GCP Cloud Services — Enterprise-Level Architecture & Delivery
Table of Contents
While AWS is our primary cloud specialization, we also design and deliver production-grade solutions on Google Cloud Platform (GCP) for customers who choose or already operate on GCP.
We focus on cloud architecture, platform engineering, DevOps, data, and AI integration on GCP. Application feature development (business logic, product features) is not our core offering and is handled on an on-demand basis when required, similar to how we approach custom development in other areas.
Our team has hands-on experience across a wide range of GCP services and can select and combine the right components based on your technical and business requirements.
GCP Infrastructure Deployment
A solid infrastructure foundation on GCP is critical for long-term stability and scalability.
We design and deploy GCP environments that follow best practices for security, networking, and automation, including:
- VPC design with subnets, routing, and firewall rules
- Private and public service access
- Identity and Access Management (IAM) policies and roles
- Load balancing and traffic management
- Infrastructure as Code (Terraform / Deployment Manager)
Value: You get a clean, well-structured GCP foundation that can support future workloads without constant rework.
GCP Architecture Design & Service Selection
We treat GCP with the same architectural rigor as AWS: start from requirements, then choose the right services.
Our work includes:
- Translating business and technical requirements into GCP architecture
- Selecting appropriate services (GKE, Cloud Run, Cloud Functions, Pub/Sub, BigQuery, Cloud Storage, Cloud SQL, etc.)
- Designing for security, reliability, performance, and cost
- Aligning with Google Cloud Architecture Framework principles
Value: Instead of “just using GCP”, you get a purpose-built architecture that fits your use case and growth plans.
Cloud Migration to GCP
For customers moving from on-premise or another cloud to GCP, we provide structured migration services:
- Assessment of current systems and dependencies
- Migration strategy (rehost, replatform, partial refactor)
- Network and identity integration
- Data migration to Cloud Storage, BigQuery, or managed databases
- Cutover planning with minimal downtime
Value: A controlled, low-risk migration that leaves you with a more modern, maintainable platform on GCP.
DevOps & CI/CD on GCP
We build cloud-native delivery pipelines that integrate tightly with GCP:
- CI/CD with GitHub Actions or GitLab CI targeting GCP workloads
- Terraform-based provisioning for GCP infrastructure
- Automated deployments to GKE, Cloud Run, or Cloud Functions
- Environment separation (dev/stage/prod) with clear promotion flows
Value: Faster, safer releases with a consistent, automated path from code to production on GCP.
Kubernetes & Microservices on GKE
For microservices and containerized workloads, we leverage Google Kubernetes Engine (GKE):
- GKE cluster design and provisioning
- Workload deployment and rollout strategies
- Service mesh integration (e.g., Istio)
- Autoscaling (HPA, cluster autoscaler)
- Observability with Cloud Monitoring, Cloud Logging, and Prometheus/Grafana if required
Important note: We specialize in platform and infrastructure for microservices (Kubernetes, networking, deployment, observability).
We do not position ourselves as a full application development shop. If you need us to implement microservices business logic, this will be handled as on-demand development, scoped separately.
Value: A robust, scalable microservices platform on GKE that your development teams can build on.
Data Lake & Analytics on GCP
We design and implement data platforms on GCP using:
- Cloud Storage as a central data lake
- Data ingestion and processing with Dataflow or custom pipelines
- BigQuery for analytics and reporting
- Integration with Pub/Sub for streaming data where needed
- Basic governance and lifecycle policies
On AWS, we work with S3, Glue, and Athena; on GCP, we apply similar patterns using native services like BigQuery and Dataflow.
Value: A modern data foundation that supports analytics, reporting, and future AI/ML initiatives.
AI/ML & GenAI on GCP (Vertex AI, LLM Integration)
While your primary AI/GenAI strategy may live on AWS (e.g., Bedrock, SageMaker), we can also support AI/ML workloads on GCP when required:
- Model deployment and serving on Vertex AI
- Integration with LLMs and external AI providers via secure backend services
- Data pipelines feeding models from BigQuery or Cloud Storage
- Automation and orchestration of AI workflows
For multi-agent and GenAI workflows, we can design architectures where:
- A central AI Manager agent receives user requests
- The manager analyzes the request and decides which specialized AI agents or workflows to trigger
- All calls to external AI providers are made via a backend service that securely stores and manages tokens
- AI agents never hold tokens directly and can only operate through predefined templates and workflows
Value: Safe, controlled AI integration on GCP, with clear separation between orchestration logic, security boundaries, and external AI providers.
On-Demand Cloud Operations on GCP
Instead of a fixed 24/7 managed service, we provide on-demand operations for GCP environments:
- Monitoring setup and tuning
- Incident investigation and remediation
- Backup and recovery configuration
- Security hardening and patching guidance
Value: You get expert support when you need it, without committing to a full-time managed operations contract.
Cost Management & Optimization on GCP
We apply FinOps principles to GCP just as we do on AWS:
- Cost analysis using GCP Billing and Cost tools
- Rightsizing compute and storage
- Identifying idle or underutilized resources
- Architecture adjustments to reduce recurring costs
We have successfully reduced cloud costs by around 30% for customers using both AWS and GCP.
Value: Lower cloud bills without sacrificing performance or reliability.
Enterprise Deployment Lifecycle (Applied to GCP)
We use a consistent, proven delivery lifecycle across clouds (AWS and GCP):
- Discovery & Assessment: Understand goals, constraints, and current systems
- Architecture & Planning: Design GCP architecture aligned with requirements
- Implementation & Automation: Build infrastructure, pipelines, and platforms
- Testing & Hardening: Validate performance, security, and reliability
- Go-Live: Execute cutover with minimal disruption
- Continuous Optimization: Improve cost, performance, and operations over time
We follow Agile, Scrum, and DevOps principles, with a strong emphasis on speed and practicality rather than heavy, slow processes.