Simulating realistic RAG / search-augmented answers parameters (8,000 in / 500 out with 50% cache reuse). Groq LPU — Llama 4 Maverick delivers a 53% cost reduction over Cerebras — GPT OSS 120B.
| Traffic Volume Tier | Cerebras — GPT OSS 120B Monthly | Groq LPU — Llama 4 Maverick Monthly | Monthly Savings by picking Groq LPU — Llama 4 Maverick |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $3.175 | $1.50 | Save $1.675 / mo |
| 10,000 reqs/mo (Small App) | $31.75 | $15.00 | Save $16.75 / mo |
| 100,000 reqs/mo (Growth Production) | $317.50 | $150.00 | Save $167.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $3,175.00 | $1,500.00 | Save $1,675.00 / mo |
Groq LPU — Llama 4 Maverick is 53% cheaper for RAG / search-augmented answers workloads. At standard RAG / search-augmented answers parameter ratios (8,000 input tokens, 500 output tokens, 50% cache hit), Groq LPU — Llama 4 Maverick costs $0.0015 per request compared to $0.003175 on Cerebras — GPT OSS 120B.
Cerebras — GPT OSS 120B offers a context window of 128,000 tokens (max output: 8,192), while Groq LPU — Llama 4 Maverick offers 512,000 tokens (max output: 16,384).
At 100,000 requests per month, using Groq LPU — Llama 4 Maverick saves $167.50 every month (or $2,010.00 annually) compared to Cerebras — GPT OSS 120B.
retrieved-context dominates cost — tune top-k and chunk size before switching models. Re-rank and drop marginal chunks; halving context roughly halves input cost. Deduplicate repeated chunks across queries to raise the cache hit rate.