Deploying Prisme.ai with Helm on Kubernetes
Complete guide for deploying Prisme.ai on Kubernetes using Helm charts.
Helm simplifies managing Kubernetes applications, offering powerful templating and lifecycle management. This guide details how to deploy and manage Prisme.ai using Helm charts effectively.
Prerequisites
Before deploying Prisme.ai with Helm, ensure:
- Kubernetes cluster (version 1.26+) ready.
- Helm (version 3+) installed and configured.
- Basic familiarity with Kubernetes commands (
kubectl
).
Step-by-Step Helm Deployment
Add Helm Repository
Add the official Prisme.ai Helm repository:
Prepare the Configuration (`values.yaml`)
Download and customize the Helm chart configuration:
Customize values.yaml
with your specific settings:
- Database connections (MongoDB, Redis, Elasticsearch)
- Object storage configurations (S3-compatible storage, Azure Blob, GCS)
- Ingress settings, SSL certificates
Create Kubernetes Namespace
Create a dedicated namespace for Prisme.ai:
Install Prisme.ai via Helm
Deploy Prisme.ai using Helm into your cluster:
Verify the Deployment
Check pod status and ensure all components are running correctly:
Helm Best Practices
Version Control
- Version your
values.yaml
using Git for better tracking. - Regularly upgrade Helm releases to latest stable charts.
Secrets Management
- Store sensitive configurations securely using Kubernetes Secrets or external secret management solutions (e.g., HashiCorp Vault, AWS Secrets Manager).
Resource Optimization
- Define clear resource requests and limits in your Helm chart configuration.
- Regularly review and optimize these values according to observed resource usage.
Monitoring & Alerts
- Integrate Helm deployments with Prometheus & Grafana Operator.
- Configure alerts for critical components of Prisme.ai.
Common Helm Commands
Next Steps
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