challenges include the following:

Technical expertise

Open source software often receives light support and training compared to what traditional proprietary vendors offer. Organizations must engage expert AI model development teams that understand AI model deployment, integration and maintenance. Open source communities can provide some support, but beyond common documentation and examples, community support can be incomplete or incompatible with organizational security standards.

Data quality and availability

Open source AI models are often pretrained. This can ease the data burden for businesses, but it's important to evaluate the training data and algorithms for incomplete, inaccurate or biased content. Poor data quality can lead to AI model performance that's unfair, discriminatory or outright inaccurate. Further, unknown or untraceable training data can result in compliance violations. A skilled data science team might need to validate the open source AI model before using that asset in an AI project. Or the team might need to modify, retrain and fine-tune their model to ensure it provides optimal outcomes with production data.

Another problem for any AI model is the availability of appropriate data. Some businesses might lack access to enough quality data to train or fine-tune an open source AI model, leaving the business with the burden of collecting and curating quality data for the open source AI model. This is especially true in cases where developers modified the model for specific business verticals or use cases.

Infrastructure resources

As with any AI project, using an open source AI model can require significant computing resources, which can tax the infrastructure of smaller organizations. Cloud computing can alleviate many resource constraints, but it requires skill in cloud resource provisioning and management, and can carry unexpected cloud computing costs. Infrastructure availability and costs can also become a serious constraint when the AI project scales up with higher data volumes and greater user demand. Consider where the AI will run and the associated resources and costs involved.

Integration with other systems

An AI model rarely operates in a vacuum and must interoperate with other components, such as other AI models or agents, backend systems like databases and enterprise-specific platforms. Open source AI models share this concern, so it's important to consider how the model communicates with other components and what integrations the AI project requires.

Model integrity

Open source software is vulnerable to exploitation. Malicious actors can corrupt open source code or introduce back doors or other malware into it. Each new version or variation of the open source AI model has the potential to contain malicious elements. Software developers must carefully evaluate the open source AI model code for vulnerabilities in the code.

Licensing

It's important to carefully examine an open source AI model's license to ensure the model can perform its business purpose without violating the license terms. Although not a technical challenge, it's important to understand that the license can prohibit using the open source model in a proprietary software product. There also might be strict limits on how the resulting AI project is licensed and distributed. A legal team versed in open source software licenses can advise the AI project team on the considerations and limitations of the open source AI model's license.

Examples of open source AI models

There are hundreds of open source models available for various AI tasks such as transcription; chat; the creation and processing of audio, images and video; code creation; embeddings used to represent complex unstructured data; and reranking to change list orders.

Some popular examples of open source AI models include the following, alphabetized by category:

• Audio models. Audio provides the key element of AI's ability to meaningfully interact with humans. The broadest category of audio models is text-to-speech (TTS), which converts text, like the output from a large language model (LLM), into natural-sounding speech. Popular open source TTS models include Fish Speech v1.5, Kokoro TTS, Mozilla TTS and XTTS-v2. Generative audio, such as speech and music generation, is another growing interest. Some open source AI tools for music generation include Uberduck and YuE.
• Chat models. Interactive chat is a vital feature of AI technology. Businesses use these models in advanced support and helpdesk environments to answer user questions and offer users advice. While chat typically involves LLM capabilities, some open source models focus on chat functionality, like the DeepSeek, GPT-OSS, LibreChat, Lobe Chat and Rasa models.
• Code creation models. One of the premier use cases for AI is code generation software with minimal direct input from humans other than an input prompt. This is commonly referred to as vibe coding. Code creation models are often based on LLMs. However, several specialized open source AI models exist, including CodeGeeX, DeepSeek-Coder-V2, OpenAI Codex, PR-Agent, Quen 2.5 Coder 7B and Yi-Coder-9B-Chat.
• Embedding models. Embedding is a means of classifying complex unstructured data. Businesses use these models for AI tasks like semantic searches and text classification. Popular open source AI embedding models include Alibaba's Qwen3-Embedding-8B, Google's EmbeddingGemma and Microsoft's E5 models.
• Frameworks and libraries. These represent a broad set of tools for various AI tasks such as deep learning, model training, analyzing data, predictive modeling and natural language processing. Important open source frameworks and libraries include Google's TensorFlow, Hugging Face Transformers, Keras, Meta's PyTorch, and Scikit-learn.
• Image and vision models. Perception is critical to the advancement of AI, so developers can draw from open source AI models that focus on computer vision and imaging tasks. This forms the foundation for image recognition, video processing and activity recognition. They can also complete generative tasks like image and video creation. Notable open source image and vision models include LLaVA, OpenCV and Stable Diffusion.
• LLMs. These powerful AI models learn the rules, patterns and relationships of language tasks. Well-trained LLMs can summarize information, answer questions, translate languages, extract context and even generate content. Popular open source LLMs include Google's Gemma 2, Meta's Llama 3, Mistral 7B, OpenHermes 2.5, Phi-3 and Zephyr.
• Moderation models. AI often needs guardrails to identify and respond to questionable or unsafe content. While fine-tuned LLMs can offer some forms of moderation, there are also specialized AI models available to help moderate content. Open source AI moderation models include Content-Checker, Llama Guard 3, Mod-Guard and Modcandy.
• Transcription models. Transcribers specialize in converting spoken language into text. This can accelerate everyday tasks, such as note-taking, and can assist verticals, such as healthcare, to automate clinician notes. Noteworthy open source transcription models include Coqui, DeepSpeech, Kaldi, Mistral's Voxtral, Nvidia's Canary and Parakeet, and OpenAI's Whisper.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.