Cloud models like OpenAI are large and expensive because they need to be good at everything. Your specific tasks don't require that level of complexity. By training smaller, open-source models on your data, you can deploy them efficiently and reduce costs significantly.

To make cloud models work well on complex tasks, you need to send them large prompts with many examples, which increases costs and inference time. By fine-tuning open-source models, you can train them on many examples, eliminating the need for long prompts during inference and reducing costs.

Cloud LLMs don't know your specific tasks and preferences. Training your own model helps it become the best at your task, like generating SQL queries for your unique database.

Sending all your users data to OpenAI is dangerous, keep your data inside your company infrastructure

For consistent model behavior, fine-tuning a small LoRA adapter is faster and more effective than complex prompting or repeated API calls.

Training the model on thousands of examples is more efficient than including them all in the prompt, allowing it to internalize patterns and generalize effectively.