Do you support Vertex AI workloads?

Yes. Vertex AI for managed ML training and serving, alongside our own LLM deployment work. We can operate Vertex AI pipelines or build equivalent pipelines on your own infrastructure.

Can you migrate our existing GCP environment to GKE?

Yes. Containerization assessment, GKE cluster setup, workload migration, and ongoing GKE operations are all within scope. We document migration sequencing to minimize disruption.

How do you handle GCP committed use discounts?

Committed use discount analysis is part of every monthly cost review. We model the break-even for 1-year and 3-year commitments against your projected usage and make a recommendation with data.

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Platforms

Gcp.

Google Cloud infrastructure with a focus on data and AI-native tooling. BigQuery, GKE, Cloud Run, Vertex AI, and Cloud SQL -- operated with the same principal-led model as every other platform.

Google Cloud for data-intensive, AI-native, and Kubernetes-first workloads. BigQuery, GKE, Cloud Run, Vertex AI, Cloud SQL, and Pub/Sub -- operated by engineers who understand the GCP billing model well enough to keep costs predictable.

What we operate on GCP

Kubernetes: GKE clusters with Autopilot or Standard mode, Workload Identity, and GitOps deployment
Serverless: Cloud Run for containerized APIs and Cloud Functions for event-driven pipelines
Data: BigQuery for analytics, Cloud SQL for transactional workloads, Spanner for global consistency
AI: Vertex AI for managed ML pipelines and model serving alongside our own LLM deployments
Observability: Cloud Monitoring, Cloud Logging, and custom dashboards in Grafana

When we recommend GCP

For teams already in Google Workspace, workloads that need BigQuery at scale, or AI/ML pipelines where Vertex AI tooling reduces infrastructure complexity.

You get GCP operated by engineers who understand its billing model well enough to keep BigQuery and GKE costs predictable.

Why teams choose Cobham for GCP

No reseller margin. You hold the GCP account and pay Google directly.
Depth where GCP earns it. BigQuery at scale, GKE, and Vertex AI alongside our own LLM deployments.
Recommended only when it fits. Typically Google Workspace teams or BigQuery-heavy analytics, not by default.

Our model on Gcp

You own the account. We operate it.

We are platform-agnostic. We recommend Gcp when it is the right fit for cost, compliance, or performance -- not because we have a reseller agreement. You hold the cloud account and we charge a flat engineering retainer.

No reseller margin

Your cloud bill is between you and Gcp. We never touch the invoice or add a markup.

Flat engineering retainer

One monthly rate for the full scope of operations. Monitoring, patching, incident response, and cost reviews included.

30-day cancellation

Cancel any retainer with 30 days written notice. We hand off runbooks, IaC, and documentation -- you keep everything.

Common questions

Frequently asked.

Do you support Vertex AI workloads?: Yes. Vertex AI for managed ML training and serving, alongside our own LLM deployment work. We can operate Vertex AI pipelines or build equivalent pipelines on your own infrastructure.
Can you migrate our existing GCP environment to GKE?: Yes. Containerization assessment, GKE cluster setup, workload migration, and ongoing GKE operations are all within scope. We document migration sequencing to minimize disruption.
How do you handle GCP committed use discounts?: Committed use discount analysis is part of every monthly cost review. We model the break-even for 1-year and 3-year commitments against your projected usage and make a recommendation with data.

Other platforms we operate

Platform-agnostic from day one.

Cloud

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Gcp

Talk to a principal.

Schedule a 30-minute call. We assess your workload, tell you whether Gcp is the right fit, and scope the engagement before you sign anything.

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