Simulating realistic Agentic workflow parameters (40,000 in / 1,500 out with 65% cache reuse). Llama 4 Scout (109B MoE) delivers a 85% cost reduction over Amazon Nova Premier.
| Traffic Volume Tier | Llama 4 Scout (109B MoE) Monthly | Amazon Nova Premier Monthly | Monthly Savings by picking Llama 4 Scout (109B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.675 | $45.20 | Save $38.525 / mo |
| 10,000 reqs/mo (Small App) | $66.75 | $452.00 | Save $385.25 / mo |
| 100,000 reqs/mo (Growth Production) | $667.50 | $4,520.00 | Save $3,852.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,675.00 | $45,200.00 | Save $38,525.00 / mo |
Llama 4 Scout (109B MoE) is 85% cheaper for Agentic workflow workloads. At standard Agentic workflow parameter ratios (40,000 input tokens, 1,500 output tokens, 65% cache hit), Llama 4 Scout (109B MoE) costs $0.006675 per request compared to $0.0452 on Amazon Nova Premier.
Llama 4 Scout (109B MoE) offers a context window of 10,000,000 tokens (max output: 16,384), while Amazon Nova Premier offers 1,000,000 tokens (max output: 32,768).
At 100,000 requests per month, using Llama 4 Scout (109B MoE) saves $3,852.50 every month (or $46,230.00 annually) compared to Amazon Nova Premier.
Prompt caching is the single biggest lever — each step re-reads prior context. Summarize or prune tool outputs before appending them to the transcript. Cap the step budget; runaway loops are the #1 surprise on agent invoices.