At 200 requests/user/day (totaling 3,000,000 queries/mo), running Data extraction & tagging on Llama 3.1 405B Instruct costs $13,125.00 / month ($26.25 / user / mo).
Total expected API invoice for 500 Active Users generating 3,000,000 queries.
Direct inference cost per MAU to model your SaaS pricing tiers and gross margins.
Savings generated by exploiting 20% cache hits on prompt context.
| Model | Provider | Monthly Spend (500 Active Users) | Cost / User / Mo | Annual Spend | Context Limit |
|---|---|---|---|---|---|
| Llama 3.1 405B Instruct (Current) | meta | $13,125.00 | $26.25 | $157,500.00 | 128,000 |
| GPT-5.6 Sol | openai | $47,100.00 | $94.20 | $565,200.00 | 1,050,000 |
| GPT-5.6 Cyber | openai | $117,750.00 | $235.50 | $1,413,000.00 | 1,050,000 |
| GPT-5.5 Standard | openai | $47,100.00 | $94.20 | $565,200.00 | 512,000 |
| GPT-5.5 Pro | openai | $282,600.00 | $565.20 | $3,391,200.00 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $158,400.00 | $316.80 | $1,900,800.00 | 1,000,000 |
At 200 queries per user/day with Llama 3.1 405B Instruct, the estimated cost is $26.25 per monthly active user (MAU). Total monthly bill for 500 Active Users is $13,125.00.
Assuming a 20% cache hit rate on repeated system and context tokens, prompt caching saves $0.00 per month ($0.00/year) on Llama 3.1 405B Instruct.
With a direct COGS of $26.25 per user/month on Llama 3.1 405B Instruct, charging at least $131.25 per user/month ensures an 80%+ software gross margin.