The tech industry faced something of a reckoning in late 2022 with the arrival of OpenAI’s ChatGPT, a simple chatbot that could answer questions, seemingly by magic.

The early frontier large language models (LLMs) from OpenAI and rivals like Anthropic and Google could reason, solve problems, and answer detailed queries put to them using natural language. But no one knew exactly what was in the LLM black boxes underpinning the services.

Soon, AI models such as Meta’s Llama and Mistral came along to break the proprietary AI monopoly. These kinds of models could be freely downloaded and modified to fit specific applications.

In late 2024, Chinese firm DeepSeek released V3, an open model reportedly trained at a fraction of the cost US frontier labs spent. A reasoning model, R1, followed in January 2025 and was seen as a legitimate threat to frontier models.

During the last year, open models such as Alibaba’s Qwen and Moonshot’s Kimi have also been gaining ground  among enterprises for reasoning, agentic and physical AI. “The result is a clear trend: with each generation, open-source models take half as long to catch up to the first closed-source model of the era,” research firm SemiAnalysis explained in a newsletter this month.

Open model variations explained

Many versions of “open” AI models are now floating around, including “open-weight” models and “open-source” models. Though they sound similar, it’s important to understand how they differ.

The most common “open” models used in enterprises are the open-weight models. These let companies customize AI services and tools to their own internal requirements. Corporate leaders and IT decision-makers can see inside the model, audit it, and tune it to their own specific data.

Open-weights are the parameters processed by mathematical techniques to produce an output. Enterprises can customize a model by fine-tuning the weights, adding their internal data, and deploying it in-house. “Open-weight models help provide that assurance by allowing organizations to control their own data, evaluate and adapt models to their own needs, and deploy them wherever their business requirements demand,” Nvidia CE Jensen Huang said in a letter released last month.

But — and here’s the main difference — open-weight models hide information such as code and training data, so some parts can’t be modified. According to the Open Source Initiative (OSI), a truly open-source model also releases the data it was trained on, along with other information allowing those models to be studied, inspected, used, modified and freely distributed.

Enterprise use for open models 

To be sure, the likes of ChatGPT, Google’s Gemini, and Anthropic’s Claude provide well-rounded AI capabilities; they aren’t going anywhere anytime soon. But the services are expensive and could be overkill for specific corporate uses.

Many enterprises can be better served by a small language model (SLM) or LLM that’s focused on their specific needs. Open models are blank canvases on which enterprises can paint their workflows, said Deepak Seth, senior director analyst at Gartner.

“An enterprise’s real needs sit in specific workflows with specific data, and a general-purpose closed model trained on the entire internet is overkill for most of them,” Seth said.

China is a proponent of open source and open weight models as it increasingly becomes an AI rival to the US. Other major economies, including Germany, France and India, are also encouraging the adoption of open models.

Companies such as ServiceNow and RWS have deployed dozens of open-source models (in addition to proprietary models) that specialize in specific tasks,. “We shouldn’t be afraid to adopt a multi-vendor approach if we think that we can get value from different AI tools rather than risk the lock-in of having a single AI tool,” said Max Goss, research director at Gartner.

With physical operations, such as in a vehicle or on-site, decisions need to happen in milliseconds — sometimes directly on the device. That favors models that are purpose-built, efficient, and able to run close to where the data is generated, said Praveen Murugesan, vice president of engineering at Samsara.

“Open models make that layered deployment possible because the enterprise controls where each model runs and what data it touches,” Murugesan said.

Governance and control as key assets

As more companies embrace AI, uptime is becoming important to keeping workflows humming. Open-source models give enterprises more control over their AI future — and protection against vulnerabilities exposed in proprietary LLMs.

Securing and controlling AI systems and data is part of the governance needed to successfully deploy AI within enterprises. “You actually build the boundaries around it. So the responsible AI is built in,” said Jinsook Han, founder and partner at Spruce Peak Ventures.

Open models can be run internally, cut off from the cloud. That provides additional security, especially for regulated industries where data control is paramount. “There’s definitely a gravitation towards comfort level on more of an open model and relying on, ‘Hey, I want everything on-prem. I want this on the system of work,’” Han said.

Companies are going to want models trained on their proprietary information, and will need solid control over the tools because of IP leakage worries, said Craig LeClair, vice president and principal analyst at Forrester Research “Open source models will be run in controlled on-premise environments, which just makes them less open source pretty quickly,” LeClair said.

Open models allow enterprises to “inspect the weights, audit the training data, or air-gap the deployment,” Gartner’s Goss said. “You can’t govern what you can’t see.”

AI and digital sovereignty

Open models can help countries customize AI to meet indigenous customs, traditions, policies and regional regulatory constraints. “Open models are important to sovereign AI so nations can understand, adapt, and control systems powering digital infrastructure,” said Richard Morton, vice president and managing director at the Abu Dhabi-based Institute of Foundation Models at Mohamed bin Zayed University of Artificial Intelligence.

Open models also do a better job of innovating based on localized knowledge and requirements, said Kari Briski, Nvidia’s vice president of generative AI software. “Open models and open data are that bootstrap: you don’t have to recreate capturing the knowledge of the internet as a pre-training model,” Briski said.

The downsides to going open

Proprietary technologies can stifle innovation, even as they provide a higher level of security than open technologies, said Jack Gold, principal analyst at J. Gold Associates.

And while open models are available from major service providers, their deployment, maintenance and updates in many cases fall in the hands of enterprises.

Beyond that, open models might not always be completely vetted, introducing a level of risk that proprietary information in a model could be leaked beyond the borders of an enterprise, Gold said.

For example, while there’s been plenty of interest in OpenClaw, which can scan file systems, access personal information and communicate with LLMs, the agents could create new attack surfaces; that’s why top technologists are experimenting carefully before deploying in the enterprise. 

“We are a big believer in open source and we are super excited to see the ecosystem around these open-source models build and thrive. In the fullness of time, these open-source models will have their own space,” said Samar Abbas, co-founder and CEO of Temporal.

Ultimately, enterprises need an inventory of every model and agent, sanctioned or shadow, with a clear view of what data each one is accessing, Abbas said. “Some workflows need a frontier-class closed model. Many do not, and an open model fitted to internal data will outperform a horizontal closed one.”