Allocate resources dynamically, monitor performance in real time, and stay ahead of incidents without a dedicated DBA.
Workloads change. OpenEverest lets you adjust CPU, memory, and storage for running clusters without downtime or manual migration.
Increase or decrease compute resources per node. Changes are applied via rolling restarts or, on Kubernetes 1.35+, in-place.
Grow persistent volumes when your data needs more room. No need to provision a new cluster and migrate.
Resource changes are coordinated through the underlying database operator, ensuring quorum is maintained and replicas stay in sync.
OpenEverest is designed to integrate with the monitoring tools your team already uses. Today it ships with PMM support; integrations with Prometheus, Grafana, and other popular stacks are actively being developed.
Track QPS, replication lag, connection counts, and dozens of engine-specific counters on live dashboards.
Identify slow queries, missing indexes, and execution plan regressions before users notice.
See node status, Kubernetes resource usage, and operator reconciliation state in a single view.
The monitoring layer is pluggable. Bring Prometheus, Datadog, or any metrics pipeline that fits your stack.
Monitoring is only useful if it leads to action. OpenEverest works with standard alerting pipelines so you can respond to problems early or automate the response entirely.
Set alerts on any metric like disk usage, replication lag, or error rates. Route them to email, Slack, PagerDuty, or any webhook.
Use historical data to predict when you will run out of storage, connections, or compute headroom.
Build Grafana dashboards tailored to your team KPIs, or use any visualization tool that speaks PromQL.
Deploy OpenEverest on any Kubernetes cluster and provision your first database in minutes.
