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lakshmilisted

Cost optimization for cloud infrastructure and LLM API spend — budgets, billing alerts, token usage, model selection. Use when discussing cloud costs, API bills, token usage, budgets, or when anything is called "expensive" or needs a cost estimate.
arjuncrevathi/asthra · ★ 0 · AI & Automation · score 68
Install: claude install-skill arjuncrevathi/asthra
# Lakshmi — Goddess of Prosperity (Cost Optimization) Lakshmi ensures wealth flows wisely: every dollar of cloud and API spend is deliberate. ## Cloud spend - Tag every resource with `service`, `env`, and `owner` so costs are attributable. Untagged spend is unaccountable spend. - Right-size instances from actual utilization metrics, not guesses. If CPU sits under 20% for a week, downsize. - Delete idle resources: unattached volumes, old snapshots, forgotten load balancers. Dev environments shut down nights and weekends (scheduled scale-to-zero). - Set billing alerts at 50%, 80%, and 100% of monthly budget, per account and per project. An alert at 100% only is an autopsy, not an alarm. - Prefer managed services (RDS, managed Redis, Vercel) until scale genuinely justifies self-hosting — engineer time is the most expensive resource. - Watch egress: cross-region and internet-out traffic bills quietly. Keep chatty services in one region; put static assets behind a CDN. - Review the bill line-by-line monthly; the top five line items usually hide at least one surprise. - Buy savings plans / reserved capacity only for proven steady-state load — never in month one. ## AI-native specifics (the big line item) - Choose the smallest model that passes evals. Run the eval suite (see `agni`) against a cheaper model before defaulting to the flagship; route easy requests to small models, hard ones to large. - Cache LLM responses: exact-match cache for deterministic calls, embedding-simila