Intermediate·Concept·2 min read

Google Cloud: Vertex AI, Cloud Run, and Cloud Functions

Cloud AI AI Engineering Should know

Practice this question

WANT TO UNDERSTAND THE CONCEPT FIRST?Learn Google Cloud: Vertex AI, Cloud Run, and Cloud Functions

Overview

Vertex AI is the familiar Google Cloud AI platform name used in many existing projects; its model documentation currently routes into evolving Google enterprise agent-platform documentation.
Cloud Run executes containerized applications and workloads.
Cloud Functions terminology appears within Cloud Run functions documentation for function-style deployment.

Why this matters

Google Cloud teams can connect supported models to existing identity, storage, and container execution.

An everyday example

Use the company's existing office and access system while hiring an approved language specialist.

A technical example

A Cloud Run Node API calls an approved model; a separate parsing service processes document jobs.

How it works

Establish service identity
Deploy compute
Configure approved model endpoints
Process/retrieve evidence
Validate output
Emit traces/metrics.

Architecture and flow

Authenticated app → Cloud Run/functions → model platform + search/storage services.

The insurance assistant example

A Google-centered insurer deploys Node and Python containers with controlled service-to-service access.

When to use it

Existing Google Cloud operations and evaluated model/deployment fit.

When to avoid it

Do not confuse the developer Gemini API account/configuration with every Google Cloud model-service feature or governance setting.

Interview answer

“I map Google's model platform and container/function services to the same inference, compute, identity, and data boundaries used on other clouds.”

Benefits, trade-offs, and production considerations

Verify current product names, endpoints, regions, quotas, service identities, and networking before implementation.
These change faster than the architecture.
Model platform docs, Cloud Run, Cloud Run functions.

Requirement	Azure approach	AWS approach	Google Cloud approach
Hosted models	Azure OpenAI / Foundry model services	Bedrock	Vertex/Google model-platform services
Container services	Container Apps	ECS	Cloud Run
Event functions	Functions	Lambda	Cloud Run functions
Knowledge search	AI Search	OpenSearch or chosen vector store	Chosen managed/search/vector service
Identity/secrets	Entra/managed identity + Key Vault	IAM + approved secret management	Service identities/IAM + approved secret management
Primary decision	Existing enterprise fit, approved regions, model features, total operating cost	Same criteria	Same criteria

A cloud product diagram is not a finished system design.
You must still specify document lifecycle, business authorization, retries, budgets, evaluation, observability, and disaster recovery.

Adapted from 13_AI.MD · “Google Cloud: Vertex AI, Cloud Run, and Cloud Functions”. Code examples are study fragments. Difficulty and core labels are editorial estimates; version-specific behavior should be checked against your project.