A question that comes up more and more often in AI conversations is this: do we have to use models from OpenAI, Anthropic or Google, or are there other options?
For many businesses the default assumption is yes, if you want to use AI you have to choose a big provider. That is completely understandable. Their models are powerful, the platforms easy to access, and in many use cases they are the best choice.
But alongside the big commercial models, there is a growing ecosystem of open source AI models. These models are trained and made available for others to use for free. Meta, Mistral, Qwen and others have released powerful general-purpose models, while others have focused on smaller, faster models designed for more specific tasks. These models are incredibly capable and can be adapted, fine-tuned and deployed in your own private environment. There are several situations where this approach offers distinct advantages:
Data governance
Many businesses are rightly cautious about where their data goes, how it is processed, and who has access to it. Customer records, commercial, operational or financial data, or legal documents all need rigorous data controls. A private LLM deployed inside a controlled cloud environment gives you complete confidence about how your data is handled.
Cost control
With commercial AI models costs are linked to usage. Once AI starts to become embedded in day-to-day workflows usage can grow quickly. Recent price hikes by all the major providers have been steep (running some of Google’s models can now cost 30x more than they did just 18 months ago). Cost control is a key for every business, and running your own AI models is the one guaranteed way to ensure the AI services you embed in your business don’t suddenly become too expensive to run.
Customisation.
One of the most interesting advantages of open source models is the ability to adapt them to a specific business context. This might mean fine-tuning a model on examples from a particular domain, giving it a more consistent style, or making it better at a narrow operational task. This isn’t alway necessary, but in the right setting, fine-tuning can move an AI system from a useful tool to something that’s fully embedded in how a business operates.
Speed
Finally, not every AI task requires the most powerful frontier model available. Classifying emails, summarising documents, extracting information, matching records, generating structured outputs, routing enquiries or helping staff search internal knowledge can often be handled just as effectively by smaller, faster models.
Taken together, these are very compelling reasons to consider running your own AI models. There are trade-offs of course. Infrastructure has to managed, performance monitored, the system kept secure, and someone has to evaluate model quality and handle updates, all without disrupting the service. In addition, open source models are not always as strong as the very best commercial models on every single task.
The right answer depends on what the business is trying to achieve. But that is exactly the point – the decision should be driven by the use case, not by a default decision to go with a big provider, and I believe that anyone who is serious about taking their business to the next level needs to know about these options in order to get real value from their investment in AI.
