# Why does a classifier cost less to run than an LLM judge?

Because one check does less work. A classifier tests a narrow property with a rule or a small trained model; an LLM judge reads the trace and generates a verdict. Training the classifier happens once, but running it on each new trace still uses compute. [Hosted inference is billed while it runs](https://huggingface.co/docs/inference-endpoints/pricing), even when the per-trace cost is small.

A classifier can run across the stream cheaply, but it only checks the property its rule or training defined. [An LLM judge](/answers/llm-as-judge/when-to-use-llm-judge) costs more per event and can judge whether an answer's reasoning holds up.

---

Sources:
- Hugging Face Inference Endpoints pricing: https://huggingface.co/docs/inference-endpoints/pricing (fetched 2026-09-25)

Source: https://tessary.ai/answers/eval-costs/why-does-a-classifier-cost-less-to-run-than-an-llm-judge
More on Eval costs: https://tessary.ai/answers/eval-costs
From Tessary, agent reliability for AI agents in production: https://tessary.ai