For workloads that live or die on data, not just uptime

Google Cloud

Google Cloud earns its keep on data and container-native workloads, and loses it fast when treated like a second AWS account. This practice builds around what GCP is actually strong at: BigQuery analytics, GKE-native reliability, and an org hierarchy that holds as projects multiply.

Talk to UsScope the Work
AI augmented, human governedEvidence before estimatesYour IP from day oneOne accountable owner per engagementFounded in 2011. Part of the Vinove Group500+ full-time engineers, as of September 2026NASSCOM member
Watch · the 90-second brief

How a Google Cloud project decision actually gets made

Org hierarchy, GKE topology and the data platform choices that hold under growth.

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A Google Cloud project hierarchy and data platform under review.90 secSample frame · video pending

01Before the first project multiplies

Project sprawl is a data problem before it is a cost one

A handful of ungoverned projects usually means nobody can see which team owns which dataset. Four questions settle the structure before that becomes unrecoverable.

How much cluster operation should Google manage for you?

Autopilot removes node management entirely. Standard mode trades that convenience for control over scheduling and custom hardware.

Does the workload's data belong in a warehouse or a database?

Analytical queries across large volumes belong in BigQuery. Transactional application state belongs in Cloud SQL or Spanner instead.

How many projects does your org hierarchy actually need?

Splitting by environment and team usually beats one shared project, but too many folders becomes its own management burden.

What does your existing GCP commitment already cover?

Committed use discounts already purchased change which region and machine family are cheapest before any new design gets picked.

GKE Autopilot against GKE Standard
GKE AutopilotGKE Standard
Control modelGoogle manages nodes for youYou manage the node pools
Operational loadLower, less to patch yourselfHigher, full node-level control
Team fitTeams wanting less cluster operationTeams needing custom node configuration
PortabilityStill standard Kubernetes underneathStill standard Kubernetes underneath
Best forMost container workloadsSpecialised scheduling or hardware needs

02Agreed before build starts

The ledger that keeps Google Cloud work honest

Every Google Cloud engagement fixes what you bring, what changes, what returns, and what never moves out of your control regardless of the project shape chosen.

  1. Your side

    You bring

    • Every project currently open across teams
    • Any compliance duties the org needs to satisfy
    • Budget range and the timeline for restructuring
  2. Our side

    We build

    • A folder and project hierarchy mapped to business units
    • Organisation policies applied at the right scope
    • IAM roles cleaned up to match real responsibilities
  3. Your side

    You receive

    • A documented org structure your team can extend
    • Policy constraints enforcing the guardrails agreed
    • A template for provisioning the next project

Never leaves you

  • The Google Cloud billing relationship and its terms
  • Final approval over every policy and folder
  • Ownership of every project inside the hierarchy

Where the line sitsWe design the folder and project boundary. The billing relationship and every policy call stay yours to approve.

03From org model to first query

Four stages, each closing on something you can inspect

Google Cloud engagements drift when data ownership gets decided informally after pipelines already exist. This system closes every stage with an artefact your team can inspect directly.

  1. Model the project boundary

    Folder structure, IAM roles and policy scope are chosen against your actual teams, not a generic org chart template.

    An org hierarchy with reasons attached

  2. Build the GKE or data layer

    Cluster topology or warehouse schema are designed against real traffic and query patterns, not a reference architecture.

    A platform matched to the workload

  3. Ship in ordered waves

    Each wave is validated against production-like load before the next one starts, so a bad assumption surfaces early.

    A tested rollout sequence

  4. Hand over the operating model

    Runbooks, dashboards and the project provisioning path move to your team, so growth after handover needs no further calls.

    A system your team can operate

04What actually drives the number

Four drivers, not a flat rate card

Google Cloud work is priced against the assessment, not a template, because these four factors change effort by multiples.

Driver 01

Projects and folders already in play

A greenfield org is faster to structure than one with dozens of projects created ad hoc over several years.

Driver 02

Data platform complexity

A single BigQuery dataset costs less to build than pipelines feeding dozens of tables from several source systems.

