Google AI Studio to Cloud Run
Production Deployment
You built something real in Google AI Studio. We take it the rest of the way — containerized, deployed on Cloud Run, with the CI/CD and monitoring your team can actually trust.
Your Prototype Works.
Your Production Story Doesn't.
Stuck in Google AI Studio
You built an application in Google AI Studio and it works great in the sandbox. But Google AI Studio is a prototyping environment — not a production platform. Getting it in front of real users with real traffic is an entirely different problem.
No Pipeline, Just Pain
Every deploy is a manual, high-stakes event. There's no staging environment, no automated tests, and no rollback strategy — just someone SSHing into a server and hoping for the best.
Not Ready for Real Users
There's no authentication layer, no rate limiting, no structured logging, and no alerting. The moment traffic spikes or an API key leaks, you're flying blind with no safety net.
From Google AI Studio Experiment to
Production-Grade Cloud Run Deployment
We follow a repeatable, four-step engagement built specifically for teams that prototyped in Google AI Studio and need to ship — without rebuilding everything from scratch.
Audit Your Google AI Studio App
We review what you built: your Gemini model configuration, API dependencies, environment secrets, and any data your app touches. We map every assumption baked into the prototype that will break in production.
Containerize for Portability
We write a production-grade Dockerfile and connect it to Cloud Build. Your app goes from running on a laptop or in Google AI Studio's sandbox to a portable, reproducible container image stored in Artifact Registry.
Deploy to Cloud Run
We configure your Cloud Run service with auto-scaling, IAM-based access control, VPC connectivity, and Secret Manager integration. Your app runs serverless, scales to zero, and costs nothing when idle.
Wire Up GitOps
We build a full CI/CD pipeline using GitHub Actions or Cloud Build triggers — with separate staging and production environments. Every merge to main runs tests and deploys automatically. No more manual anything.
Everything Your Team Owns When We're Done
Terraform Infrastructure as Code
Every GCP resource is defined in Terraform — reproducible, version-controlled, and ready to clone for new environments. No click-ops, no mystery state.
Cloud Run Deployment
A fully configured Cloud Run service with auto-scaling policies, concurrency limits, health checks, and minimum instance settings tuned to your workload.
CI/CD Pipeline
Automated build, test, and deploy pipeline wired to your Git repository. Push to a branch, get a staging deploy. Merge to main, ship to production.
Secrets & IAM
API keys and credentials move out of your codebase and into Secret Manager. Service accounts follow least-privilege IAM — no overpermissioned keys floating around.
Monitoring & Alerts
Cloud Monitoring dashboards, structured log queries, and uptime checks are configured out of the box. You'll know about a failure before your users do.
Artifact Registry
Your container images are stored and scanned in Artifact Registry with versioned tags and a retention policy. Every deploy is traceable back to an exact commit.
We've Seen This Before.
Here's Where We Help.
Google AI Studio App Going Live
You built a Gemini-powered chatbot or summarization tool in Google AI Studio and need it running in a production GCP project — with real auth, real monitoring, and real users.
Internal Tools on Cloud Run
Your team built an internal tool that lives on someone's laptop or in a shared notebook — we containerize it and give it a proper home on Cloud Run with IAP-secured access.
Greenfield App on GCP
Starting from zero and want to build on GCP the right way from day one — we design and build your full platform foundation so your team ships features, not infrastructure.
Two Ways to Engage
AI Studio Launch
- App audit and dependency mapping
- Dockerfile and Cloud Build configuration
- Cloud Run service deployment
- Secret Manager and IAM setup
- Basic CI/CD pipeline (staging + prod)
- Cloud Monitoring and alerting baseline
Full Platform Engineering
- Everything in AI Studio Launch
- Full Terraform IaC for all GCP resources
- Multi-environment setup (dev, staging, prod)
- Advanced GitOps pipeline with branch policies
- Artifact Registry with image scanning
- VPC, Cloud Armor, and network security baseline
- Runbook and handoff documentation
Engagements are scoped and fixed-price. No hourly billing surprises.
Common questions
- Can you deploy a Google AI Studio prototype to production?
- Yes, that is the core of this engagement. We containerise the app, deploy it to Cloud Run, wire up CI/CD, put the infrastructure in Terraform, move secrets into Secret Manager, and configure Gemini Enterprise Agent Platform (formerly Vertex AI) so the thing runs as a production service rather than a sandbox.
- Why Cloud Run rather than GKE?
- For most applications Cloud Run is cheaper and simpler: it scales to zero, so an idle service costs nothing, and there is no cluster to operate. GKE earns its complexity when you need fine-grained control over scheduling, networking or stateful workloads. We will tell you honestly which one your workload actually needs.
- What does it cost to run a Cloud Run service?
- The Always Free tier covers 2 million requests, 180,000 vCPU-seconds and 360,000 GiB-seconds per month. Beyond that, request-based billing is $0.000024 per vCPU-second and $0.0000025 per GiB-second of active time, plus $0.40 per million requests. A genuinely low-traffic service can sit inside the free tier indefinitely.
- Is $10,000 the full price?
- It is the starting point for a full platform engineering engagement. Smaller pieces of work — deploying a single service, or setting up one pipeline — are better bought as GCP Admin as a Service hours from $3,500.
Your Google AI Studio App Deserves
a Real Production Environment
Tell us what you built. We'll tell you exactly what it takes to ship it on GCP — no sales pitch, no vague roadmap. Just a clear, honest architecture review from engineers who live in this stack.
Book a Free Architecture Review