Google Cloud Consulting Services

PS3G provides Google Cloud consulting for organizations that have chosen Google Cloud, or are weighing it, for three reasons that usually come together: a data platform built on BigQuery, AI features built on Vertex AI and Gemini, and applications on Kubernetes or Cloud Run. That includes a founder building an AI-first product, a growing company whose analytics outgrew spreadsheets and a single database, and a data or platform team inside a larger organization with a Google Workspace estate and a migration to plan. We design the foundation, migrate workloads, build the data and AI platforms, and run the estate under defined SLAs if you want us to. We also run our own HIPAA-compliant EHR and telehealth platform, DocVilla, on Google Cloud, so the advice comes from operating it, not only from building it.

Google Cloud Consulting Services: what we build and for whom

Google Cloud’s strengths are concentrated: BigQuery for analytics, Vertex AI and Gemini for machine learning and generative AI, GKE for Kubernetes, Cloud Run for containers without clusters, and a global network. Organizations pick it when those strengths match their roadmap. Our gcp consulting work falls into four groups.

• Foundations. An organization, folder and project structure with identity, networking, logging, policy and billing set up as code before workloads arrive.
• Migration. Moving servers, databases and applications to Google Cloud from on-premises or another cloud, choosing per workload between rehosting, replatforming and refactoring. The method is on our cloud migration page.
• Data and AI platforms. BigQuery warehouses and lakehouses, pipelines, governance and reporting; Vertex AI and Gemini integrations for search, summarization, classification, agents and custom models. This connects to our big data solutions and AI development services.
• Application development and operations. Services on GKE and Cloud Run, event-driven integration with Pub/Sub, APIs, pipelines, security and ongoing management.

Google Cloud is not always the right choice. An organization whose applications, identity and licensing are Microsoft-centered usually gets a simpler estate on Azure, and one that is AWS-native rarely benefits from moving. We work on all three clouds and give a vendor-neutral recommendation, which is the approach on our cloud solutions page. Many organizations land on Google Cloud for data and AI only, with applications elsewhere, and that is a legitimate design when the boundary is drawn deliberately.

What the service covers

The table lists the typical work in our google cloud consulting services and the Google Cloud services involved. Names change; we use the current equivalents.

Type of work	What it looks like	Google Cloud services typically involved
Foundations and governance	Resource hierarchy, identity and access, shared VPC, logging and monitoring, organization policy, billing accounts and budgets	Cloud Identity, IAM, VPC, Cloud Logging and Monitoring, Organization Policy, Terraform
GCP migration services	Discovery, wave planning, server and database migration, cutover and validation	Migration Center, Migrate to Virtual Machines, Database Migration Service, Compute Engine, Cloud SQL
BigQuery consulting	Warehouse and lakehouse design, ingestion pipelines, transformation, governance, reporting, slot and storage cost control	BigQuery, Dataflow, Cloud Composer, Dataform or dbt, Dataplex, Looker or Looker Studio
AI and machine learning	Retrieval-augmented generation, document processing, agents, custom model training and serving, evaluation and monitoring	Vertex AI, Gemini models, Vertex AI Search, Document AI, Vertex AI Agent Builder
Google cloud development	Containerized services, serverless containers and functions, event-driven integration, APIs	GKE, Cloud Run, Cloud Functions, Pub/Sub, Cloud Tasks, API Gateway, Apigee
Security	Least-privilege IAM, secrets, encryption, network controls, threat detection, compliance evidence	IAM, Secret Manager, Cloud KMS, Cloud Armor, Security Command Center, VPC Service Controls
DevOps and operations	Pipelines, infrastructure as code, monitoring, backup, cost reviews, incident response under defined SLAs	Cloud Build or GitHub Actions, Artifact Registry, Terraform, Cloud Monitoring, Backup and DR

How we work

Google Cloud projects go wrong when the data platform is built before anyone agrees who owns the data, or when a Kubernetes cluster is created for a workload that Cloud Run would have served with less to manage. Our process is built to settle those questions early.

1. Assessment. An inventory of applications, data sources, integrations and identity; the analytics and AI use cases with their owners; a target state and a cost estimate from Google Cloud’s pricing tools for the workloads in scope.
2. Foundations. The resource hierarchy, identity, networking, logging, policy and budgets deployed as code and reviewed with your security lead before any workload lands.
3. Pilot. One workload, one data pipeline or one AI use case delivered end to end, with monitoring, backup and rollback proven, and for AI work an evaluation set that defines what good looks like.
4. Waves and builds. Migration waves grouped by dependency; data and application work in two-week increments with demos; a written cutover plan wherever production is affected.
5. Operate or hand over. Documentation, a cost baseline, a review of security findings, then handover to your team or a managed-services agreement with defined SLAs.

