I gotta say, i Built a DeepSeek API Service with FastAPI: Here's the Data
I'll be honest — I didn't set out to write about DeepSeek and FastAPI. What I actually wanted was a sane way to ship LLM features without my cloud bill doubling every quarter. After a few weekends of tinkering, I ended up with a small FastAPI wrapper around DeepSeek models routed through Global API, and I learned a few things the hard way. This post is my field notes, complete with the numbers I wish someone had shown me upfront.
A quick disclaimer before we start: the sample sizes in some of my experiments are small (n=20 to n=50 per query type), so treat any "correlation" you see in the charts below as exploratory, not causal. The pricing data, however, comes straight from the Global API catalog and is exact as of the time of writing.
Why I Even Looked at This
My team runs a modest document-processing pipeline. We summarize, classify, and extract structured fields from roughly 40,000 documents a month. For the longest time we were calling GPT-4o directly, because, well, it works and nobody wanted to be the person who broke production with a cheaper model. Then I started looking at the invoice.
