Google Cloud Services Built for Businesses That Cannot Afford to Get It Wrong

Cloud migration is the project with the most ways to go wrong and the least tolerance for rework. We use a wave-based migration approach aligned with Google’s Migration Framework – assess, mobilise, migrate, optimise – applied to your specific estate rather than a generic template.

Migrations we handle regularly:

• On-premises VMware and bare-metal workloads to Compute Engine or GKE
• AWS and Azure workloads to GCP, including cross-cloud database replication
• Relational database migration to Cloud SQL (PostgreSQL, MySQL, SQL Server) and AlloyDB
• Analytical workloads from Redshift, Synapse, or on-premises data warehouses to BigQuery
• Legacy monolithic applications decomposed and re-platformed onto Cloud Run or GKE

We run cutover events with zero-downtime strategies – blue-green switches, traffic mirroring, and staged DNS cutovers – so your users experience no service interruption.

Modernising infrastructure is not about moving to the cloud. It is about removing the operational ceiling that legacy infrastructure imposes on your engineering team. We replace hand-built server configurations with managed GCP services and codify everything in Terraform so your infrastructure is reproducible, auditable, and version-controlled.

• VPC design with shared VPC for multi-project organisations and Private Google Access for secure service connectivity
• Compute Engine fleet management with managed instance groups and autoscaling policies
• Cloud Storage bucket design, lifecycle policies, and cross-region replication
• Cloud CDN and Cloud Load Balancing for global traffic distribution
• Terraform and Cloud Deployment Manager IaC, with CI-triggered plan and apply pipelines
• Multi-region active-active and active-passive topology design

GKE is the natural home for containerised workloads on Google Cloud, but a misconfigured cluster is not just inefficient – it is a security and reliability liability. We build GKE environments that are hardened, observable, and right-sized from the start, using Autopilot where operational overhead needs to be minimal and Standard where workload control matters.

• GKE Standard and Autopilot cluster design, provisioning, and day-two operations
• Node pool architecture for mixed workload types (web, batch, ML inference)
• Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) configuration
• Workload Identity setup to eliminate long-lived service account keys
• GKE cluster hardening: CIS Benchmark alignment, Pod Security Standards, Binary Authorization
• Service mesh implementation with Cloud Service Mesh (Istio) for mTLS and traffic management
• Multi-cluster fleet management with GKE Enterprise and Config Sync

We regularly support teams migrating from self-managed Kubernetes on EC2 or Azure AKS who need the operational efficiency and Google-native integration that GKE provides.

A CI/CD pipeline that engineers trust is one of the highest-leverage investments a team can make. We build deployment pipelines that are fast, reliable, and observable – with appropriate gates for security scanning, test coverage, and policy compliance built in, not bolted on.

• Cloud Build pipeline design with caching strategies for fast builds
• Cloud Deploy release pipelines with approval gates and rollback automation
• Integration with GitHub Actions, GitLab CI, and Jenkins for teams with existing toolchains
• Artifact Registry management with vulnerability scanning via Container Analysis
• GitOps workflows with ArgoCD or Flux CD for Kubernetes delivery
• Blue-green, canary, and traffic-split deployments on GKE and Cloud Run
• DORA metrics instrumentation so engineering leaders can track deployment frequency, lead time, and failure rate

Building an application on GCP from the ground up means you are not constrained by the architecture decisions of an on-premises era. We design cloud-native systems that scale proportionally, cost proportionally, and fail gracefully – using serverless compute, managed messaging, and event-driven patterns where they reduce complexity rather than add it.

• Cloud Run service design for stateless APIs and event processors
• Cloud Functions for lightweight event triggers and scheduled tasks
• Pub/Sub and Eventarc for reliable asynchronous messaging between services
• Apigee API management for external API monetisation and developer portals
• Firebase integration for real-time web and mobile application backends
• Microservices decomposition and domain boundary design aligned with team topology

BigQuery is genuinely one of the most capable analytics engines available. We have used it with datasets from a few gigabytes to multi-petabyte data warehouses, and the architecture decisions that matter at each scale are different. We build pipelines and schemas that are designed for the queries your business actually runs, not a generic star schema.

