Should I use an LLM or a traditional ML model for classification?

For fewer than 20 categories with clear boundaries and high volume (10,000+ daily), a fine-tuned traditional ML classifier (BERT-based) is faster and cheaper. For many categories, nuanced distinctions, or low volume, an LLM with few-shot examples works well without training data. Many teams start with LLM classification to validate categories, then train a dedicated classifier once patterns stabilize.

Classifier

Written by Max Zeshut

Founder at Agentmelt · Last updated May 26, 2026

An AI model or component that categorizes inputs into predefined classes—such as sorting emails into 'sales inquiry,' 'support request,' and 'spam,' or labeling customer sentiment as positive, negative, or neutral. Classifiers are workhorses inside AI agent systems: they route incoming requests to the right agent or workflow, filter content for moderation, triage support tickets by priority, and categorize financial transactions. Classifiers can be rule-based, ML-based, or LLM-based.

Example

An AI support system uses a classifier as its first step: every incoming message is classified into one of 12 categories (billing, technical issue, feature request, cancellation, etc.) with a confidence score. High-confidence classifications route directly to specialized agents; low-confidence ones queue for human triage.

Frequently asked questions

Should I use an LLM or a traditional ML model for classification?: For fewer than 20 categories with clear boundaries and high volume (10,000+ daily), a fine-tuned traditional ML classifier (BERT-based) is faster and cheaper. For many categories, nuanced distinctions, or low volume, an LLM with few-shot examples works well without training data. Many teams start with LLM classification to validate categories, then train a dedicated classifier once patterns stabilize.

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