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Cloud & DevOps

Google Cloud

Google Cloud Platform offers compute, data analytics, and AI services built on Google's global infrastructure and networking.

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01What it is

In production terms.

GCP provides Cloud Run for containerized workloads, BigQuery for analytics, Vertex AI for model training, and GKE for managed Kubernetes. Cloud Storage, Cloud SQL, and Pub/Sub cover object storage, relational databases, and event messaging.

Codeaxes selects Google Cloud when projects lean on data analytics, BigQuery warehousing, or Vertex AI model serving. Cloud Run offers a straightforward path from Docker containers to scale-to-zero production without managing cluster nodes.

02Fit

Best-fit use cases.

  1. 01Containerized APIs on Cloud Run with Cloud SQL PostgreSQL
  2. 02Analytics dashboards powered by BigQuery and Looker Studio
  3. 03ML model training and deployment on Vertex AI
  4. 04Pub/Sub event buses connecting microservices and Cloud Functions

03Trade-offs

Benefits and limits.

Benefits

  • BigQuery excels at petabyte-scale analytics without cluster management
  • Cloud Run simplifies container deployment with per-request billing
  • Strong networking performance via Google's private backbone
  • Vertex AI integrates training, registry, and endpoint serving

Limitations

  • Smaller overall service catalog compared to AWS for niche workloads
  • Enterprise support tiers and billing transparency require upfront planning
  • Some teams find IAM policy syntax less intuitive than AWS equivalents

04Ecosystem

Compatible technologies.

05In the field

Example applications.

  • Analytics platform ingesting event streams into BigQuery via Pub/Sub
  • AI product with Vertex AI endpoints serving LangChain agent responses
  • Cloud Run microservices with auto-scaling and Cloud SQL connection pooling

06Services

Where this stack shows up.

07Cloud & DevOps

More in this group.

Use Google Cloud on a Codeaxes engagement.

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