Google Cloud Platform

GKE, BigQuery and Modern App Platforms on GCP

Cloud-native application architecture and data platforms built on Google Cloud for teams running data-intensive or AI/ML-adjacent workloads, from cluster design through the pipelines and IAM controls sitting on top of it.

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01Why GCP With Us

Built for Data-Intensive Workloads

GCP's strengths are data and Kubernetes, and the design reflects that from day one.

  • GKE-native architecture

    Kubernetes clusters are designed around Google's own operational model - node auto-provisioning, Workload Identity and GKE Autopilot where it fits - instead of a generic setup ported over from another cloud.

  • Data platforms that scale

    BigQuery and Cloud SQL are configured for the query patterns your team actually runs, with partitioning, clustering and connection pooling tuned to keep both performance and cost under control.

  • IAM least privilege

    Service accounts and IAM roles are scoped tightly to what a workload actually needs, instead of granted broadly because it's faster to set up that way and fix later.

  • Cost governance built in

    Budgets and committed use discounts are evaluated against real usage data, not vendor estimates, so the savings plan you commit to actually matches how your workloads run.

  • Modern compute options

    Cloud Run and GKE Autopilot are considered alongside traditional Compute Engine, so you're not paying for always-on infrastructure a serverless or managed option would handle for less.

  • Ongoing operations

    Monitoring, cost review and support continue once the platform is live, through a standing engagement rather than a project that ends the day it launches.

02What's Included

What Our Google Cloud Services Cover

From cluster design to the data pipelines running on top of it.

  • GKE & Cloud Run

    Kubernetes and serverless container platforms are sized for your traffic, with the choice between GKE and Cloud Run made on your workload's actual scaling and cost profile, not a default preference.

  • BigQuery & Data Pipelines

    Data warehousing and pipeline design are built for analytics and reporting workloads, with partitioning and clustering strategies that keep query costs predictable as data volume grows.

  • Cloud SQL & Spanner

    Managed relational databases come with backup, replication and failover configured up front, with Spanner brought in for workloads that genuinely need global consistency at scale.

  • IAM & Security

    Service accounts, IAM policies and VPC Service Controls are configured for least privilege, with perimeter controls in place so sensitive data doesn't leave the boundary you've defined.

  • Networking & CDN

    VPC design, Cloud Load Balancing and Cloud CDN are set up for global traffic, with network tiers chosen based on the latency and cost tradeoffs for where your users actually are.

  • Monitoring & Logging

    Cloud Monitoring dashboards and Cloud Logging aggregation bring signals from across services into one place, so your team isn't switching between consoles during an incident.

  • Cost Optimization

    Committed use discounts and budget alerts are reviewed on an ongoing basis, tracked against actual usage rather than set once at launch and forgotten.

  • Migration & Modernization

    Workload migration to GCP comes with a modernization path toward managed, cloud-native services, staged so you're not forced into a single high-risk cutover.

03How We Work

How We Deliver Google Cloud Services

The same structured process whether we're building a new GCP project or taking over an existing one.

  1. 01

    Assess

    Review the current account, architecture and cost baseline, or design one from scratch.

  2. 02

    Architect

    Design a target environment scoped to your workload, budget and compliance needs.

  3. 03

    Implement

    Build and deploy through code that's version-controlled, reviewed and repeatable.

  4. 04

    Operate

    Ongoing monitoring, cost review and support once the environment is live.

04GCP FAQ

Common Questions About Google Cloud Services

  • GCP is a core focus alongside AWS and Azure, not a platform we dabble in. Engagements get the same depth of attention regardless of which cloud you're on.

  • Yes. From dataset and table design through to pipeline architecture for ingesting and transforming data at the volume you're working with.

  • Yes, and we help evaluate which fits better for your workload: Autopilot for reduced operational overhead, standard GKE for finer-grained control.

  • Yes. A common starting point is an IAM and project structure review, tightening scope before any new work is layered on top.

  • Committed use discounts, rightsizing and storage class review are evaluated against actual usage data, with budget alerts set up so costs don't drift silently.

Get started

Ready to Build on the Cloud?

Let's design a cloud environment on AWS, Azure or GCP that is secure, scalable, reliable and cost-effective.

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