Is GCP a safe long-term choice?

For core services, yes. The deprecation reputation comes largely from consumer products rather than Cloud, though Google has retired platform features with more notice than warmth. We design so the pieces most at risk are the easiest to replace.

Can you migrate us from AWS or Azure?

Yes, and often we recommend moving only part of it. Analytics workloads frequently justify the move on their own while applications stay where they are. A full migration is worth doing when there is a reason beyond consolidating vendors.

Is it harder to hire GCP engineers?

Somewhat, since the pool is smaller than AWS. In practice the concepts transfer, and an experienced AWS or Azure engineer becomes productive on Google Cloud quickly. It is a real consideration for in-house teams rather than a blocker.

Does GCP work for regulated industries?

Yes. Google Cloud holds the certifications healthcare and financial work requires, supports the necessary agreements, and offers regional controls for data residency. Your audit still covers how it is configured, which is where we do the work.

Google Cloud
Development Services

Stateside Digitals provides GCP development services for teams whose hardest problem is data: analytics platforms, machine learning products, and container workloads built on Google Cloud rather than around it.

Google Cloud Platform is Google’s public cloud, offering the same categories of service as its competitors: compute, storage, databases, and networking. What distinguishes it is where it came from. The platform grew out of infrastructure Google built to run search and video at planetary scale, and the parts closest to that heritage are the ones worth choosing it for.

For a business, that translates into a specific strength rather than a general one. If your work involves large volumes of data, analytics that need answers in seconds, or machine learning as part of the product, Google Cloud is frequently the better platform. For ordinary application hosting, the three major clouds are far closer than any of them admit.

Data At Any Scale

Query Terabytes In Seconds

BigQuery runs analytical queries across enormous datasets without a cluster to size, tune, or keep running when nobody is asking questions.

Kubernetes Done Right

Built By Its Creators

Google originated Kubernetes, and its managed service reflects that, with less of the operational overhead the same setup takes elsewhere.

Networking Advantage

Google’s Own Backbone

Traffic between regions travels Google’s private network rather than the public internet, which shows up as lower latency for global users.

Analytics Platforms

Warehouses and reporting layers where the questions change constantly and waiting overnight for an answer stopped being acceptable.

Machine Learning Products

Recommendations, forecasting, document understanding, and language features built on Vertex AI and Gemini, trained on your data and deployed as part of the product itself.

Container Workloads

Applications running on GKE or Cloud Run, scaling to zero between bursts so quiet periods cost close to nothing to keep available.

This is the honest version of why most companies end up on Google Cloud. They do not evaluate three platforms and pick this one on balance. They have a data problem, someone shows them BigQuery, and the decision gets made from there. That is a reasonable way to choose, because BigQuery genuinely is ahead of its equivalents for a lot of analytical work.

What people often miss is that it does not commit you to anything else. BigQuery reads data sitting in other clouds, and plenty of our clients run applications on AWS while their analytics live here. So the real question is narrower than it first appears: not whether to move to Google Cloud, but whether your data belongs there while everything else stays put.

We build across BigQuery and Dataflow for analytics, Vertex AI for machine learning, and Cloud Run or GKE for application workloads. Data sits in Cloud SQL, AlloyDB, or Spanner depending on scale and consistency needs, with Pub/Sub moving events between services. Infrastructure is defined in Terraform, built through Cloud Build, and reported on in Looker where teams need dashboards.

Google Cloud
BigQuery
Dataflow
Vertex AI
Cloud Run
GKE
Cloud SQL
Spanner
Pub/Sub
Terraform
Cloud Build
Looker

Data Residency Planned

Deprecations Tracked

Portable Where Possible

Billing Split By Project

Regions and storage locations are chosen against the rules you actually operate under, before data starts accumulating somewhere awkward to move it from.

We watch Google’s deprecation notices so a retirement is a planned migration rather than something you read about in a console banner.

Containers and standard databases stay portable, and we tell you which parts of a design are genuine commitments to the platform.

Workloads sit in separate projects so spend is attributable to a team or product rather than arriving as one number nobody owns.

Data Residency Planned

Deprecations Tracked

Portable Where Possible

Billing Split By Project

For general hosting they are closer than any vendor will tell you, and the right answer usually follows your team’s existing skills. Google Cloud pulls ahead when data analytics or machine learning is central, which is the case we would build an argument on.

Google Cloud
Development Services