GCP consulting and hands-on support

GCP (Google Cloud Platform) is a public cloud platform for running application and data workloads on elastic, globally distributed infrastructure. It is commonly used by platform, DevOps, data, and security teams to provision environments, modernize legacy systems, and standardize governance across multiple projects with clear cost controls. For an overview of services, see https://cloud.google.com/.

GCP is often adopted through a landing-zone approach that defines organization structure, networking, identity, and logging up front, then scales using infrastructure as code (frequently Terraform) and CI/CD automation to keep environments consistent across teams.

• Compute options for VM-based and containerized applications
• Managed Kubernetes with Google Kubernetes Engine (GKE)
• Storage and managed databases for transactional and analytical workloads
• Networking, IAM, and policy controls for secure multi-project setups
• Centralized logging, monitoring, and alerting for operational visibility

GCP (Google Cloud Platform) is a public cloud platform for running application, data, and AI workloads on elastic, globally distributed infrastructure. It is often used to reduce operational overhead with managed services while maintaining strong security controls, reliability patterns, and cost governance.

• Global regions and Google’s private backbone support low-latency delivery, resilient multi-zone designs, and disaster recovery architectures.
• Multiple compute models across VMs, managed Kubernetes, and serverless services allow teams to align runtime choices to performance, portability, and operational needs.
• GKE provides a mature managed Kubernetes platform with automated upgrades, node management, and policy integration for standardized container operations.
• Cloud Run enables fast deployment of stateless services and event-driven workloads with per-request scaling and minimal infrastructure management.
• BigQuery offers a fully managed analytics warehouse with separation of storage and compute, strong concurrency, and built-in governance features for large-scale analytics.
• Pub/Sub, Dataflow, and Dataproc support streaming and batch pipelines without managing the underlying messaging or processing infrastructure.
• IAM, service accounts, and Workload Identity enable least-privilege access and safer workload-to-service authentication patterns across projects.
• Organization policies, VPC Service Controls, and Cloud KMS support governance, data perimeter controls, and key management for regulated environments.
• Shared VPC, Cloud VPN, and Interconnect provide secure connectivity patterns for multi-project organizations and hybrid networking.
• Infrastructure as code aligns well with Terraform-based delivery, enabling repeatable landing zones, standardized projects, and enforceable guardrails.
• Cloud Logging and Monitoring centralize observability for SLO-based operations, incident response, and auditability across environments.
• Billing exports, budgets, and alerts support chargeback, anomaly detection, and continuous cost optimization across teams and projects.

GCP is commonly chosen for analytics platforms, container-centric application delivery, and managed data services. A well-designed organization and folder structure, shared networking model, and IAM boundaries are important to prevent project sprawl and unintended access paths.

Common alternatives include AWS and Microsoft Azure.

Our experience with GCP helped us build repeatable delivery patterns, automation, and operational guardrails that we apply to help clients run secure, scalable, and cost-controlled cloud platforms.

Some of the things we did include:

• Designed and provisioned GCP landing zones with Terraform/Terragrunt, including org/folder hierarchy, project factories, IAM baselines, Shared VPC, and clear separation of environments.
• Implemented governance controls using Organization Policies, standardized labels, budget alerts, and audit-ready logging aligned to the Google Cloud Architecture Framework.
• Planned and executed application and data migrations to GCP using dependency mapping, phased cutovers, runbooks, and rollback plans, including hybrid connectivity via Cloud VPN and Interconnect where needed.
• Built container platforms on GKE with hardened cluster baselines, node pool strategies, autoscaling, and GitOps-style delivery integrated with Kubernetes operating practices.
• Delivered CI/CD for applications and infrastructure, including artifact promotion, environment-specific releases, and automated security/quality checks tied to IaC changes.
• Set up observability with Cloud Monitoring and Cloud Logging, including SLOs, alert routing, dashboards, and metrics integration with Prometheus for teams standardizing on a unified monitoring stack.
• Hardened security posture with least-privilege IAM, Workload Identity patterns, Secret Manager adoption, network segmentation, and incident-ready audit trails across critical projects.
• Implemented data platforms on BigQuery and Cloud Storage, including ingestion pipelines, partitioning/clustering strategies, dataset governance, and query cost controls for analytics at scale.
• Designed high-availability and disaster recovery approaches across regions, including backup/restore automation, recovery runbooks, and regular recovery testing to validate RTO/RPO targets.
• Optimized cloud spend with rightsizing, commitment recommendations, budget guardrails, and chargeback/showback reporting so teams could forecast and control costs reliably.

This experience helped us accumulate significant knowledge across multiple GCP use-cases—from greenfield builds to migrations and ongoing optimization—and enables us to deliver high-quality GCP setups that are maintainable, observable, and ready to scale.

Some of the things we can help you do with GCP include:

• Assess your current GCP environment and deliver a prioritized report covering architecture, reliability, security posture, and cost drivers.
• Define an adoption roadmap and target operating model, including org/project structure, networking, IAM strategy, and policy guardrails.
• Design and implement a production-ready landing zone with standardized accounts, VPC patterns, logging, and shared services.
• Build repeatable GCP foundations using Infrastructure as Code with Terraform, including modular patterns and controlled promotion across environments.
• Standardize delivery with CI/CD and GitOps-style workflows to reduce lead time, improve change safety, and enforce approvals.
• Implement security and compliance controls such as least-privilege IAM, key/secret management, network segmentation, and centralized audit logging.
• Establish observability with metrics, logs, traces, SLOs, and actionable alerting to improve incident response and service reliability.
• Optimize performance and spend through rightsizing, autoscaling, commitment planning, and chargeback-ready tagging and cost allocation.
• Plan and execute migrations to GCP with cutover runbooks, testing strategy, rollback plans, and post-migration stabilization.
• Enable your teams through hands-on workshops, pairing, and operational runbooks so they can operate and evolve the platform confidently.