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Data & AI cost optimization
Snowflake, AWS, and AI spend you can see, attribute, and cut — and keep down after we leave.
What it covers
- Cost attribution: tagging and per-team, per-workload views across Snowflake, AWS, and hosted-model APIs, using CloudHealth or similar FinOps tooling and the providers' own usage data
- Snowflake: warehouse sizing and auto-suspend, query and clustering tuning, storage tiering, and retiring the pipelines nobody reads
- AWS: right-sizing, reserved and spot strategy, storage lifecycle policies, and the idle resources Terraform forgot
- AI spend: token accounting per feature, prompt caching, routing each request to the smallest model that does the job, and — when the numbers say so — self-hosting an open-weight model
- Budgets, alerts, and a monthly review so the savings hold
How an engagement runs
We start with thirty days of billing and usage data and produce a ranked list of savings, each with its effort and risk. Then we take the top items end to end and set up the attribution and alerts that keep the number honest.
What you get
A ranked savings plan with the first items implemented, cost attribution your finance team can read, budgets and alerts, and a short set of rules the team can apply to new work.
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