Small and distilled AI models (often a few billion parameters, trained to imitate larger "teacher" models) now handle most everyday business tasks at a fraction of the cost and latency of frontier models. Classification, extraction, summarising, routing and short replies rarely need the biggest model. For many AI features, choosing a small model is the single easiest way to cut costs.
What "distilled" means
A large model generates examples of good answers, and a smaller model is trained to reproduce them. The small model won't match the large one on hard reasoning, but on the narrow tasks it was trained for, it can get remarkably close, running faster and far cheaper, sometimes on a laptop or phone.
Where small models are enough
- Classifying support tickets, leads or reviews.
- Extracting fields from invoices, orders and forms.
- Summarising short documents and conversations.
- Routing requests to the right team or the right larger model.
- Rewriting text into a set format or tone.
- On-device features where privacy or offline use matters.
Where you still want a big model
- Multi-step reasoning, planning and complex coding.
- Long, nuanced writing.
- Open-ended questions over large, messy knowledge bases.
How to switch safely
- Build an evaluation set of 50–100 real examples with known good outputs.
- Run the small model against it and compare quality and cost with your current model.
- Use a cascade: try small first, and escalate to a bigger model when confidence is low.
- Constrain the output with structured formats (JSON schemas), so small models stay reliable.
- Monitor in production. Track error rates by model.
Many products end up with a mix: small models for 80% of requests and a frontier model for the rest. See multi-model routing for how that works.
Right-sized AI
At NextGen Fusion, our AI development company in Lucknow, we choose the smallest model that does each job well. That's how AI features stay fast and affordable at scale.
