Choosing the Right AI Model: Foundation vs. Custom Solutions

If you’ve ever felt overwhelmed by the choices in today’s AI world, you’re not alone. Foundation models like GPT and CLIP have become popular because they work across a range of tasks and are great for rapid prototyping. They’re large and pre-trained, which makes them versatile—but they can also be expensive, introduce latency, and raise privacy concerns. On the flip side, custom models built with your own data can offer more control, cost efficiency, and specialised performance. Sure, they might take longer to develop and need greater expertise, but they can deliver faster responses and better protect sensitive information. Often, a hybrid approach works best: for example, starting with a foundation model to kick off data labelling or bootstrapping before shifting to a custom setup can balance speed with precision. Ultimately, the best choice depends on your project needs, resources, and long-term goals.