Cloud & Infrastructure - Google Cloud Platform
Google Cloud is a powerful platform.
An unmanaged GCP project is an expensive one.
We design, migrate, secure, and run Google Cloud environments for mid-market firms - Compute Engine, GKE, BigQuery, Cloud Storage, and IAM - built to a real architecture and a cost model you can defend.
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$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build with
Most mid-market GCP projects are stood up fast and never governed
The pattern is consistent. A team enables a few Google Cloud APIs for one project, spins up Compute Engine instances and a Cloud SQL database, drops data into a Cloud Storage bucket, and ships. It works. Then another team does the same in another project, IAM roles get granted at the project or organization level to unblock people quickly, and within a year you have a Google Cloud organization with sprawling resource hierarchy, no consistent labeling, idle instances running nights and weekends, BigQuery queries scanning full tables because nobody set up partitioning, and a bill that grows every month with no clear owner. The platform is doing exactly what it was told. The instructions were just never designed.
Revenue Institute fixes that. We design the resource hierarchy - organization, folders, projects - with a labeling and billing model so cost maps to teams. We tighten IAM to least privilege using groups and custom roles instead of broad primitive roles, configure VPC networking and Private Google Access so traffic stays controlled, set lifecycle policies and committed-use discounts to cut spend, and codify the whole thing in Terraform so it is reproducible and auditable. Whether you are migrating onto GCP, cleaning up an account that grew organically, or want us to run it on an ongoing basis, we hand you a governed environment - not a console full of resources nobody owns.
What we do with Google Cloud Platform
What we build inside your Google Cloud environment
Resource hierarchy and cost allocation
We structure your organization, folders, and projects so workloads are isolated and spend is attributable. A consistent labeling model plus billing export to BigQuery means you can answer what each team, project, and environment actually costs - and budget alerts fire before the invoice does, not after.
IAM tightened to least privilege
We replace broad primitive roles with group-based access and custom roles scoped to what each team actually needs, configure service accounts with workload identity rather than long-lived keys, and set up the audit logging your security team needs. Access becomes something you can reason about instead of a liability waiting for an incident.
Networking, VPC, and secure connectivity
We design VPC architecture, subnets, firewall rules, Private Google Access, and Cloud VPN or Interconnect to on-premises where needed, so internal traffic stays private and services reach data without exposure to the public internet. Networking is documented and segmented, not a flat default network everything happens to share.
Cost optimization that sticks
We right-size Compute Engine and GKE node pools, schedule non-production resources to stop off-hours, apply committed-use and sustained-use discounts where workloads justify them, partition and cluster BigQuery tables to cut scan costs, and set storage lifecycle rules. Then we leave a dashboard and alerting so the savings hold rather than erode.
Data and analytics on BigQuery
BigQuery is one of the strongest reasons to run on Google Cloud, and one of the easiest to misuse. We design datasets, partitioning, and access controls, wire ingestion from your operational systems, and build the cost guardrails - reservations, query cost limits - so analytics scales without surprise spend. The result is a warehouse your team trusts and finance does not fear.
Infrastructure as code and managed operations
We codify the environment in Terraform so it is versioned, reviewable, and reproducible, with CI for infrastructure changes. From there you can run it internally with confidence, or we run it for you - monitoring, patching, cost governance, and on-call - through our managed services. Either way the environment is documented, not improvised.
Our framework
How a Google Cloud engagement runs
Assess and architect
We inventory your current GCP footprint - or your on-premises and other-cloud workloads if you are migrating - map dependencies and cost, and produce an architecture and remediation plan your IT and security teams can review and approve before we change anything. You see exactly what is wrong, what it costs, and the order we will fix it in.
Build, migrate, and secure
We implement the resource hierarchy, IAM, networking, and cost controls, and execute any migration in sequenced waves with rollback paths defined up front. We work in short cycles using Terraform so changes are auditable and you can see progress without a single high-risk cutover that has to go perfectly.
Govern and hand off
Before handoff we set budget and security alerts, document the environment and runbooks, and either train your internal owner or stand up ongoing managed operations. You receive a governed, documented Google Cloud environment with cost and security visibility, not just a set of resources that happen to be running.