• BigQuery dataset architecture with partitioning, clustering, and materialised view strategies
• Dataflow pipeline development for real-time and batch ETL workloads
• Cloud Composer (Apache Airflow) for complex multi-step orchestration
• Looker and Looker Studio dashboard development for operational and executive reporting
• Data warehouse migration from Redshift, Synapse, Snowflake, or on-premises Oracle
• BigQuery Omni configuration for cross-cloud analytical queries
• Data governance with Dataplex and column-level security policies

The gap between a machine learning experiment and a model that runs reliably in production is where most AI projects stall. We help engineering teams close that gap using Vertex AI – handling the infrastructure, pipeline orchestration, and MLOps practices that turn a trained model into a managed production system.

• Vertex AI training pipeline design with experiment tracking and hyperparameter tuning
• Model deployment to Vertex AI Endpoints with autoscaling and canary rollout support
• MLOps pipeline automation: data validation, model evaluation, drift detection, and retraining triggers
• Pre-trained API integration: Vision AI, Natural Language API, Speech-to-Text, Document AI
• Gemini API integration and grounding configurations for generative AI applications
• Vector search and embedding pipelines using Vertex AI Vector Search

Cloud security failures rarely come from sophisticated attacks. They come from over-permissioned service accounts, public storage buckets, missing audit logs, and organisation policies that were never set. We have seen all of it, and we design GCP environments so these failure modes are structurally prevented rather than detected after the fact.

• IAM design with least-privilege enforcement and regular permission audits
• VPC Service Controls perimeters to prevent data exfiltration from sensitive projects
• Cloud Armor WAF rule configuration and DDoS protection policies
• Security Command Center Premium setup with continuous threat detection and export to SIEM
• Cloud KMS key management with rotation policies and CMEK enforcement for sensitive datasets
• Organisation policy constraints to prevent unsafe resource configurations at scale
• Compliance evidence collection and control mapping for SOC 2, HIPAA, PCI-DSS, and ISO 27001

We work alongside your security team, or act as it if you do not have one, to produce a GCP security posture that is documented, auditable, and maintainable.

Your disaster recovery strategy is only worth the last time it was tested. We design GCP backup and DR architectures with defined Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), and we run live failover tests to confirm those objectives are achievable before they are needed.

• Cloud SQL automated backups, point-in-time recovery, and cross-region replica configuration
• Compute Engine snapshot policies with retention schedules and cross-region copy
• Cloud Storage cross-region replication with Object Versioning and Soft Delete
• Google Cloud Backup and DR service for centralised backup management
• DR runbook development with clearly documented RTO/RPO targets per workload tier
• Scheduled failover testing and post-test remediation

Observability is not dashboards. It is the ability for your engineering team to understand why a system is behaving the way it is, quickly enough to act before users notice. We build observability stacks that give your team that capability using Google Cloud’s operations suite, supplemented where needed with open-source tooling.

• Cloud Monitoring alert policy design with escalation paths and on-call rotation integration
• Log-based metrics and structured logging standards across all services
• Cloud Trace and Cloud Profiler integration for application performance analysis
• SLO and SLI definition using Google’s Site Reliability Engineering framework
• Error budget tracking and burn rate alerting to protect production reliability
• Uptime check configuration and synthetic monitoring for critical user journeys

Cloud cost problems are architecture problems. When a team is overspending on GCP, it is almost always because resources were provisioned for peak capacity and never adjusted, committed use discounts were not applied, or the billing data was not being monitored in a meaningful way. We fix the root cause, not the symptoms.

• GCP billing export to BigQuery with cost allocation labels and dashboard reporting
• Committed use discount (CUD) strategy and sustained use discount (SUD) analysis
• Compute Engine and GKE node right-sizing using Cloud Monitoring utilisation data
• Spot VM and preemptible instance configuration for fault-tolerant batch workloads
• Cloud Storage lifecycle policy implementation to move cold data to Nearline or Coldline
• Budget alert policies with programmatic enforcement via Pub/Sub and Cloud Functions
• Monthly FinOps reviews with spend attribution, variance analysis, and optimisation roadmap

Running a GCP environment well is a continuous operational commitment. Patching, capacity planning, cost review, incident response, security posture management, and architecture evolution all require sustained attention. For teams that need those capabilities without building a dedicated cloud operations function internally, Rlogical provides fully managed GCP services with defined SLAs and a named account team.

• 24/7 infrastructure monitoring with documented escalation and incident response procedures
• OS patching, security update management, and vulnerability remediation
• Proactive capacity planning with scaling recommendations ahead of forecasted demand
• Monthly architecture and cost review with written recommendations
• Dedicated GCP account architect and support engineer, not a ticketing queue