Cloud & AI Cost Optimization (FinOps)
Cloud and AI cost optimization: warehouse and pipeline cost audits, AI and LLM spend control, BI tool rationalization, and a FinOps operating model. Senior-led, self-funding.
What this service covers
- cloud cost optimization
- AI cost optimization
- FinOps consulting
- LLM cost optimization
- Snowflake cost optimization
- data warehouse cost
- cloud spend management
- BI license rationalization
Frequently asked questions
What is cloud and AI cost optimization (FinOps)?
Cloud and AI cost optimization, often called FinOps, is the practice of bringing financial accountability to variable cloud, data-warehouse, and AI spend. It combines a technical audit (finding waste in compute, storage, pipelines, queries, and AI token usage) with an operating model (cost allocation, budgets, alerts, and a review cadence) so spend maps to value and stops surprising finance.
How much can cloud and AI cost optimization save?
In a typical optimization sprint we recover 30 to 45 percent of cloud, warehouse, and AI compute spend, with the bulk coming from idle or oversized capacity, inefficient queries, duplicate pipelines, and unused licenses. The exact figure depends on how much governance already exists. Environments that have never been optimized usually see the largest first-pass savings.
How is FinOps different from just turning things off?
Turning things off is a one-time cleanup that drifts back within a quarter. FinOps is the operating model that keeps spend controlled: cost allocated to the team or workload that caused it, budgets and alerts in place, and a regular review so new waste gets caught early. The audit finds the savings; the operating model keeps them.
Do you optimize AI and LLM costs specifically?
Yes. AI spend is now one of the fastest-growing and least-governed line items. We instrument token and compute usage, right-size model selection, add caching and batching where real-time is not required, and set ceilings so agentic and GenAI workloads scale in capability without the cost scaling one-to-one with usage.
Which platforms do you work with?
We work across the major cloud and data platforms our clients run, including Snowflake, Databricks, BigQuery, Microsoft Fabric, AWS, Azure, and the BI tools on top (Tableau, Power BI). We are platform-agnostic: the goal is the lowest defensible cost for the workload, not a migration to whatever we resell.
What does a cost optimization engagement cost?
A focused cost audit for a single platform or domain runs the equivalent of a 3 to 5 week senior-led engagement and is usually self-funding from the savings it surfaces. A full FinOps operating-model rollout across teams typically lands in the 2 to 4 month range. We price by deliverable, not by hours bucket.
Request the 30-day Analytics Truth Audit to scope this engagement for your environment.