Predictive models trained on your own workload tell you what will page you — before it does.
A forecasting platform that ingests months of workload telemetry and builds per-instance and per-query models. It predicts capacity exhaustion, storage cliffs, connection pool saturation, and the slow-burn query regressions that quietly eat your SLO budget.
Every serious database incident has a precursor. Autovacuum falling behind. A working set drifting out of memory. A query plan that has started to flap. The signals are there — they just get lost in the daily noise. We surface them weeks in advance.
How enterprises deploy this service to solve specific, high-stakes problems.
The storage-growth model flagged that the bookings shard would exhaust at 104% of projected Q4 peak. The team added storage + partitioned the largest hot table well before the incident could have landed.
A slow drift in a key plan fingerprint was flagged, traced to a statistics staleness issue, and fixed during a planned maintenance window — avoiding a weekend P1.
Capacity forecasts identified 47 instances with sustained < 15% utilization. Right-sizing cut annual spend without a single SLO regression.
Ingest 30–90 days of historical telemetry to train per-instance models.
Produce weekly 30/60/90-day forecasts with confidence intervals.
Early-warning alerts routed to capacity planners and SREs.
Recommendations tied to specific actions: add storage, partition, upgrade class.
Not generic thresholds — trained on your actual traffic shape.
Every forecast maps to a specific change you can make.
Every alert shows the signals driving the prediction.
Book a 30-minute working session with our team. We'll walk through your stack, your pain points, and what a pilot looks like.
