I Cut RAG Costs 65% With DeepSeek + ChromaDB — Full Data
Last quarter my team burned through $14,800 on a single RAG workload. That's not a typo. I stared at the invoice like it owed me money, and honestly, it kind of did. So I did what any data scientist with a grudge would do — I spent six weeks running benchmarks across every model I could get my hands on through Global API. 184 models. Same questions, same retrieval corpus, same evaluation harness. What follows is the unfiltered breakdown.
A quick note before we dive in: every price point below comes straight from the Global API catalog at the time of writing. I'm not editorializing on cost, just reporting what the data told me. Sample size for my benchmark runs was n=500 queries per model, repeated three times to control for variance. Standard deviation stayed under 4% on latency measurements, which gave me reasonable confidence in the averages I'm about to share.
The Cost Problem Nobody Talks About
When people say "RAG is expensive," they're usually hand-waving. Let me give you the actual numbers from my November billing cycle. The baseline stack I inherited was a flagship OpenAI-class model pulling from a vector store, no caching, no routing, just pure brute force generation. Per million tokens at scale, the math gets brutal fast.
