How much does AI classification cost at volume?
Tagging, triaging or routing incoming items to the right queue or category.
The short answer
On Claude Haiku 4.5, at 1,200 input and 50 output tokens per call. A short item plus a label list in, one label out. The volume is what makes this interesting: two thousand calls a month is a queue rather than a person.
Your numbers
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Your numbers
Rough numbers are fine. You are checking whether you are near the line, not being exact.
Classification, extraction, routing. Anything where the job is narrow and the volume is high.
Everything you send it
What it sends back
How often one user uses it
What it costs you
What drives the cost
Volume alone. Per call this is the cheapest thing in the list, and at scale it is not free. It is also the use case where moving down a model tier changes the arithmetic most dramatically.
Where the estimate goes wrong
Using a frontier model because the first prototype used one. Classification is a narrow job and the cheapest model in the list usually does it at parity, at a twentieth of the cost. Where the verdict here is negative, the model choice is almost always the reason.
What this does not price
A rules engine handles the easy eighty per cent of classification for nothing. The honest comparison is not model against nothing, it is model against rules-plus-model-for-the-remainder, which is both cheaper and more predictable than either alone.
Other features people cost
Customer support agent
A conversational agent answering customer questions against your help centre and ticket history.
Retrieval-augmented search
Semantic search over your own documents, with a generated answer and citations.
Document summarisation
Turning long uploaded documents into a short summary or structured extract.
Next step
If classification and routing is on your roadmap and the number here made you pause, that is exactly the conversation worth having before an engineer starts.