Monitoring Prisme.ai with Prometheus & Grafana
How to monitor your self-hosted Prisme.ai deployment using Prometheus and Grafana Operators on Kubernetes.
To operate Prisme.ai efficiently in production, it’s essential to monitor service health, resource usage, and error rates. This guide explains how to install and configure Prometheus and Grafana using Operators in a Kubernetes environment.
Why Use Operators?
Using Kubernetes Operators simplifies lifecycle management of complex systems like Prometheus and Grafana:
- Automated installation and upgrades
- Simplified configuration
- Native CRDs for monitoring targets, dashboards, alerts
Step-by-Step Installation
Install Prometheus Operator
You can install the Prometheus Operator via Helm:
Expose Grafana Dashboard
Expose Grafana using an Ingress or port-forward:
Then access it at http://localhost:3000
Default credentials:
- Username: admin
- Password:
admin
(or seeadminPassword
in the values file)
Configure Prometheus Scrape Targets
Prisme.ai services expose Prometheus-compatible metrics endpoints (e.g. /metrics
).
To scrape them, define a ServiceMonitor
:
Import Dashboards
Grafana supports importing dashboards via the UI or ConfigMaps.
Use community dashboards for:
- Kubernetes cluster monitoring
- Pod resource usage
- API Gateway latency & error rates
- Redis, MongoDB, and Elasticsearch health
Alerts and Notifications
Set up alert rules and connect them to notification channels:
Best Practices
Namespace Separation
- Run monitoring stack in a dedicated namespace (
monitoring
) - Use RBAC to isolate metrics access
Retention & Storage
- Configure Prometheus retention (
--storage.tsdb.retention.time=15d
) - Mount persistent volumes for metric storage
Service Discovery
- Use
ServiceMonitor
andPodMonitor
for automatic discovery - Label all Prisme.ai services consistently (e.g.,
app: api-gateway
)
Grafana Security
- Change default admin password
- Enable SSO integration (e.g., OAuth, LDAP) if required
Next Steps
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