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GCP Infrastructure & DevOps

Kubernetes-native infrastructure built by the platform experts who understand containers at Google-scale.

Container-Native by Design

Google Cloud was designed from the ground up for containerized, microservice architectures. Where competing platforms bolted Kubernetes support onto existing VM-centric infrastructure, GCP builds on the same Borg-derived container orchestration technology that runs Google Search, YouTube, and Gmail — handling billions of containers weekly at a scale no other platform can match.

We engineer GKE clusters, Cloud Run deployments, and fully automated CI/CD pipelines that make deploying containerized applications effortless. Our infrastructure designs embrace the GCP-native service mesh, logging, and monitoring stack — providing deep observability into every container, every request, and every deployment without the operational overhead of self-managed monitoring infrastructure.

For workloads that do not require full Kubernetes complexity, we deploy Cloud Run — the fully managed serverless container platform that scales to zero when idle and handles thousands of concurrent requests automatically. Cloud Run provides the simplicity of serverless with the flexibility of containers — no cold starts, no VM management, and instant scaling.

GCP Infrastructure & DevOps

When to Invest in GCP Infrastructure

Scenarios where GCP infrastructure engineering delivers transformative value.

01

Kubernetes Operations Burden

Your team spends more time managing Kubernetes cluster upgrades, node pool scaling, and certificate rotation than building application features. GKE Autopilot mode eliminates all node management — Google handles the infrastructure while your team focuses purely on deploying containers.

02

Complex Microservice Architecture

Your application consists of 30+ microservices with intricate inter-service communication patterns. Without a service mesh, tracing request flows, enforcing mTLS encryption, and implementing traffic management becomes increasingly impossible as services multiply.

03

Deployment Pipeline Chaos

Your CI/CD pipeline is a fragile chain of manually configured Jenkins jobs, custom shell scripts, and tribal knowledge. Nobody can deploy when the one engineer who built the pipeline is on vacation. You need automated, repeatable, self-documenting deployment infrastructure.

04

Cost-Inefficient Compute

You are paying for 24/7 VM capacity to serve workloads that are idle 80% of the time. Cloud Run's scale-to-zero billing model eliminates waste entirely — you pay exclusively for the milliseconds your containers spend processing actual requests.

Infrastructure Engineering Capabilities

Enterprise-grade container orchestration and automation services.

01/ GKE Cluster Architecture

Deploying production-grade Google Kubernetes Engine clusters with proper node pool design, network policy enforcement, pod security standards, and autoscaling configurations. We design clusters that handle massive traffic variations without manual intervention while maintaining strict resource isolation between workloads.

GKE Standard and Autopilot mode selection based on your team's operational maturity and cost preferences
Multi-cluster fleet management using GKE Hub for organizations requiring clusters across multiple regions
Workload Identity configuration replacing service account key files with secure, automatic credential injection
Horizontal and Vertical Pod Autoscaler configuration with custom metrics from Cloud Monitoring
02/ Cloud Run & Serverless Compute

Deploying containerized applications on Cloud Run for workloads where Kubernetes complexity is unnecessary. Cloud Run provides fully managed scaling, request-based billing, and zero cold-start container delivery — ideal for APIs, webhooks, and event-driven microservices that experience variable traffic patterns.

Cloud Run service deployment with custom domains, SSL certificates, and traffic splitting for A/B testing
Cloud Run Jobs for scheduled and event-triggered batch processing without standing compute infrastructure
Pub/Sub and Eventarc integration for event-driven architectures triggering Cloud Run on message arrival
Cloud Run revision management with gradual traffic migration and instant rollback capabilities
03/ CI/CD Pipeline Automation

Building deterministic, fully automated deployment pipelines that take code from commit to production with zero manual intervention. We design multi-stage pipelines with automated testing, security scanning, container building, and progressive deployment — enabling teams to ship changes safely multiple times per day.

