Growth forecast
| Scenario | Monthly Cost |
|---|---|
| Current usage | — |
| +25% growth | — |
| +50% growth | — |
| +100% growth | — |
| Annual projection | — |
| 3-year projection | — |
Estimate OpenAI embedding cost and rough vector storage for PDFs, articles, knowledge bases, and support documentation.
Pricing reference date: 2026-06-19. Pricing fields are editable because API rates can change.
| Scenario | Monthly Cost |
|---|---|
| Current usage | — |
| +25% growth | — |
| +50% growth | — |
| +100% growth | — |
| Annual projection | — |
| 3-year projection | — |
| Metric | Value |
|---|
Embedding cost depends on total corpus tokens and reindex frequency. Storage depends mostly on vector dimension and metadata size.
This is a planning estimate, not a billing guarantee. Confirm current OpenAI prices and your actual usage dashboard before committing budget.
A 5,000-document knowledge base with 1,200 tokens per document has 6 million embedded tokens before reindexing.
This calculator estimates OpenAI API usage, cost, capacity, or system economics based on the values you enter. It is designed for planning, comparison, and rough budgeting before production deployment.
The calculator multiplies usage volume by editable pricing inputs, then adds any fixed, storage, tool, retry, or platform costs that apply to that specific calculator.
Default pricing fields are included as editable presets. Because API pricing can change, always check the official OpenAI pricing page before using the result for a budget or customer quote.
The estimate is only as accurate as your input assumptions. Real usage may vary because prompts, outputs, retries, caching, tools, and user behavior can change significantly.
Common cost reductions include using a smaller model, shortening output length, caching repeated prompts, reducing retrieved context, batching background jobs, and monitoring usage by project.
Common mistakes include ignoring output tokens, forgetting retry or regeneration costs, using average requests that are too low, and assuming cached pricing applies to every input token.
Use it before launching an OpenAI-powered feature, comparing models, estimating SaaS margins, planning RAG storage, or deciding whether a workflow is economically viable.