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Cloud cost optimization built inside your data and AI platform, not bolted onto the invoice. Delivered through 1Platform accelerators and Polestar Analytics engineering teams.
Most enterprises already have tags, allocations and showback. The spend still climbs. What is left sits below the invoice, inside pipelines, queries, forgotten tables and AI workloads that a billing dashboard cannot see.
A scoped discovery across pipelines, jobs, tables, clusters and AI workloads that isolates the assets driving most of your cost.
Unified telemetry across ingestion, transformation and consumption, with health, usage and cost signals in one cockpit.
Right sizing, scheduling, caching and retention recommendations, validated with your engineers before anything changes.
Profiling and retirement of orphan and duplicate assets, with hygiene standards that stop the sprawl returning.
Policy automation, chargeback and a Value Realization Office that owns the savings number quarter after quarter.
Start with the baseline. Four to six weeks, one ranked opportunity list, no platform change required.
Most cost leakage is an engineering decision made months earlier. A data engineering practice that retires wasteful pipelines and governs AI spend is, in effect, a FinOps programme.
Token pricing, volatile GPU consumption and idle AI workloads break assumptions built for hourly instances. We instrument cost to serve by use case and keep agent spend in the same governance loop as everything else.
Reporting tells you what was spent. FinOps services explain why, who owns it and what changes next. It runs as a continuous loop across finance, engineering and platform teams rather than a monthly review.
A scoped discovery usually runs four to six weeks and ends with a baseline, a ranked opportunity list and a savings plan tied to your current platform and governance stack.
Azure, AWS and Google Cloud, with deep engineering on Databricks, Snowflake, Microsoft Fabric, Azure Data Factory, Power BI, Unity Catalog and Purview.
Both models are available. Every recommendation is reviewed and tested by your team before production, and you decide what we execute and what stays advisory.
A Value Realization Office owns the baseline and the savings number, revalidates quarterly and updates policy so new pipelines and workloads are governed at creation rather than cleaned up later.