Accelerate Innovation with Google Cloud Consulting Services
Updated
A Smarter Way to Build on Google Cloud
Google Cloud helps businesses move faster, scale securely, and make better decisions with data. At Sthenos, we guide organizations through an outcome-driven GCP journey—modernizing infrastructure, strengthening data foundations, and enabling cloud-native innovation. From strategy and migration to optimization and ongoing governance, we focus on turning Google Cloud investments into measurable business impact.
Our Core GCP Support & Consulting Services
Compute
Storage
Google Kubernetes Engine (GKE)
Google Cloud SQL
Google Big Query
Looker Studio
Vertex AI
How Sthenos drives business impact with Google Cloud Platform
Cloud Migration
Seamlessly move complex workloads to Google Cloud with minimal disruption. We plan, execute, and validate migrations that improve performance, resilience, and long-term cost efficiency.
Platform Modernization
Modernize legacy systems using cloud-native architectures, containers, and managed GCP services. We help businesses improve agility, scalability, and release velocity without operational overhead.
Data Intelligence
Design and build scalable data platforms on BigQuery and GCP analytics services. We unify data sources and enable real-time insights that support faster, more confident decision-making.
AI Enablement
Operationalize AI and ML using Google Cloud’s advanced capabilities. From predictive models to GenAI use cases, we help businesses move from experimentation to production with governance built in.
Security & Compliance
Embed security, privacy, and compliance across your GCP environment. We implement identity controls, network protection, and policy enforcement aligned with enterprise and regulatory requirements.
Operational Excellence
Optimize cloud operations through automation, governance, and continuous improvement. We help organizations control costs, improve reliability, and scale confidently as business demands grow.
Custom AI Solutions Designed For Measurable Business Outcomes
Solving IT Challenges In Every Industry, Every Day.
Working with Sthenos on Google Cloud
Google Cloud Delivery
We help organizations design, migrate, and scale workloads on Google Cloud with reliability, security, and performance built in from day one.
End-to-End Ownership
From cloud strategy and migration to modernization, AI enablement, and managed services, we cover the full GCP lifecycle.
GCP-Native Expertise
Our teams bring deep hands-on experience across Google Cloud services, including data, AI/ML, infrastructure, and security, enabling architectures that are cloud-native and future-ready.
Outcome-Focused Execution
We align every GCP initiative to measurable business goals—cost optimization, faster time-to-market, improved reliability, and scalable innovation.
Security-First Approach
Security, governance, and compliance are embedded into every GCP engagement, enabling enterprises to innovate confidently without increasing risk.
Human-Centered Engineering
We design solutions around real business workflows and users, ensuring high adoption, long-term value, and sustainable digital transformation.
Clients of NeoSOFT
Google Cloud consulting at Sthenos means designing, migrating and optimizing GCP environments that modernize infrastructure, unify data and build intelligent, cloud-native applications at scale, from data engineering and AI to scalable cloud operations. This section groups the seven Google Cloud services on this page, defines each one at Google's own documentation, sets Amazon Web Services vs Google Cloud vs Azure side by side by what this site covers on each, and sets out how an engagement runs, what it produces, what it costs and what to ask.
From Compute to Vertex AI, grouped in Figure 1.
The typical band published on our cloud consulting page.
Depending on the seniority and mix of the team, published sitewide.
Google calls it a cloud foundation, often a prerequisite to deploying enterprise workloads.
On this page
- Google Cloud consulting services, grouped by what they do
- The Google Cloud terms, defined at Google
- AWS, Google Cloud and Azure: what this site covers on each, and which to choose
- How a Google Cloud migration runs
- What it costs, and what moves the number
- What you get from a Google Cloud engagement
- What to ask before you hire a cloud consultancy
- Frequently asked questions
- Related pages
Google Cloud consulting services, grouped by what they do
The seven Google Cloud services on this page fall into four groups: where workloads run, where data is stored, where it is analysed, and where models are deployed and monitored. This page's own summary runs from cloud strategy consulting and migration to optimization and ongoing governance.
Figure 1. The seven Google Cloud services on this page, by what they do
Every box is a service card on this page, named in its own words. The grouping is for reading; nothing in the figure is counted or measured.
Cloud application development services on Google Cloud mean, on this page, cloud-native architectures, containers and managed GCP services (the Platform Modernization card above). Cloud security services mean identity controls, network protection and policy enforcement across your GCP environment (the band under the figure).
The Google Cloud terms, defined at Google
Each definition below is Google's own, linked so you can read the source rather than take ours.
Of the cloud data platforms on this page, BigQuery is the analytics foundation, Cloud SQL provides managed relational databases for your apps, and Looker Studio turns complex data into decision-ready dashboards.
AWS, Google Cloud and Azure: what this site covers on each, and which to choose
This site has a page for each of the three providers. The table below sets out which services each page names, so the comparison is between what we describe, not a ranking of the providers.
| Building block | AWS | Google Cloud | Azure |
|---|---|---|---|
| Kubernetes | Not named on our AWS services page | Google Kubernetes Engine (GKE), on this page | Azure Kubernetes Service (AKS), on our Azure services page |
| Data and analytics | Data Lake and Analytics | BigQuery and Looker Studio | Azure Data Factory, Azure SQL Database and Power BI Dashboards |
| Machine learning and AI | Generative AI and AI/ML | Vertex AI | Azure OpenAI Services and Azure Machine Learning |
| Migration | Cloud Migration | Cloud Migration | Azure Database Migration Service, and cloud migration and modernization |
Which of the three to choose is answered on our cloud consulting page: usually the one your team already knows, unless a specific service or an existing enterprise agreement decides it. Migrating skills is slower and more expensive than migrating workloads.
