Deploying Prisme.ai on Google Cloud (GCP)
Step-by-step guide and recommendations for deploying Prisme.ai in a self-hosted environment on Google Cloud Platform (GCP).
Google Cloud Platform provides powerful managed services ideal for deploying Prisme.ai. This guide covers recommended infrastructure, deployment steps, and best practices for self-hosting Prisme.ai on GCP.
GCP Prerequisites
Before starting, ensure you have:
- A GCP account with adequate permissions.
- Google Cloud SDK (gcloud CLI) installed and authenticated.
- Basic knowledge of Kubernetes and essential GCP services (GKE, Cloud SQL, Memorystore, Cloud Storage).
Recommended GCP Infrastructure
Deploy Prisme.ai leveraging these recommended GCP-managed services:
GCP Deployment Steps
Create Project & Enable APIs
Create a GCP project and enable required APIs:
Deploy GKE Cluster
Provision a Kubernetes cluster with GKE:
Provision Databases & Storage
- Deploy MongoDB via MongoDB Atlas or self-host on GKE.
- Set up Redis via Memorystore.
- Provision Elasticsearch via Elastic Cloud or Kubernetes.
- Create Cloud Storage buckets and Filestore instances.
Configure DNS & Networking
Set up Google Cloud DNS or external DNS:
api.yourdomain.com
studio.yourdomain.com
*.pages.yourdomain.com
Deploy Prisme.ai via Helm
Deploy Prisme.ai using Helm to your GKE cluster:
Ingress & SSL Setup
Configure Kubernetes Ingress via GKE’s built-in Ingress Controller:
Use Google-managed SSL certificates for secure HTTPS access.
Security Best Practices
Workload Identity
- Use GKE Workload Identity for secure and granular permissions between Kubernetes and Google Cloud services.
Private VPC
- Configure a private VPC for your cluster.
- Use Cloud NAT for controlled egress internet access.
Secrets & Configuration
- Store sensitive configurations securely in Google Secret Manager.
- Implement regular key rotation policies.
Monitoring & Logging
- Integrate with Google Cloud Operations Suite (Cloud Monitoring and Cloud Logging).
- Set alerts for abnormal resource usage.
Next Steps
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