Team and stack choices

A lead architect in the United States owns the design and your weekly conversation; cloud, data and AI engineers at our India delivery center carry out the work with overlapping hours; a security engineer reviews the foundation and each wave. Everything is infrastructure as code in your repositories and projects. We prefer Cloud Run over GKE unless the workload needs a cluster, BigQuery over self-managed analytics databases, and managed models over self-hosted ones unless data residency or cost argues otherwise.

Ask for a Google Cloud assessment and we will come back with a written proposal.

Where this fits with the rest of your systems

Google Cloud is often one of two clouds in an organization, with the data and AI platform on Google Cloud and the application estate elsewhere. That split works when the boundary is explicit: which systems are sources, how data moves, where identity lives, and which cloud each team operates.

Because PS3G’s core engineering practice is .NET and the Microsoft stack, we know that boundary from the inside. Our own product runs a Microsoft application stack on Google Cloud, which is less unusual than it sounds: .NET runs well in containers on GKE and Cloud Run, and Google Cloud’s data and AI services are called over APIs that do not care what language the application is written in. A .NET or Java application can publish events to Pub/Sub, a pipeline can land them in BigQuery, a Vertex AI model can score them, and the result can come back to the application through a typed API. We design those contracts rather than letting the data team query the application database directly.

Examples by organization size

These are typical engagement shapes, not named clients.

• A founder building an AI-first product. A fixed-scope engagement that sets up a small, governed Google Cloud environment, a Cloud Run service, a managed database, a Vertex AI retrieval pipeline with an evaluation set, a deployment pipeline and budget alerts, so the product starts on a foundation that will not need rebuilding.
• A growing company outgrowing its reporting. A dedicated data engineer for a few months who builds a BigQuery warehouse fed by pipelines from the company’s operational systems, with transformation, data-quality checks, cost controls and dashboards the finance and operations leads trust.
• A platform team inside a larger organization. A landing zone aligned to the organization’s security standards, a migration program in waves, a GKE platform with the guardrails internal teams need, and a managed-services agreement that complements the internal team rather than replacing it.

Engagement models

• Fixed-scope project: a defined deliverable and price for foundations, a migration wave, a data platform or an AI integration.
• Dedicated team or staff augmentation: Google Cloud architects, data engineers and AI engineers inside your team on a monthly basis.
• Managed services: we monitor, patch, back up, secure and cost-manage your Google Cloud estate under defined SLAs.
• Advisory: a Google Cloud assessment, an architecture or cost review, or fractional cloud and data leadership.

How we start: a first conversation, a short assessment, a written proposal.

Why PS3G for Google Cloud work

We run our own product on Google Cloud, which means we manage its cost, uptime, security and compliance obligations every day for ourselves, under HIPAA, before we advise anyone else. We have built and run software for more than a decade from Wilmington, Delaware and our India delivery center, with engineers certified across Microsoft and open-source technologies. Our AI development and AI governance practices sit on the same teams as our cloud architects, which matters when a Gemini integration touches customer data.

Related services

• All services: everything we build, test, run and advise on.
• Cloud solutions: our vendor-neutral view across Google Cloud, Azure and AWS.
• Cloud migration: the method we use for every migration, whichever cloud.
• Big data solutions: the data platforms BigQuery work belongs to.
• AI development: the use cases, models and governance behind Vertex AI and Gemini integrations.

Frequently asked questions

What do your Google Cloud consulting services include?

Foundations and governance, migration of servers, databases and applications to Google Cloud, BigQuery data platforms and pipelines, Vertex AI and Gemini integrations, development on GKE and Cloud Run, security hardening, cost control and ongoing management under defined SLAs. Each is available as a fixed-scope project, staff augmentation, a managed service or an assessment.

Are you a Google Cloud partner?

We do not make partner-status claims on this site. What we can show is that we run our own HIPAA-regulated product on Google Cloud, that we have built and operated software for more than a decade, and that our cloud, data and AI engineers work on Google Cloud, Azure and AWS alike. Ask for references for work similar to yours during the assessment.

Is BigQuery the right data warehouse for us?

Often, yes, if your reporting has outgrown a single database and you want a warehouse with no servers to manage. It is a strong fit for organizations on Google Workspace, for event and clickstream data, and for teams that want Looker Studio or Looker on top. Cost needs design: partitioning, clustering, slot or on-demand pricing and storage tiers all matter, and we set those up with you.

Can you integrate Gemini or Vertex AI into our application?

Yes. We build retrieval-augmented generation, document processing, classification, summarization and agent features on Vertex AI and Gemini, with an evaluation set, logging, guardrails and cost controls from the start. The application that uses the result can stay in .NET, Java, Node.js or Python and call the AI service over an authenticated API. Our AI governance practice covers the policy and risk side.

Should we use GKE or Cloud Run?

Cloud Run for most services: it runs containers without a cluster to manage, scales to zero and is simpler to secure and cost. GKE when you need a full Kubernetes platform, such as many services with shared infrastructure, specific networking or stateful workloads, or an internal platform team that will run clusters for other teams. We recommend one in the assessment and can move from Cloud Run to GKE later if growth requires it.