For data-platform leaders & CFOs
A faster, cheaper, safer warehouse. From the queries you already run.
Meaning, turned into action.
Builds the case. · Lands the change. · Measures predicted vs realized.
Spotonix Advisor learns how your data is actually used, turns it into ranked, dollar-quantified recommendations, lands approved changes through your own dbt workflow, and measures predicted vs realized.
Three outcomes a leader cares about
Spend less
Finds the waste no one has time to hunt — idle warehouses, redundant scans, materializations that cost more to refresh than they return — each as a dollar-quantified case.
Go faster
Speeds up the queries that actually matter to the business, ranked by impact, so dashboards and decisions stop waiting on data.
Stay in control
Nothing changes without approval. Every change is a reviewable dbt PR with an undo window — and it grades predicted vs realized, so no surprises.
The problem
You're optimizing the symptom.
Every cost tool shows you the bill — the symptom. None of them read the workload — the cause. So the bill climbs, no one has time to trace why, and your team optimizes on assumptions: which dashboards still matter, which pipelines earn their keep, which materializations pay back their refresh. Most teams overspend on purpose, because overspending feels safer than breaking something.
Spotonix Advisor reads the workload, not the bill.
What it does
Meaning, derived from how your data is actually used — turned into action.
The platform already knows what your data means (Context Graph) and how it's used (query-history fingerprint). Advisor is the layer that says: here's exactly what to do about it — ranked in dollars, with the evidence, and nothing changes until you approve.
How it works
01
Profile
Reads every query that actually ran — six months of history, resolved against your Context Graph. Schema, dbt lineage, BI assets, and query plans included.
02
Make the case
Turns the workload into ranked, falsifiable recommendations you approve — evidence + estimated ROI + confidence + blast radius + reversibility, in one Case Card. Not a tip. A contract.
03
Land & prove
Applies the approved change as a dbt PR, then measures the exact queries it targeted — before vs after, against a comparison group. Predicted vs realized.
Why you can trust it
Shows its work
Every recommendation is backed by your real usage and a dollar figure — not a generic best-practice tip.
Nothing changes without approval
Advisor recommends and simulates; a human merges the dbt PR. Every change has an undo window.
Grades its own forecasts
After each change it measures realized vs predicted against an agreed baseline — so the next number earns more trust.
- Observe Read-only profiling. No recommendations surfaced.
- Advise Ranked Cases with evidence and predicted impact. Nothing changes.
- Propose Each change drafted as a dbt PR. Your team approves every one.
- Autopilot A class earns delegation only after six on-band outcomes — within guardrails, with undo.
You decide how much you delegate. Rungs are earned per class, by measured outcomes — never switched on globally.
Why it's different
Optimizers act without understanding. Spotonix understands first.
A FinOps dashboard is a thermometer — it shows the bill. Autonomous optimizers act in the dark, on one vendor. Spotonix understands how your data is used, explains the fix in dollars, and acts only when you approve — across every engine, and on top of the engines' own native automation rather than fighting it.
| FinOps dashboard (monitoring) | Autonomous optimizer (autopilot) | Spotonix Advisor | |
|---|---|---|---|
| How fixes are found | a chart of last month's spend | a black box acts in the dark | every query captured & understood |
| Evidence per recommendation | a hover tooltip | none — it just acts | a one-page Case Card in dollars |
| Who approves the change | you, manually | the tool, automatically | you — via a dbt PR with undo |
| Grades its own forecasts? | No | No | Yes — predicted vs realized |
| Engine coverage | one engine | usually one vendor | engine-agnostic |
Illustrative comparison based on the Advisor preview operating model. Methodology and customer-specific figures in the pilot proposal.
How it fits your data stack
- Read-only to start. One warehouse credential plus your dbt project. No mutations until you opt in.
- Engine-agnostic. Works across Snowflake, Databricks, BigQuery, Redshift — not locked to one vendor.
- Lands in your workflow. Approved changes arrive as dbt pull requests your team reviews like any other.
- Value in days, not quarters. No rip-and-replace, no new home for your data.
One brain, two products
One graph. Two products. Analyst turns your data's meaning into answers; Advisor turns it into action. What one learns, the other reuses.
The pilot
Two phases. Read-only first. Change second — only if you opt in.
Phase 1 · Assessment · 30 days · read-only
- Read-only credentials to one warehouse + your dbt project. No mutations.
- Six months of query history captured and indexed against your Context Graph.
- Top ten ranked Cases, each with a Case Card (evidence + predicted impact + blast radius + measurement plan).
- You get: the ranked backlog, the asset ledger, an executive readout, and a go/no-go on Phase 2.
Phase 2 · Change sprint · optional · ~30 days · mutation under approval
- You select one or two low-blast-radius Cases from Phase 1.
- Advisor drafts each Change as a dbt PR. Your team approves before merge.
- After merge, Advisor records the first Health Record — predicted vs realized against an agreed baseline.
- You get: measured outcomes per Case, plus the engagement model for ongoing work.
“A FinOps dashboard tells you the bill. Spotonix Advisor tells you what to do about it — and then proves it worked.”