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.

Discovery sprint is 4 weeks fixed-fee at 35,000 EUR — Migration Center assessment, dependency mapping, CFT landing zone design, BigQuery / data platform architecture (if in scope), cost model and migration waves. Execution runs as dedicated team engagements from 12,000 EUR/month per pod (TPM + senior GCP engineer + SRE or data engineer). A 150-VM estate with BigQuery analytics layer typically completes in 4–6 months. FinOps + SRE retainer post-cutover from 6,500 EUR/month.