Services & Tools

A curated list of GCP services relevant to competing in NM i AI. You don't need all of these — pick what fits your approach.

Hosting Your Endpoint

Service	Use case	When to use
Cloud Run	Deploy containerized APIs	Tripletex & Astar Island tasks — this is the go-to
Compute Engine	Full VM (any OS)	Need GPU or persistent server

Recommendation: Start with Cloud Run. It's simpler and free with your account. Only use Compute Engine if you need a GPU or persistent background processes.

AI & Machine Learning

Service	Use case	When to use
Vertex AI	Managed ML platform	Access Gemini and other models via API
Model Garden	Pre-trained model catalog	Browse and deploy models (Gemini, Llama, Mistral)
AI Studio	Experiment with Gemini	Quick prototyping, prompt engineering

Using Vertex AI from Your Endpoint

Call Gemini from your Cloud Run endpoint:

import vertexai
from vertexai.generative_models import GenerativeModel
 
vertexai.init(project="your-project-id", location="europe-north1")
model = GenerativeModel("gemini-2.0-flash")
 
response = model.generate_content("Parse this accounting task: ...")
print(response.text)

Install with: pip install google-cloud-aiplatform

Data & Storage

Service	Use case	When to use
Cloud Storage	File storage (buckets)	Store datasets, model weights, logs
Cloud SQL	Managed PostgreSQL/MySQL	Need a relational database
BigQuery	Data warehouse	Analyze large datasets with SQL

Development Tools

Tool	How to access	What it does
Cloud Shell	Console top-right icon	Free terminal with everything pre-installed
Cloud Shell Editor	"Open Editor" button	VS Code in the browser
Gemini Code Assist	Cloud Shell Editor sidebar	AI coding companion
Gemini CLI	`gemini` in Cloud Shell	AI assistant in the terminal
Cloud Build	Automatic with `gcloud run deploy --source .`	Builds your Docker images

Collaboration

Your @gcplab.me account also works with:

Gmail — communicate with teammates
Google Docs — shared documentation
Google Chat — team messaging
NotebookLM — AI-powered research notebook