Fine-tuning vs Inference
Fine-tuning (Model Fine-tuning) and Inference (Model Inference) both come up in ai & ml conversations and get confused. Here's the plain-English difference, side by side, so you can use each one with confidence.
The key difference: Fine-tuning refers to model fine-tuning, while Inference refers to model inference — they describe different things even when they show up in the same sentence.
Fine-tuning — Model Fine-tuning
Continuing the training of a pre-trained model on your own data to specialize its behavior. Powerful, but often the wrong first move — prompt engineering and retrieval usually beat fine-tuning on cost and time-to-value.
Inference — Model Inference
The act of running a trained model on new input to produce output. Inference cost — not training cost — is what actually shows up on the cloud bill once a feature is live.
When to use Fine-tuning
Reach for "Fine-tuning" when the conversation is specifically about model fine-tuning. Continuing the training of a pre-trained model on your own data to specialize its behavior. Powerful, but often the wrong first move — prompt engineering and retrieval usually beat fine-tuning on cost and time-to-value.
When to use Inference
Reach for "Inference" when the conversation is specifically about model inference. The act of running a trained model on new input to produce output. Inference cost — not training cost — is what actually shows up on the cloud bill once a feature is live.
FAQs
What is the difference between Fine-tuning and Inference?
Fine-tuning stands for Model Fine-tuning — Continuing the training of a pre-trained model on your own data to specialize its behavior. Powerful, but often the wrong first move — prompt engineering and retrieval usually beat fine-tuning on cost and time-to-value. Inference stands for Model Inference — The act of running a trained model on new input to produce output. Inference cost — not training cost — is what actually shows up on the cloud bill once a feature is live.
Are Fine-tuning and Inference the same thing?
No. They're often used in the same conversation because they're related, but they describe different concepts. Fine-tuning = Model Fine-tuning. Inference = Model Inference.
When should I use Fine-tuning vs Inference?
Use Fine-tuning when you're specifically referring to model fine-tuning. Use Inference when the topic is model inference.