$50K/mo cloud waste eliminated
Mathematical VM rightsizing and intelligent context caching across 3 cloud providers. Token governance cut LLM API spend by 58%. Spend caps and metered billing guardrails deployed in 2 weeks.
First month savings exceeded full engagement cost.
Multi-cloud ML platform spending $80K/month on idle VM capacity and uncached LLM calls. The CFO had hit a quarterly cap and wanted answers.
We deployed the FinOps agent against their billing export and workload metrics. SLO-guarded rightsizing recommendations were auto-PRed as Terraform changes; engineers reviewed and merged. Token-cost analysis on top of LLM-served features identified four endpoints accounting for 71% of the spend; caching layers in front of those collapsed average tokens-per-call from 50K to under 1K.
Two weeks from kickoff, monthly spend was at $30K with no measurable user impact. SLO burn-rate alerts gated every rightsize decision; nothing applied without a guarantee of capacity to absorb traffic spikes.
It was the first time finance and engineering were looking at the same number for cloud cost.
Could this be your team?
Every engagement starts with a 30-minute scoping call. We'll walk through the data shape, blast-radius constraints, and which tier of advisory or product fits.