AI engineering toolkit: split long documents into vector RAG chunks with overlap, calculate token costs against a dated model price registry, and scan prompts for common secret/PII patterns locally.
{{ c }}This is a cost multiplier on token counts you provide — it does not tokenize text for you. Use the RAG Chunker's char÷4 estimate, or your provider's own tokenizer, for real prompts.
| Model Name | Input Rate (/1M) | Output Rate (/1M) | Cost Per Call | Total Cost ({{ callsCount.toLocaleString() }} Calls) |
|---|---|---|---|---|
| {{ currentModel.display_name }} | ${{ currentModel.input_price }} | ${{ currentModel.output_price }} | ${{ perCallCost.toFixed(6) }} | ${{ totalCallsCost.toFixed(3) }} |
Source: {{ currentModel.source_url }} (verify before budgeting — prices change often)
Reuses the model and per-call cost from above — {{ mau.toLocaleString() }} users × {{ callsPerUserMonth }} calls/mo = {{ (mau*callsPerUserMonth).toLocaleString() }} calls/mo.
| Line item | Monthly cost |
|---|---|
| LLM API ({{ currentModel ? currentModel.display_name : '—' }}) | ${{ monthlyLlmCost.toFixed(2) }} |
| {{ h.service.provider }} | ${{ h.cost.toFixed(2) }} |
| Total / month | ${{ totalMonthlyCost.toFixed(2) }} |
Reference estimate from list prices only — free tiers, discounts and taxes are not modeled. Vercel additionally meters Active CPU time and Provisioned Memory with no free tier, which this estimate does not include, so a real Vercel bill runs higher than shown here.
{{ sanitizedPiiText }}LLM Developer Workbench provides AI engineers and prompt developers with a workspace to split long documents into vector RAG chunks, estimate token API costs against a dated multi-provider price registry, and scan for common secret/PII patterns locally.
Privacy: 100% Client-side. All document chunking, token estimation, and prompt sanitization run locally in your browser memory. Only the price registry JSON (public data, no user content) is fetched from this site's own server.
