GCP Consulting Services for US & EU Companies

GCP Migration Center (formerly StratoZone + Migrate for Compute Engine) is the primary discovery surface for VMware, AWS and Azure source estates. We deploy the mcdc collector, ingest VMware vCenter inventory or cloud billing data, and run group analysis to produce TCO comparisons against GCE, GKE Autopilot, Cloud Run and Cloud SQL targets. Output is a per-application 7Rs decision (Google's framing is similar to AWS's) with a defensible cost projection. We supplement Migration Center's app-layer blind spot with our own eBPF dependency tracing for two weeks where east-west traffic patterns matter.

Terraform via the official Cloud Foundation Toolkit (CFT) modules and Terraform Example Foundation as the starting point, customized to your org structure. We do not use Config Connector for foundation — it is fine for application-layer GCP resources inside GKE but adds blast-radius risk for org-level resources. Resource hierarchy is org → folders by business unit → environments (dev/stage/prod/security/logging) → projects. Identity is Cloud Identity or Workspace federated to your IdP (Okta, Entra ID), with mandatory just-in-time elevation via Privileged Access Manager for roles above viewer on production projects. Org Policies enforce region pinning, OS Login, and shielded VM requirements.

EU personal data lands in europe-west1 (Belgium), europe-west3 (Frankfurt), europe-west4 (Netherlands), europe-west9 (Paris), or europe-west12 (Turin) depending on latency and sovereignty requirements. For sovereign workloads we deploy on Sovereign Controls by Partner (T-Systems for Germany, Thales for France) where contractually needed. Org Policy enforces resource location constraints — denied at the org level, not the project level. For Schrems II compliance we use Confidential VMs (AMD SEV / Intel TDX), Cloud KMS with EKM (External Key Manager) for keys held outside GCP, VPC Service Controls perimeters around BigQuery and Cloud Storage, and SCCs in the DPA with Access Transparency logs enabled.

Cloud Run for stateless HTTP services where request-driven scaling and scale-to-zero matter — this is the cheapest correct answer for ~70 percent of microservices we see, and teams that pick GKE for those workloads are usually paying a complexity tax for nothing. GKE Autopilot when you genuinely need Kubernetes primitives (StatefulSets, sidecars, service mesh, custom CNI) but do not want to operate the node pool. GKE Standard only when you need GPUs, custom node configs, or workloads that exceed Autopilot limits. Cloud Run jobs for batch, Workflows for orchestration. We resist the default 'everything on GKE' pattern that adds 30 percent operational overhead for no benefit.

BigQuery is the strongest reason most teams pick GCP over AWS or Azure — serverless, separation of storage and compute, BI Engine for sub-second dashboards, and a sane SQL dialect. We design with partitioning + clustering by default, slot reservations (Editions) for predictable workloads, on-demand for spiky, and dbt for transformation. Streaming via Pub/Sub + Dataflow (Apache Beam) or BigQuery's Storage Write API for low-latency ingestion. For AI/ML, Vertex AI Pipelines for MLOps, Vertex AI Model Garden for managed Gemini and open-weight models, Vertex AI Vector Search for embeddings, with VPC Service Controls perimeters enforcing data boundary on all of it.

Fixed-scope, all-in USD pricing: individual services from $200, a lift-and-shift from $1,800, a re-platform from $4,100, and a full exit off AWS/Azure/GCP from $5,800. You see the line-item budget at the end of discovery and sign off before any code is written — no recruitment markup, no tool surcharges, and cloud fees run on your own accounts so you keep the cost lever. Timelines scale with estate size: a 150-VM estate with a BigQuery analytics layer typically completes in 4–6 months, with re-platform and data-platform work quoted per wave.

Often you should not move the whole estate, and we will say so. The defensible case for GCP is usually workload-specific: the analytics and AI plane (BigQuery + Vertex AI) is where the migration pays for itself, while a stable transactional app on AWS may cost more to move than it saves. Our discovery produces a per-application TCO comparison — source cost vs GCE, GKE Autopilot, Cloud Run and Cloud SQL targets — so the decision is a number, not a preference. Common outcomes are a targeted data-platform move to GCP with BigQuery Omni querying your S3 in place, or a full re-platform when the source bill and lock-in justify it. We do not have a quota to fill on migrations.

FinOps is built into the foundation, not bolted on afterwards. Committed Use Discounts and Sustained Use Discounts sized against real utilisation, Cloud Run scale-to-zero for idle services, BigQuery Editions slot reservations instead of runaway on-demand, budget alerts and Recommender waste reports wired to your team, and per-project/per-label cost attribution so every team owns its spend. Billing export lands in BigQuery with a Looker Studio dashboard you keep. The goal is a bill your finance team can read line by line — not a six-figure BigQuery invoice nobody can explain. Cloud fees run on your own billing account throughout, so you hold the cost lever from day one.

Yes — most engagements are collaborative, not takeover. We work inside your GitLab/GitHub, submit Terraform via merge request with your review, pair with your engineers on the landing zone so knowledge transfers as we go, and document every decision in an architecture decision record. Handover includes runbooks, the Terraform repo you already own, and a walkthrough of the Cloud Foundation Toolkit structure. Teams that want to run GCP themselves afterwards keep everything they need to; teams that want ongoing SRE can retain us per wave. Either way there is no dependency on us to operate what we build.

None. The GCP organisation, projects and billing account are yours and live under your Cloud Identity from the start — we operate with granted, time-boxed access via Privileged Access Manager, not as owners. All infrastructure is Terraform in your repository, all pipelines in your CI, all documentation in your wiki. The contract carries an explicit no-vendor-lock-out clause: you can revoke our access and continue with any other team or in-house without a rebuild. We deliberately avoid proprietary tooling or bespoke scripts that only we can maintain.

The engineers on delivery hold Google Cloud Professional certifications across the relevant tracks — Cloud Architect, Data Engineer, DevOps Engineer and Security Engineer — and the team works to the Google Cloud Architecture Framework and Security Foundations Blueprint as standard. We build against Google's own reference patterns (Cloud Foundation Toolkit, Terraform Example Foundation) rather than home-grown equivalents, and for regulated workloads we align to the Google Cloud security best-practices centre and review quarterly. On top of GCP-specific rigour we run to ISO 27001-ready and SOC 2 Type II (in progress) internal controls, GDPR-aligned processing with SCCs, and HIPAA-capable delivery for health data.

DR is designed to your RTO/RPO, not to a template. Options we architect: multi-zone by default for high availability inside a region, regional failover for stateful data (Cloud SQL cross-region replicas, AlloyDB, Spanner multi-region for zero-RPO), and cross-region backup with Backup and DR Service. Object durability via dual-region or multi-region Cloud Storage buckets, BigQuery dataset replication where analytics must survive a region loss, and infrastructure that is fully re-creatable from Terraform so a region rebuild is a pipeline run, not a manual scramble. We document the runbook, define the failover trigger, and where the budget justifies it we run a game-day to prove the RTO number is real rather than aspirational.