Driver 03

GKE cluster count and workload complexity

One cluster running a handful of services is simpler than a fleet spanning multiple regions and teams.

Driver 04

Existing committed use discounts

Capacity already purchased changes which machine family and region are cheapest before any new architecture gets designed.

05Evidence and proof

Data platforms people actually query.

Google Cloud proof here is a warehouse analysts use and a cluster that survives real traffic. Every record waits for written client approval before production.

Explore case studies
A BigQuery warehouse consolidating six disconnected data sources.
Tern · Logistics SaaS, EU

Six disconnected data sources became one queryable warehouse.

Tern's reporting lived across spreadsheets and three separate systems with no shared view. A BigQuery warehouse and scheduled pipelines gave analysts one source to query directly.

Data sources consolidated
6
Manual reporting hours saved weekly
Double digits
Sample · pending client approval
A GKE Autopilot cluster absorbing a seasonal traffic spike.
Vela Ledger · FinTech, UK/EU

A GKE Autopilot rebuild survived a traffic spike unattended.

Vela Ledger's checkout service ran on a hand-tuned cluster nobody wanted to touch. Rebuilding on GKE Autopilot let it absorb a seasonal spike without a single page.

Traffic spike absorbed
3x baseline
On-call pages during spike
Zero
Sample · pending client approval
Portrait of an operations director reviewing a BigQuery dashboard.

Our analysts stopped asking engineering for data and started querying it themselves. That shift alone justified the platform.

Mei TanakaDirector of Operations, Tern
Sample · pending client approval

06Read this first

When Google Cloud engineering is the wrong spend

Paying for org and data platform design when one team runs one simple app would cost more than it returns. The exits are marked before the pitch. When the work is justified, it runs against BigQuery, GKE, Cloud SQL, IAM and organisation policy.

  • You run one workload for one small team

    A single project with light governance may already cover it. Org-wide hierarchy solves a scale problem you may not have yet.

  • The workload needs portability across clouds

    When running the same workload across multiple providers matters more than GCP-specific depth, the platform-agnostic route fits better.

    Kubernetes practice

  • Your own engineers already run GCP well

    If your team already owns the architecture and just needs more hands, hiring cloud engineers fits better than adding a delivery layer.

    Hire Cloud Engineers
Engagement shapes for Google Cloud work
ShapeBest whenYou directWe carry
Scoped buildThe project model is settledPriorities and rollout orderGKE or data platform delivery
Delivery podThe model will evolve with learningProduct decisions each cycleEngineering, quality, releases
Team extensionYour leads already run GCP wellArchitecture and standardsNamed engineers and continuity

07Straight answers

Asked before a Google Cloud project ships.

Not answered here?

Ask us directly Or scope the work first
How is this different from a general Kubernetes engagement?

This practice engineers GCP specifically: project hierarchy, IAM, BigQuery and GKE together. A general Kubernetes engagement addresses cluster design alone and leaves the surrounding project and data structure untouched.

Do you help decide between BigQuery, Cloud SQL and Spanner?

Yes, weighed against your query pattern and consistency needs rather than a default choice. Analytical workloads suit BigQuery; transactional state suits Cloud SQL or Spanner depending on scale.

How should a growing company structure GCP projects?

Typically by environment and team inside a folder hierarchy, with organisation policy enforced at the folder level. The right split depends on how many teams need isolated billing and access.

Can you take over a GCP project another vendor built?

Yes, starting with a project and IAM review rather than a rebuild pitch. Many inherited GCP environments need governance and access cleanup, not a full redesign from scratch.

What does Google Cloud consulting cost?

Scoped engagements are priced against project count, data platform complexity and GKE scope identified during assessment. The team then works to a monthly commitment through to launch.

Do you build the pipelines that feed BigQuery?

Yes, sized to your actual source systems and refresh requirements rather than a generic ETL template. Pipeline design is scoped alongside the warehouse schema, not treated as a separate afterthought.

Will this lock us into GCP over AWS or Azure?

No. A well-structured GCP project does not prevent a later multi-cloud decision, though depth on one provider first is usually the more defensible starting order.