Google Cloud consulting services with cost controls
We build analytics and model platforms on Google Cloud for data and engineering teams. We plan the data layout, configure access and cost controls, and hand over a platform your team can operate.
01Capabilities
Data and platform engineering on Google Cloud
Our Google Cloud Platform work covers data and platform engineering, from BigQuery tables to model access and application hosting. Delivery runs through cloud migration and cloud infrastructure engineering.
- Analytics
Build BigQuery tables around the query
We partition and model event and transaction data so queries read the data they need. That covers shipment and telematics events in logistics, and order and clickstream data in e-commerce. Pipeline work sits within data engineering.
- Models
Configure model access within the project
We scope Vertex AI training, endpoints and evaluation around the data the workload is permitted to use. Project configuration, permissions and service behaviour are checked against your security and residency requirements. Model family selection sits on Gemini.
- Containers
Choose GKE or a managed application runtime
We assess GKE alongside Cloud Run, a managed application platform, against the workload and the operating responsibilities your team will own. The cluster decision is covered on Kubernetes.
- Moves
Move the workload or the data platform
We plan a bounded migration to Google Cloud, including data cutover, rollback and transfer costs. Applications can stay on the current cloud where the data platform is the scope. The move includes operational handover.
02Fit
Choose the Google Cloud scope around your data
We compare the warehouse, model and application requirements before choosing a Google Cloud design. Table layout, access boundaries and operating costs belong in that decision alongside the provider.
| Your situation | What we recommend |
|---|---|
| A SQL-fluent team analysing event data with variable query demand | Use Google CloudBigQuery supports on-demand and capacity-based query pricing. We compare both against the query pattern, table layout and storage cost. |
| A Microsoft estate: .NET services, Active Directory, SQL Server, an agreement already signed | Assess AzureWe assess Azure against the Microsoft identity, database and licensing requirements, including the work to connect it to the wider estate. |
| A wide spread of workloads, unusual service requirements, a team already fluent in one cloud | Assess AWSWe compare AWS service fit with the cloud skills already in your team, including migration work and ongoing operations in the estimate. |
| The data platform is moving while existing applications continue in their current cloud | Move the data platformWe scope the Google Cloud data layer separately, including cross-cloud transfer and identity integration. Application migration remains a separate decision. |
| A managed Kubernetes service is one option for hosting the workload | Compare GKE and Cloud RunWe assess cluster requirements on Kubernetes and compare them with Cloud Run's managed application model. The recommendation includes the operational responsibilities of each option. |
| Choosing a warehouse, with BigQuery up against Snowflake | Compare warehousesWe assess Snowflake alongside BigQuery against portability, existing identity, query demand and the cost of moving data between platforms. |
Scope
We define the Google Cloud project and access layout, cost controls and handover within the agreed scope. Your security and residency requirements guide the configuration we validate. We work under an ISO 27001:2022 certified information-security management system. unicrew has 100+ senior in-house engineers across six countries.
We design a Google Cloud estate for the years your team will run it. Before anything runs, we lay out folders, projects and access, one project per environment. We build it so your team can operate it after we hand over.
Oleksandr TrofimovChief Technology Officer, unicrew03Delivery
Plan the data layout before moving the workload
A Google Cloud implementation starts with the decisions the data must support. We establish access and cost controls before moving workloads into the agreed project structure.
- Define the questions and data sourcesWe identify the decisions the platform must support, agree the measures behind them and map where the data lives today. Those definitions guide the warehouse layout and migration scope.
- Set up projects, access and billingWe configure folders, a project per environment, access controls and billing labels. Permissions and service behaviour are checked against the approved data requirements, with cost attribution that your team can use after handover.
- Set the BigQuery layout and cost modelWe assess partitioning, clustering and the data each query needs. Under on-demand pricing, query cost depends on the bytes processed; capacity-based compute is compared against the workload separately, with storage treated as its own cost.
- Move, verify and hand overWe plan the cutover and rehearse rollback for the scoped workload, then verify behaviour against real traffic. The QA and test automation checks are agreed for the move. Tuning follows verification, with runbooks and operating responsibilities included in handover.
04Stack
The cloud, warehouse and model decisions
We decide whether Google Cloud will host applications or only the data platform before comparing the supporting technologies.
05Questions
Questions about Google Cloud scope and cost
BigQuery pricing and cloud consumption affect the project scope. We establish those costs alongside the warehouse, migration and operating decisions.
Yes. We scope Google Cloud engineering around the data platform, model workload or migration you want to deliver.
A managed development team includes unicrew delivery coordination. A defined workload move is scoped through cloud migration. We agree architecture decisions, cost controls and handover responsibilities with your team.
BigQuery fits a Google Cloud data platform; Snowflake belongs in the comparison when warehouse portability matters.
The comparison includes the query pattern and the cost of running each design within your existing estate. We price data transfer and identity integration alongside warehouse compute and storage.
Google Cloud consumption depends on the services and usage; we estimate it separately from engineering fees.
- BigQuery query processing uses on-demand or capacity-based pricing. Under on-demand pricing, the bytes processed affect the query charge.
- Storage is costed separately from query compute.
- Data transfer depends on how the data moves within the design.
We set an agreed cost target per workload and alert on it, then compare compute models against real query demand.
We scope the data platform separately when applications can remain in their current cloud.
We assess the transfer between clouds and the identity work needed to connect the platform to the existing estate. Application migration is a separate scope decision, based on its dependencies and operating requirements.
We start with the data workload and the decision the platform needs to support.
For an existing Google Cloud estate, we ask for read access to the project and billing export. We assess query demand and the data boundaries with your team, then return a written design or migration scope, cost profile and first priorities. Most engagements start within two to four weeks.
We quote the Google Cloud engineering scope per project or engagement model.
- Time and materials is billed hourly and quoted per project.
- Fixed price is outcome based and quoted per project.
- Team extension is billed monthly per team member.
The quotation sets out the delivery work and agreed handover. Google Cloud consumption is estimated separately.
Scope your Google Cloud data or platform project
Bring the workload, data volumes and current cloud setup to a scoping call. We will outline the Google Cloud design, access boundaries and cost model, including which applications stay in place.