Question 1

What is data infrastructure FinOps?

Accepted Answer

Data infrastructure FinOps is the practice of continuously matching what you spend on a data platform, warehouse compute, storage, and AI infrastructure, to what the workload actually needs, instead of provisioning once and letting usage drift. It applies the FinOps discipline of visibility, optimization, and accountability specifically to Snowflake, BigQuery, Databricks, and the databases underneath them, rather than general cloud compute.

Question 2

How do you know if you're overpaying for Snowflake, BigQuery, or Databricks?

Accepted Answer

A few reliable signals: warehouse or cluster utilization sits well below half most of the day, the bill grows faster than query volume or data volume, nobody can say which team or pipeline is driving a specific cost line, and Reserved Instance or Savings Plan commitments were never revisited after the initial purchase. If billing questions get answered with a guess instead of a dashboard, there's usually room to cut.

Question 3

What typically drives the overspend?

Accepted Answer

Oversized warehouses or clusters running at low utilization, inefficient query patterns that scan more data than needed, unused or duplicated storage, and Reserved Instance or Savings Plan capacity nobody bought. Most companies overpay on cloud data platforms, and it accumulates quietly until the number gets uncomfortable.

Question 4

How do you right-size a Snowflake warehouse or Databricks cluster?

Accepted Answer

Start from actual query and job history, not the original sizing guess: track warehouse utilization and queue time in Snowflake, or cluster CPU/memory utilization and autoscaling behavior in Databricks, over a representative period. Downsize where utilization is consistently low and queue time is near zero. The harder call is spiky-demand workloads, where autoscaling policy tuning usually beats a fixed larger size.

Question 5

Reserved capacity vs on-demand — how do you decide?

Accepted Answer

Commit — Reserved Instances, Snowflake capacity commitments, Databricks committed-use discounts — once usage is stable and predictable enough to forecast a year out. Stay on-demand for workloads still growing unpredictably or for seasonal spikes you don't want to overcommit against. Committing too early locks in a number sized to last quarter's usage, not this quarter's, so we typically wait for a few months of stable utilization data before recommending a commitment.

Question 6

What does the engagement look like, and how long does it take?

Accepted Answer

The audit covers a full spend breakdown by workload, right-sizing recommendations, and a prioritized fix list. Implementation follows in phases, structured so the identified savings typically fund the engagement.

Question 7

How is this different from Database FinOps or Database Engineering's Cost sub-area?

Accepted Answer

Database Engineering's Cost sub-area covers RDS, Aurora, and other operational database right-sizing. Data Cost Optimization covers the platform layer above that: Snowflake, BigQuery, Databricks compute and storage, and AI data infrastructure spend. If your problem is a database bill, start with Database Engineering. If it's a warehouse or lakehouse bill, this is the right service.

Question 8

How is this different from hiring a full-time FinOps engineer or platform team?

Accepted Answer

A dedicated data platform FinOps hire is a significant full-time salary commitment, and most teams don't have enough ongoing optimization work to keep one fully occupied. We run the audit and implement the fixes in weeks, then offer a lighter-weight monitoring retainer instead of a full-time salary, so you get the cost control without the headcount commitment.

Question 9

Why isn't this just a one-time audit?

Accepted Answer

A cost audit is a snapshot; usage patterns and pricing tiers shift within a quarter. We scope the first engagement as an audit with a fixed set of fixes, then offer an ongoing retainer so someone is actually watching the bill after the initial cleanup, instead of it drifting back up six months later.

Question 10

How much does a FinOps engagement cost, and how is it priced?

Accepted Answer

The cost audit is typically priced as a percentage of the spend under review once we've seen your billing breakdown. Implementation is often structured so the fixes largely pay for themselves. The ongoing monitoring retainer is priced separately, sized to your platform footprint.

Question 11

How do you verify the savings are real, not just projected?

Accepted Answer

We check the actual bill after the fixes ship, not the audit's estimate. A right-sizing change that looks correct on paper can get undone by a config rollback, a new pipeline nobody scoped for, or a workload that grows back into the smaller instance within a month. Verification means the invoice reflects the projected number, across more than one billing cycle, before we call the engagement done.

Situation	Likely Solution
A quarterly cloud bill review flags Snowflake, BigQuery, or Databricks as a top-3 line item with no clear owner	Snowflake & BigQuery Cost Optimization — warehouse and slot right-sizing, query pattern review
A workload or team scaled up and nobody resized the warehouse or cluster back down afterward	Storage & Compute Optimization — workload scheduling to use committed capacity instead of on-demand
Finance is asking for a cost-per-team or cost-per-pipeline breakdown that doesn't exist yet	Databricks Cost Optimization — job-level compute cost attribution so you know which pipeline is driving the bill
A new AI/LLM feature is about to ship and nobody has scoped what vector storage and model serving will cost	AI Data Infrastructure Costs — vector storage, embedding compute, and model serving scoped as their own line item
Reserved capacity or committed-use discounts were never purchased, or expired without renewal	Storage & Compute Optimization — reserved capacity and committed-use discounts evaluated and applied

Before	After
Warehouse or cluster sized for peak, running 24/7	Right-sized to actual workload, scheduled around usage
Nobody owns watching the bill month to month	Retainer monitoring flags drift before the next invoice
Storage never tiered or cleaned up	Cold data tiered, duplicates and orphaned storage removed
AI/vector infrastructure cost buried inside general platform spend	Scoped and tracked as its own line item
On-demand credits at list price	Reserved or committed capacity where usage justifies it

Approach	What you get	The gap
Native cloud cost dashboards	Visibility into current spend	Shows what's happening, not what to fix or who implements it
Generic FinOps SaaS tool	Dashboards, recommendations, alerts	Someone still has to interpret and implement every recommendation
In-house FinOps/platform hire	Dedicated, ongoing ownership	A full-time salary commitment most teams don't have enough recurring optimization work to fully justify
DharmOps	Audit, implemented fixes, and an optional monitoring retainer	Fixes get implemented, not just recommended, without a full-time hire

Data Cost Optimization

Who Needs Data Cost Optimization?

What Problems Trigger This Engagement?

Match Your Situation to the Fix

What Is Data Cost Optimization?

Why an Engagement, Not a Report

Reduce the Bill, Not Just Report On It

Ongoing, Because Usage Doesn't Stand Still

AI Infrastructure Costs, Covered From the Start

What DharmOps Builds

Snowflake & BigQuery Cost Optimization

Databricks Cost Optimization

Storage & Compute Optimization

AI Data Infrastructure Costs

Specific Use Cases

How a FinOps Engagement Works

Cost Audit

Prioritized Fix List

Implementation

Verification

Ongoing Cost Monitoring

Before → After

How This Differs From Alternatives