GCP Consulting

GCP (Google Cloud Platform) is a public cloud platform used to build, run, and operate applications and data workloads on scalable infrastructure. It is commonly used by engineering, platform, data, and security teams to provision environments, modernize legacy systems, and deliver services with centralized governance and cost controls.

GCP is typically adopted through a landing-zone approach that standardizes organization structure, networking, identity, and logging, then expands using infrastructure as code and automated CI/CD workflows.

• Compute and container platforms for virtual machines and Kubernetes-based applications
• Managed storage and databases for transactional and distributed workloads
• Networking, IAM, and policy controls for secure multi-project environments
• Observability services for monitoring, logging, and incident response
• Analytics and data processing services for batch, streaming, and warehousing use cases

Running and maintaining a physical data center requires significant time and effort, with limited resources compared to the extensive options offered by various Cloud providers. In certain situations, managing physical infrastructure cannot be avoided due to security or budget constraints. Nonetheless, the diverse array of top-notch services provided by cloud providers, along with their seamless integrations and user-friendly interfaces, make them an excellent option for developing software applications.

GCP (Google Cloud Platform) is a public cloud used to run applications and data workloads on globally distributed, elastic infrastructure. It is often chosen for strong data and analytics capabilities, reliable networking, and managed services that reduce operational overhead.

• Global network and regions support low-latency delivery, high availability designs, and resilient multi-zone architectures.
• Managed compute options (GCE, GKE, Cloud Run) fit different runtime needs from VMs to Kubernetes to serverless containers.
• Data and analytics services (BigQuery, Dataflow, Dataproc) enable scalable batch and streaming pipelines with minimal infrastructure management.
• Identity and access controls (IAM, Workload Identity) enable least-privilege access patterns and safer service-to-service authentication.
• Security defaults such as encryption at rest and in transit, plus key management (Cloud KMS), support regulated workloads and strong governance.
• Infrastructure as code and automation integrate well with Terraform, enabling repeatable landing zones, policy guardrails, and environment standardization.
• Observability tooling (Cloud Logging, Monitoring, Trace) supports centralized telemetry, SLO-based operations, and faster incident response.
• Cost management features (budgets, alerts, billing exports) help track spend, attribute costs, and optimize usage across teams and projects.
• Hybrid and multi-cloud capabilities (Anthos, VPN, Interconnect) support gradual migrations and connectivity to on-prem and other clouds.
• Strong ML and AI platform services (Vertex AI) streamline model training, deployment, and MLOps workflows alongside data platforms.

GCP is a strong fit for organizations prioritizing analytics, Kubernetes, and managed data services, but it can require careful project and IAM design to avoid sprawl and unintended access. Teams should plan for governance early, including org/folder structure, policy constraints, and standardized networking patterns.

Common alternatives include AWS and Azure, along with hybrid platforms such as OpenShift for Kubernetes-centric deployments.

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 and compliance controls using Organization Policies, standardized labels/tags, budget alerts, and audit-ready logging aligned to the Google Cloud Architecture Framework.
• Executed application and data migrations to GCP using phased cutovers, dependency mapping, 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:

• Run a GCP assessment and deliver a prioritized findings report covering architecture, reliability, security, and cost drivers.
• Create an adoption roadmap and landing zone plan (accounts/projects, networking, IAM, org policies) aligned to your operating model.
• Design and implement production-ready GCP platforms using Infrastructure as Code with Terraform for repeatable environments.
• Build CI/CD and GitOps-style delivery workflows to standardize deployments, reduce lead time, and improve change safety.
• Implement security guardrails (least-privilege IAM, key/secret management, logging, vulnerability scanning) to support compliance needs.
• Set up observability—metrics, logs, traces, SLOs, and alerting—to improve incident response and service reliability.
• Optimize spend and performance through rightsizing, autoscaling, committed use planning, and cost allocation/reporting.
• Plan and execute migrations to GCP with cutover runbooks, testing, rollback strategies, and post-migration stabilization.
• Troubleshoot production issues and improve operational maturity with runbooks, on-call practices, and automation.
• Enable your teams with hands-on training and working sessions so engineers can operate and extend the platform confidently.