Cutting LLM costs by 70% without losing quality
A practical playbook of caching, model cascading, retrieval, and budget alerts — with real numbers from production RAG apps.
Most teams treat LLM costs as an inevitable tax. They aren't. After shipping a dozen RAG systems we've learned that 60–80% of inference spend can be eliminated without measurable quality loss, and the rest can be governed so it never spikes.
This article walks through the patterns that actually move the needle: request-level caching, semantic caching for ambiguous prompts, model cascading from GPT-class down to OSS models, retrieval over curated corpora, and observability with per-tenant budgets. We share the dashboards we ship with every AI build and the alerts that fire before a customer notices.
Aaftab Patel
Co-founder & CTO
Keep reading
More from the team.
Want this in your inbox?
One engineering email a month.
No fluff, no roundups. Practical patterns, real numbers, and the occasional war story.