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10 questions every startup should answer before moving to production with their AI prototype
This is a technical guide providing ten essential questions for AI startups transitioning from prototypes to production on Google Cloud.
Why it matters
Implementing these onboarding, scaling, and governance practices helps startups avoid security breaches, unexpected high costs, and service instability during growth.
The details
- Start in Google AI Studio before migrating to Gemini Enterprise Agent Platform.
- Use regional endpoints and exponential backoff to reduce HTTP 429 errors.
- Store sensitive credentials in Secret Manager rather than .env files.
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Key connections
Google owns google-genai
Google provides the unified google-genai SDK for developers.
Google owns Secret Manager
Secret Manager is a Google Cloud service for managing secrets.
Google owns Cloud Billing
Cloud Billing is a financial and cost management service offered by Google.
Google owns Cloud Pub/Sub
Cloud Pub/Sub is an enterprise messaging service offered by Google.
Antigravity uses Google AI Studio
Antigravity uses API keys from Google AI Studio to build AI prototypes.
Google AI Studio uses Gemini
Google AI Studio provides access to Gemini models via API keys.
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Gemini Enterprise Agent Platform uses Gemini
Gemini Enterprise Agent Platform serves Gemini models with enterprise-level security and controls.
google-genai uses Google AI Studio
The unified google-genai SDK connects to Google AI Studio for prototyping.
google-genai uses Gemini Enterprise Agent Platform
The unified google-genai SDK connects to Gemini Enterprise Agent Platform for production workloads.
Gemini Enterprise Agent Platform uses Identity and Access Management (IAM)
Gemini Enterprise Agent Platform enforces IAM roles and service account permissions.
Gemini Enterprise Agent Platform uses Application Default Credentials (ADC)
Gemini Enterprise Agent Platform uses Application Default Credentials for keyless authentication.
Google Cloud utilizes OAuth user credentials for developer workstation authentication.
Cloud Run uses Identity and Access Management (IAM)
Cloud Run attaches service accounts with specific IAM roles to run agent workloads.
Cloud Run uses Secret Manager
Cloud Run services fetch sensitive configuration and third-party keys from Secret Manager.
Google Kubernetes Engine uses Application Default Credentials (ADC)
Google Kubernetes Engine workloads obtain short-lived tokens via Application Default Credentials.
Gemini Enterprise Agent Platform uses Cloud Monitoring
Gemini Enterprise Agent Platform publishes model serving metrics to Cloud Monitoring.
Gemini Enterprise Agent Platform uses Cloud Logging
Gemini Enterprise Agent Platform writes operational logs to Cloud Logging.
Gemini Enterprise Agent Platform uses Batch Prediction
Gemini Enterprise Agent Platform offers Batch prediction for asynchronous high-volume workloads.
Gemini Enterprise Agent Platform uses Cloud Storage
Gemini Enterprise Agent Platform reads input JSONL datasets from and writes outputs to Cloud Storage.
Cloud Billing uses Cloud Pub/Sub
Cloud Billing publishes budget threshold alerts to Cloud Pub/Sub topics.
Gemini Enterprise Agent Platform uses Model Armor
Model Armor filters prompt and response payloads sent to Gemini Enterprise Agent Platform.
Model Armor is related to Prompt Injection
Model Armor protects generative AI applications against prompt injection and jailbreaks.
Gemini Enterprise Agent Platform uses Security Command Center
Security Command Center provides behavioral monitoring and anomaly detection for AI agents.
Gemini is related to Retrieval-Augmented Generation (RAG)
Gemini models can be combined with RAG architectures for grounding on external data.
Related events
Google Cloud Publishes 10-Step AI Production Scaling Guide for Startups
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