LLM vs Traditional Machine Learning: Which Does Your Use Case Need?
Key takeaway
Use an LLM for language-heavy, flexible tasks like summarizing, drafting, and answering questions over documents. Use traditional machine learning for structured prediction at scale — fraud scoring, forecasting, recommendations. Many production systems combine both.
"Should we use an LLM or classic ML?" comes up in almost every AI project. They're not competitors so much as different tools for different shapes of problem, and choosing well saves both money and latency.
What LLMs are good at
- Understanding and generating natural language: summaries, drafts, rewriting, classification with nuance.
- Answering questions over your documents (retrieval-augmented generation).
- Flexible tasks where the rules are hard to specify but easy to demonstrate with examples.
What traditional ML is good at
- Structured, numerical prediction at scale: fraud scoring, churn, demand forecasting, pricing.
- Low-latency, high-volume inference where every millisecond and cent matters.
- Problems with clear features and lots of labeled historical data.
Cost and latency trade-offs
LLM calls are more expensive per request and slower than a tuned classical model, so pushing every prediction through an LLM rarely makes sense at scale. Conversely, forcing a language task into a classical pipeline usually means brittle feature engineering. Match the tool to the task.
A simple decision rule
If the input or output is primarily language and the task benefits from flexibility, start with an LLM. If you're predicting a number or a category from structured data at high volume, start with traditional ML. When in doubt, prototype the cheaper option first and measure. We help teams design the right architecture — often a hybrid — around real constraints.