2 posts tagged with "scaling"

Most PostgreSQL workloads are read-heavy. Dashboards, reporting queries, search, and API endpoints that serve cached-but-still-queried data all put pressure on a single primary. Vertical scaling helps, but at some point you are paying for CPU that only writes use while reads compete for the same connections.

Read replicas solve this by giving you additional PostgreSQL nodes that serve read queries from a continuously updated copy of your data. On FoundryDB, adding a replica takes one API call and a few minutes of provisioning time.

Reactive autoscaling has a fundamental problem: it waits for something to go wrong. Your database hits 95% CPU, the autoscaler wakes up, requests a resize, and for the next few minutes your application eats latency while the new resources come online. If your traffic is predictable (and most production traffic is), this delay is avoidable.

FoundryDB's predictive autoscaling engine learns your workload's seasonal patterns and scales your database before demand spikes arrive. It combines real-time metric thresholds with historical baselines, anomaly detection, and configurable cost limits so you stay fast without overspending.