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    FinOps Services That Turn Cloud Cost Visibility Into Realized Savings

    Cloud cost optimization built inside your data and AI platform, not bolted onto the invoice. Delivered through 1Platform accelerators and Polestar Analytics engineering teams.

    Talk to our finops experts
    FinOps
    Why cloud cost visibility stopped being enough

    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.

    Read the Enterprise FinOps 2026 guide
    • FinOps
      Infrastructure sprawl
      Orphan tables, idle clusters and duplicate pipelines accumulate quietly and keep charging.
    • FinOps
      Data quality as a cost centre
      Failed pipelines mean reprocessing, rework and analyst time that nobody budgets for.
    • FinOps
      Ungoverned AI and agent spend
      Token, inference and GPU consumption moves faster than the tools built to control it.
    Our FinOps services
    01
    FinOps assessment and baseline

    A scoped discovery across pipelines, jobs, tables, clusters and AI workloads that isolates the assets driving most of your cost.

    02
    Cloud cost observability

    Unified telemetry across ingestion, transformation and consumption, with health, usage and cost signals in one cockpit.

    03
    Platform and workload optimization

    Right sizing, scheduling, caching and retention recommendations, validated with your engineers before anything changes.

    04
    Data estate rationalization

    Profiling and retirement of orphan and duplicate assets, with hygiene standards that stop the sprawl returning.

    05
    Data Governance & Strategy

    Policy automation, chargeback and a Value Realization Office that owns the savings number quarter after quarter.

    06
    Not sure where your leakage sits?

    Start with the baseline. Four to six weeks, one ranked opportunity list, no platform change required.

    How our FinOps approach works
    FinOps
    01
    Discover and baseline
    Inventory the estate, profile consumption and agree the cost and performance baseline.
    FinOps
    02
    Deploy the observability fabric
    Capture telemetry across every layer and route cost signals to the engineer making the decision.
    FinOps
    03
    Optimize and automate
    AI models recommend the change, your team approves it, automation handles the routine failures.
    FinOps
    04
    Realize and protect value
    Quarterly validation, refreshed scoring and governance that keeps savings from eroding.
    What FinOps Studio puts in front of your teams
    See where the money actually goes
    • Compute, storage and total cloud cost in one cockpit
    • Optimization potential surfaced continuously, not monthly
    • Movement against the prior period, not a static snapshot
    FinOps
    Work a ranked queue, not a spreadsheet
    • Filter by scheduled queries, ad hoc queries, unused datasets and orphan tables
    • An owner attached to every item before it is actioned
    • Human review before any change reaches production
    FinOps
    Catch spend before the invoice does
    • Spend thresholds and query policies enforced continuously
    • Severity filters from critical through to low
    • Status tracked to resolution, with delegation where needed
    FinOps
    FinOps starts in the data layer

    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.

    Explore Data Engineering services
    • Data pipeline development
      Data pipeline development
      ETL and ELT for batch, streaming or hybrid sources, with reliable movement into AI ready structures.
    • Data lake optimization
      Data lake optimization
      Lakehouse architectures on Databricks, sized for the workloads you actually run.
    • Migration and modernization
      Migration and modernization
      Move workloads to cloud or modernize the architecture with cost, confidence and scale in balance.
    • Master data management
      Master data management
      Automated pipelines with data quality and governance running through the MDM process.
    AI and agent spend is now inside the FinOps scope

    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.

    Assess your FinOps maturity
    Achieved 17% cost savings with FinOps on Azure Databricks for a global pharmaceutical GCC
    17%
    Reduction in Azure Databricks spend through workload and cluster optimization
    100%
    Visibility into cloud costs across domains, projects and workloads
    150,000+
    Datasets rationalized by removing redundant pipelines and scripts
    Read the full case study
    Where our FinOps services run
    FinOps services
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    FinOps services
    FinOps services
    FinOps services
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    FinOps services
    FinOps services
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    FinOps services
    Our latest thinking on FinOps and cloud cost
    • AI
    • Wealth Management
    AI in Wealth Management Is Commoditizing Portfolios. What Will Firms Own Next?
    AI-Native RGM
    • AI
    • Revenue Growth Management
    How Does AI-Native RGM Turn Commercial Intelligence Into Action?
    Procurement Spend
    • AI
    • Revenue Growth Management
    5 Hidden Gaps Where Procurement Spend Leaks ft. ProcurePulse
    FinOps questions enterprise teams actually ask

    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.

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