Use-case preset
PDF extraction pipeline cost calculator
Extract structured fields from large PDFs in batch; output is compact JSON.
A batch pipeline that ingests large PDFs — invoices, contracts, reports — and emits structured field extractions. The prompt carries the full document text; the output is a compact JSON blob. That yields a 90/10 input/output ratio and a 32k context ceiling to accommodate long contracts without chunking. Latency is best-effort since extraction jobs run overnight or in a queue.
Because each document is unique, prompt caching provides minimal savings (5% here); the cost is dominated by raw input tokens. The highest leverage is model selection: a smaller, cheaper model that achieves 95% field accuracy beats a frontier model at 97% when you're processing thousands of PDFs daily. Run an accuracy benchmark on your document types before committing to a model. Also validate JSON output schema strictly — malformed extractions at batch scale are expensive to remediate manually.