Project Token Budget Calculator

These are sample scenarios to help you sanity-check inputs.

• Raw input/request = Prompt + Context + Overhead
• Effective input/request = Raw input × (1 − Prompt reduction%)
• Total requests = Requests/day × Project days
• Retry multiplier = 1 + Retries%
• Safety multiplier = 1 + Safety buffer%
• Input tokens = Effective input/request × Total requests × Retry × Safety
• Output tokens = Output/request × Total requests × Retry × Safety
• Embedding tokens = Embedding tokens/day × Days × Retry × Safety
• Training tokens = Training tokens total × Retry × Safety
• Cost = (Tokens ÷ 1,000,000) × Price per 1M tokens

1. Start with realistic averages for prompt, context, overhead, and output tokens.
2. Enter request volume and project duration to compute total calls.
3. Set prompt reduction if you plan to shorten prompts or compress context.
4. Add retries for expected re-asks, tool failures, or timeouts.
5. Choose a safety buffer to cover peak usage and variability.
6. Enable embeddings or training only when those workloads apply.
7. Adjust pricing fields to match your provider and region.
8. Click Calculate Budget, then export the report as CSV or PDF.

Per request tokens are driven by prompt, retrieved context, overhead, and output length. Example: 700 prompt tokens + 900 context tokens + 80 overhead equals 1,680 raw input tokens. With 10% prompt reduction, effective input becomes 1,512 tokens. At 500 requests per day for 30 days, you run 15,000 requests. That schedule consumes about 22.7 million effective input tokens and 4.5 million output tokens directly. Record averages from logs weekly.

Retries and safety buffers prevent underfunding during spikes. If retries are 3%, multiply token totals by 1.03 before applying the safety factor. A 15% safety buffer then multiplies again by 1.15, producing a combined uplift of 1.1845. On 27.2 million baseline tokens, the buffered plan becomes roughly 32.2 million tokens. This extra headroom supports burst traffic, longer replies, and prompt bloat steadily overall. Align buffers with SLO targets.

Costs scale linearly with price per million tokens, so sensitivity checks are quick. Using $5.00 per 1M input tokens and $15.00 per 1M output tokens, 26 million input tokens cost about $130, while 6 million output tokens cost about $90. A 20% price increase raises total spend by the same 20% if usage stays constant. This makes vendor comparisons straightforward and supports budget approvals by component.

Embeddings and training can dominate workflows, so budgeting them separately reduces surprises. If you generate 200,000 embedding tokens daily for 30 days, volume is 6.0 million tokens before retries and safety. At $0.10 per 1M tokens, that component costs about $0.60, measurable for corpora. Fine tuning differs: 50 million training tokens at $8.00 per 1M equals $400 before buffers. Separate line items simplify reviews. Separate ingestion and query phases clearly.

Daily averages help capacity planning, but peak day tracking matters for throttling and quotas. Divide total cost by project days to estimate a steady burn rate, then compare it to peak windows like launches. If the budget is $300 over 30 days, the average is $10/day, yet a 3x spike day consumes $30. Exporting CSV supports audit trails, while PDF reports fit procurement and leadership updates during reviews.