Build vs Buy: Choosing How to Get AI Capabilities

There are three broad routes, not two. Buying off-the-shelf SaaS means paying for a finished product such as an AI writing assistant or support chatbot. It is the fastest to launch and needs little technical talent, but you get limited control over behaviour, share the same capability as competitors and depend on the vendor's roadmap and pricing. Building on an API means using a foundation model from a provider and adding your own prompts, retrieval over your documents, tools and user interface. This is where most custom enterprise AI work happens. It offers far more control than SaaS without the cost of training models, but you need engineers who can handle evaluation, prompt design, integration and monitoring. Data still flows to the provider, so contract terms on retention and training use matter. Building in-house models means training, fine-tuning or self-hosting models, often starting from open-weight models. It gives maximum control over data location, behaviour and unit cost at high volume, but requires scarce machine learning and infrastructure skills, GPU capacity and ongoing maintenance. A practical rule is to build where AI is core to your differentiation and buy where it is a commodity. Other deciding factors: how quickly you need results, whether your team can maintain what it builds, how strict your data rules are, and expected volume, since per-call API costs can exceed self-hosting at very high scale while being far cheaper at low scale. A common misconception is that building is cheaper because there is no licence fee. Total cost of ownership includes staff, evaluation, security, on-call support and keeping up with model changes. Another is that the decision is permanent. Many teams start by buying or using APIs, learn what matters, and bring pieces in-house later.