Cloud

Google Cloud development services

Cloud architecture and migration on Google Cloud, often the strongest choice for data-intensive and machine learning workloads.

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What is Google Cloud?

Google Cloud Platform (GCP) is Google's cloud platform, generally considered strongest for data analytics, machine learning, and Kubernetes-native workloads. Codiot builds and migrates infrastructure on GCP for teams whose workloads lean data- or ML-heavy, where GCP's tooling and pricing often have an edge over competing clouds.

What we build

Real projects, not a tech-stack badge.

Cloud migrations

Moving infrastructure to GCP with attention to data pipeline and ML workload requirements.

Data and analytics infrastructure

BigQuery and related services for teams with significant data analytics needs.

ML and AI infrastructure

Vertex AI and GCP's ML tooling for teams building or deploying machine learning models at scale.

Kubernetes-native architectures

GKE for teams standardizing on Kubernetes, where GCP has strong native tooling.

The honest take

When this is the right choice, and when it isn't.

Good for

GCP is often the right choice for data-intensive or machine-learning-heavy workloads, where its analytics and ML tooling (BigQuery, Vertex AI) and Kubernetes-native design have a real edge over competing clouds.

When not to use it

For general-purpose infrastructure without specific data or ML requirements, AWS's broader service catalog and larger talent pool are often the safer default.

TerraformGKE for Kubernetes workloadsBigQuery for analyticsCloud Build for CI/CD

FAQ

Common questions, answered plainly.

When does GCP make more sense than AWS or Azure?

When your workload is data- or ML-heavy, GCP's BigQuery and Vertex AI tooling often has pricing and capability advantages. For general-purpose infrastructure without that specific need, AWS or Azure are often the safer default.

How much does GCP migration cost?

It depends on service count and data pipeline complexity. We audit your current setup and quote a fixed scope.

Do you build ML infrastructure on GCP?

Yes, Vertex AI and related GCP ML tooling is a common part of engagements for clients with data science or ML workloads.

Can you integrate GCP with our existing AWS or Azure services?

Yes, multi-cloud setups are less common but supported when there's a genuine reason for it, such as a specific data or ML workload that fits GCP best.

Start

Got a project in mind?

Tell us what you're building. We'll reply within two business days with an honest take on scope, timeline, and cost.

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