In an engagement the choice is one step, a target architecture and provider decision delivered as a written recommendation with the tradeoffs visible, including the case for staying where you are. Multi-cloud costs you a second set of skills, a second billing model and a second security model, which is why it should be a decision rather than an accident.
Virtual machines or GKE: which suits the workload
Compute
The right VM strategy- Scalable workloads with autoscaling and performance tuning
- Built for reliability and cost control from day one
Google Kubernetes Engine
Production-grade Kubernetes- Clean cluster design, security guardrails and CI/CD
- Google: "GKE is ideal if you need a platform that lets you configure the infrastructure that runs your containerized apps, such as networking, scaling, hardware, and security."
How a Google Cloud migration runs
A migration is a sequence with a decision at the end of each phase. The phases below are the order we work in, set out in full on our cloud migration and optimization page.
- AssessA workload inventory with dependencies and a risk rating per workload.
- PlanA migration plan with waves, a target architecture and a landing zone design.
- PilotA tested runbook and a set of corrections to the plan.
- Migrate in wavesThe workloads themselves, plus a cutover record per wave.
- ValidateFunctional, performance, restore and security checks against the agreed criteria.
- Optimise and operateRightsizing, lifecycling and scheduling, then the operating model and the runbooks.
What it costs, and what moves the number
A cloud assessment and roadmap is priced by the number of workloads and how much of the estate has to be inventoried, as our cloud consulting page explains. Migration is priced by workload count and complexity, and ongoing optimisation is usually a retainer. For BigQuery and Looker Studio work, our big data page publishes $40,000 to $150,000 for a first warehouse and reporting layer, depending on how many source systems have to be connected.
Figure 2. The published bands, on one scale
Bar widths are proportional to the published figures. Sources: those two pages, and our about us page for the typical project. None of them is a quote for your estate.
| Driver | Why it moves the number |
|---|---|
| How many workloads are in scope | And how many of them nobody currently owns. |
| How much downtime the business will accept | A narrow window buys rehearsal, tooling and out of hours work. |
| How deep the modernisation goes | Moving as is, moving onto managed services and rebuilding for the platform are three different projects. |
| How many environments you have to support | And whether the non-production ones have to run around the clock. |
Figure 3. The published rate band
Source: the rate published sitewide and on our about us page, depending on the seniority and mix of the team. Where the scope is clear we quote a fixed price for a defined outcome.
What you get from a Google Cloud engagement
A Google Cloud engagement produces some or all of the following, agreed in scope before it starts. The list is the one published on our cloud consulting page.
- A current-state assessment. The workload inventory, what each one depends on, where the data lives, and which items have no owner.
- A target architecture. The provider, the account and subscription structure, the network layout and the managed services chosen, with the reasons written down.
- A landing zone design. Identity, network boundaries, logging, guardrails and the tagging standard, in a form that can be built from code.
- A cost model with a rightsizing plan. The projected monthly run rate, the tagging scheme that will make it attributable, and the commitments worth making once usage is measured.
- A migration plan. Workloads grouped into waves, each with a strategy, a rollback point and a downtime window agreed in advance.
- Runbooks and an operations model. Who is paged, what they do, and what the monitoring is actually watching.
- A phased roadmap. What happens next, in what order, with the decisions that would change it named.
What to ask before you hire a cloud consultancy
The same questions apply whether you are hiring a cloud data warehouse consultant for BigQuery or a consultancy for the whole estate. They come from the same page as the list above, and work on us as well.
- Do you resell the infrastructure you are recommending? If the answer is yes, ask how the margin is disclosed and whether the recommendation would change without it.
- Whose name is on the cloud account and the repository? Anything other than yours is a dependency you did not agree to.
- What is your estimate of our monthly run rate after the move? A vendor who will only quote the project is quoting the smaller number.
- Show me the landing zone as code. If the environment was built by hand it cannot be rebuilt, reviewed or audited the same way twice.
- Which certifications do you hold, and which are your partner's? Audited and self declared are different claims, and so are a firm's own certifications and its partner's.
- What is your own attestation status? Ask for the report rather than accepting a logo. We will tell you plainly that we hold none.
- Who operates this after go live, and under what terms? A design nobody can run is not finished.
Frequently asked questions
What is a landing zone on Google Cloud?
Google calls it a cloud foundation: a modular and scalable configuration that enables organizations to adopt Google Cloud for their business needs, and often a prerequisite to deploying enterprise workloads. You need one before the second workload.
Which Google Cloud services do you work with?
Seven, listed on this page: Compute, Storage, Google Kubernetes Engine, Google Cloud SQL, Google BigQuery, Looker Studio and Vertex AI, from strategy and migration to optimization and ongoing governance.
Should we run on virtual machines or GKE?
Compute suits scalable workloads with the right VM strategy, autoscaling and performance tuning. Google's GKE overview puts it this way: "GKE is ideal if you need a platform that lets you configure the infrastructure that runs your containerized apps, such as networking, scaling, hardware, and security."
How much does Google Cloud consulting cost?
A cloud assessment and roadmap is priced by the number of workloads and how much of the estate has to be inventoried. Our rates are $150 to $250 per hour, depending on the seniority and mix of the team, and we fix the price in writing against a defined scope. Migration is priced by workload count and complexity, ongoing optimisation is usually a retainer, and our rates are $150 to $250 per hour.
Who owns the cloud accounts and the code?
You do. The repository and the cloud accounts are in your name, with no licence back to us and no dependency on us continuing to exist.