Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Llama 4 Maverick (400B MoE) delivers a 27% cost reduction over Databricks DBRX Instruct.
| Traffic Volume Tier | Llama 4 Maverick (400B MoE) Monthly | Databricks DBRX Instruct Monthly | Monthly Savings by picking Llama 4 Maverick (400B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $1.213 | $1.65 | Save $0.4375 / mo |
| 10,000 reqs/mo (Small App) | $12.125 | $16.50 | Save $4.375 / mo |
| 100,000 reqs/mo (Growth Production) | $121.25 | $165.00 | Save $43.75 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $1,212.50 | $1,650.00 | Save $437.50 / mo |
Llama 4 Maverick (400B MoE) is 27% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Llama 4 Maverick (400B MoE) costs $0.001213 per request compared to $0.00165 on Databricks DBRX Instruct.
Llama 4 Maverick (400B MoE) offers a context window of 1,000,000 tokens (max output: 16,384), while Databricks DBRX Instruct offers 32,768 tokens (max output: 4,096).
At 100,000 requests per month, using Llama 4 Maverick (400B MoE) saves $43.75 every month (or $525.00 annually) compared to Databricks DBRX Instruct.
Volume makes nano-tier models attractive — extraction rarely needs frontier reasoning. Constrain output with JSON schema modes to avoid retry loops on malformed responses. Cache shared schema instructions and few-shot examples across calls.