Why Google Cloud specifically, and where it creates operational problems
Google Cloud Platform gives mid-market firms a strong set of building blocks: Compute Engine and GKE for workloads, Cloud SQL and Spanner for databases, Cloud Storage for objects, and BigQuery, which is one of the best-in-class data warehouses available anywhere. For data-heavy and analytics-driven businesses, and for teams that value its networking and Kubernetes maturity, GCP is a deliberate and defensible choice. The services themselves are reliable and well-documented. The problems show up in how the environment is governed.
The most frequent operational issues are cost and access. GCP makes it trivial to create resources, which means it is equally trivial to leave them running idle, to scan full BigQuery tables without partitioning, and to accumulate storage with no lifecycle policy. On the access side, the convenience of broad primitive roles - granting someone Editor on a project to unblock them - quietly becomes a security exposure across the whole estate. Without a resource hierarchy, a labeling model, least-privilege IAM, and committed-use planning, a GCP organization drifts into the same sprawl that on-premises infrastructure was supposed to escape, only with a meter running.
What production-grade Google Cloud actually looks like in operations
A well-run Google Cloud environment for a mid-market firm has a deliberate structure: an organization with folders and projects that isolate workloads and environments, consistent labels so billing export to BigQuery answers what every team and project costs, and group-based IAM with custom roles instead of broad grants. Networking is segmented with VPCs, firewall rules, and Private Google Access so services reach data without public exposure. The whole environment is defined in Terraform, so changes are versioned, reviewed, and reproducible rather than clicked into a console and forgotten.
The firms that get durable value from Google Cloud treat it as infrastructure with governance requirements, not a utility that configures itself. They allocate cost, enforce least privilege, codify the environment, and review it as the business changes. Whether you need that environment designed, migrated onto GCP, cleaned up, or run on an ongoing basis through managed operations, the work is the same: turn an accidental project sprawl into a governed, cost-controlled, secure foundation that the systems driving your revenue can rely on. That is the gap Revenue Institute closes.
We're vendor-agnostic
Other Cloud & Infrastructure platforms we specialize in
Not sure Google Cloud Platform is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Google Cloud Platform questions, answered
We already have a messy GCP organization. Can you fix it without starting over?
Almost always, yes. The work is restructuring the resource hierarchy, tightening IAM, applying labeling and cost controls, and codifying the result in Terraform - all of which can be done on the live environment with care. We start with an assessment that maps every project, its cost, and its access, then remediate in priority order. A full rebuild is occasionally faster for badly tangled accounts, but it is not the default recommendation.
How much can you actually cut our Google Cloud bill?
It depends on how the environment grew, but the common wins are significant: right-sizing over-provisioned Compute Engine and GKE, stopping idle and non-production resources off-hours, applying committed-use discounts to steady workloads, partitioning BigQuery tables to cut scan costs, and setting storage lifecycle policies. We quantify the savings during the assessment so you have a real number before committing, and we leave budget alerts so the bill does not creep back up.
Can you migrate us from on-premises or AWS to Google Cloud?
Yes. We map your workloads and dependencies, choose the right landing services on GCP - Compute Engine, GKE, Cloud SQL, BigQuery - and sequence the migration so critical systems move with rollback paths in place. We are honest about which workloads are straightforward lift-and-shift and which need re-architecture to run well on GCP, and we scope downtime per system up front rather than promising none.
How does Google Cloud fit with Vertex AI and our AI plans?
Tightly. Vertex AI runs inside your Google Cloud project, drawing on the same IAM, networking, and data foundation. If we get the cloud environment right - secure data access, proper service accounts, sensible networking - your AI workloads inherit a sound foundation rather than working around a fragile one. We frequently set up the GCP base layer specifically so a Vertex AI or BigQuery ML initiative can ship without infrastructure fighting it.
Do you offer ongoing Google Cloud management or only project work?
Both. Some clients bring us in to design and migrate, then hand off to an internal team we have trained. Others retain us to run the environment - monitoring, patching, IAM and cost governance, security review, and on-call response - through our managed services, particularly when they have no dedicated cloud or DevOps staff. We will recommend the model that fits your size and roadmap honestly.
Our security team needs to approve any cloud changes. Will your work pass review?
That is how we prefer to work. We produce an architecture document covering IAM, networking, encryption, logging, and the controls relevant to your compliance framework, and we walk your security team through it before implementation. We codify the environment in Terraform so every change is reviewable and auditable. The output is designed to satisfy a security review, not to route around one.
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