Posts about cost optimisation. ← All posts
What looked like an idiomatic BigQuery MERGE was scanning the full target table on every batch. The fix was syntactic, not architectural — and it was the single biggest contributor to a 57% data-warehouse cost reduction across the Tata Group engagement.
₹100 Cr / ~$12M in proven savings across a year-plus engagement. The four levers that did the heavy lifting, the lever I expected to win that didn't, and the post-engagement playbook that became a Searce managed service.
Most queries are simple. A cascading router tries a small/fast/cheap model first; if confidence is low or the task is hard, it escalates to a larger one. Costs collapse without hurting quality.
Not every question needs retrieval. A classifier gates RAG: chat or general knowledge questions skip it; factual or document-grounded questions trigger it. Saves latency and tokens on the simple half of queries.
Exact-match caching misses paraphrases. "What is the refund policy?" and "How do refunds work?" should both hit the same cached answer. Semantic cache embeds queries and matches by similarity.
Not every query needs the production agent. A cost-aware dispatcher decides whether to route to the cheap-and-fast agent or the expensive-and-thorough one. Same UX, dramatically lower bill.
Cross-cloud data movement is billed by the GB. The bill is invisible until it isn't. A multi-region or multi-cloud architecture that doesn't model egress costs in design will discover them in production.