Cloud Build trigger configuration with automated builds on commit, branch push, and pull request events
Binary Authorization enforcement requiring container images to pass vulnerability scanning before deployment
Cloud Deploy configuration for managed continuous delivery with promotion across dev → staging → production
Artifact Registry as the private container image repository with automated vulnerability scanning
04/ Observability & SRE Practices

Implementing comprehensive monitoring, logging, tracing, and alerting that gives your team complete visibility into application health, performance, and reliability. We apply Google's Site Reliability Engineering (SRE) principles — error budgets, service level objectives (SLOs), and blameless post-mortems.

Cloud Monitoring dashboards with custom SLI/SLO definitions and error budget tracking
Cloud Trace distributed tracing across microservices for end-to-end request latency analysis
Cloud Logging with structured log queries, log-based metrics, and log routing to BigQuery for long-term analysis
Alerting policies with notification channels (PagerDuty, Slack, email) and escalation based on SLO burn rate

Infrastructure Delivery Approach

A systematic approach to building production-ready GCP infrastructure from the ground up.

01

Assessment & Design

We evaluate your application architecture, team capabilities, and scaling requirements to determine the optimal compute platform — GKE for complex multi-service architectures, Cloud Run for simpler/variable workloads, or a hybrid approach. We design the network topology, security model, and deployment strategy.

02

Infrastructure Codification

We define all GCP resources as Terraform configurations stored in version control. VPCs, subnets, GKE clusters, Cloud Run services, IAM bindings, and monitoring dashboards — everything is reproducible, auditable, and peer-reviewed through pull request workflows.

03

Pipeline Construction

We build the CI/CD pipelines that automate the entire path from source code to production deployment. Cloud Build configurations handle compilation, testing, container image creation, and deployment orchestration — with Binary Authorization ensuring only scanned images reach production.

04

Operations & Knowledge Transfer

We configure Cloud Monitoring with SLO-based alerting, deploy operational runbooks, and conduct hands-on training with your team. We establish an SRE framework with error budgets and post-mortem practices that enable your team to operate the infrastructure independently.

Industry Applications

Google Cloud solutions built for the world's most demanding data, ML, and infrastructure challenges.

SaaS & Technology Companies

Deploying multi-tenant SaaS platforms on GKE with namespace-level tenant isolation, custom metrics-based autoscaling, and Cloud Deploy managed continuous delivery — enabling the engineering team to ship features five times per day with zero-downtime deployments to 500+ customer environments.

Media & Content Delivery

Building Cloud Run-based media processing pipelines that automatically transcode uploaded video content into multiple formats and resolutions — scaling from zero to 10,000 concurrent transcoding jobs in seconds and costing nothing when no uploads are in progress.

IoT & Industrial Automation

Deploying GKE Edge clusters on Google Distributed Cloud for manufacturing environments — running ML inference models directly on factory floor hardware with central management from GCP and automatic model updates deployed through Cloud Build pipelines.

Frequently Asked Questions

Should we use GKE Standard or GKE Autopilot?
GKE Autopilot is recommended for most workloads — Google manages node provisioning, scaling, and security patching, reducing operational overhead by 80%. GKE Standard is preferred when you need precise control over node configurations, GPUs for ML workloads, or specific OS customizations that Autopilot does not support.
When should we use Cloud Run instead of GKE?
Cloud Run is ideal for stateless HTTP services, APIs, webhooks, and event-driven processors that do not require persistent connections or complex inter-service networking. If your workload can be packaged as a container that responds to HTTP requests, Cloud Run delivers simpler operations and scale-to-zero billing. GKE is better for stateful services, complex networking, and large interconnected microservice fleets.
How do you handle secrets and configuration management?
We use Google Secret Manager for sensitive credentials (database passwords, API keys) with automatic rotation policies. Application configuration is managed through ConfigMaps and environment variables in Kubernetes, or through Secret Manager references in Cloud Run. All secrets are encrypted at rest and in transit with CMEK where required.
Can Cloud Build replace our existing Jenkins infrastructure?
Yes, for most use cases. Cloud Build is serverless — there are no Jenkins servers to maintain, patch, or scale. It integrates natively with Cloud Source Repositories, GitHub, and Bitbucket. For teams with extensive Jenkins Pipeline libraries, we migrate incrementally — starting with new projects on Cloud Build while maintaining Jenkins for existing pipelines until conversion is complete.

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