Break summarization cost into input and output
AI summarization cost starts with the source material sent to the model: a call transcript, chat, meeting, report or document. The requested summary, decisions, sentiment, action items and structured fields become output tokens.
A concise summary and a detailed analysis should not use the same output assumption. Test each format with representative content, then use the calculator's monthly interaction volume to compare cost per item, monthly spend and annual spend.
| Summary workload | Input to measure | Output to budget |
|---|---|---|
| Customer call | Speaker transcript and recurring instructions | Summary, sentiment and next actions |
| Meeting notes | Full transcript and meeting metadata | Decisions, owners, risks and follow-ups |
| Business document | Extracted document text | Executive summary and requested structured fields |
Use representative samples for monthly planning
Summary workloads usually vary in length, so one unusually short sample can understate the budget. Estimate a typical item and a long-item scenario, then compare both at expected monthly volume.
The calculator covers LLM processing only. Add speech-to-text, OCR, storage, retrieval, quality review and retry costs separately when they exist in the production pipeline. Chunking can help fit long inputs but may introduce additional model calls and a final synthesis step.
| Planning scenario | How to model it | Why it matters |
|---|---|---|
| Typical month | Average source length times normal volume | Establishes the working budget |
| Peak month | Longer content and higher interaction volume | Tests budget headroom |
| Chunked input | Chunk summaries plus final synthesis | Captures extra requests for long content |
Official Pricing Sources
Provider prices and billing rules can change. Verify the current rates and special pricing conditions before committing a production budget.