Metrics
Numbers that know what normal looks like.
Infrastructure saturation, application golden signals and custom business KPIs — ingested at 1.2M points per second, baselined per metric, and alerted on only when the deviation actually matters. Threshold fatigue ends here.
Golden Signals
The four numbers, live from the checkout-platform workspace
Fictional data, real interface. Every card drills into full-resolution history with deploy markers, related traces and the logs from the same window.
requests / sec
8,412
▲ 4.2%p99 latency
142ms
▼ 8mserror rate
0.12%
stablecluster cpu
46.8%
▲ 2.1%throughput by hour
requests · last 24h · aggregated 1h
memory by node pool
GB used · 96 GB pool budget
Autoscaler forecast: pool-compute scales 8 → 10 nodes at 19:30 UTC peak.
Anomaly Detection
Detectors that learned your seasonality
Every metric gets a per-series baseline across daily and weekly cycles. Detectors fire on scored deviations — and attach the correlated deploy, trace and incident automatically.
detector board
seasonal baselines · checkout-platform · scoring every 60s
- FIRING score 0.91
checkout-svc · p99 latency
2.4σ above seasonal baseline for 12m · linked to deploy v2.41.3
- WATCHING score 0.82
postgres-01 · deadlock rate
New pattern since 14:20 UTC · correlated with INC-4417
- WATCHING score 0.64
redis-cache · evictions/sec
2.1σ above baseline · memory pressure on shard 2
- NORMAL score 0.18
api-gateway · rps
Within expected evening peak envelope
Capabilities
A metrics engine built for real production, not demos
High-resolution, long retention
10-second resolution for 48 hours, 1-minute for 90 days, pre-aggregated years. Zoom from a quarter view to a single spike without losing fidelity.
Seasonal anomaly detection
Per-metric baselines learn your daily and weekly seasonality, then flag deviations with an anomaly score — no thresholds to babysit, no static noise.
Cardinality without fear
Label sets that would melt a time-series database are handled by columnar storage with adaptive rollups. Tag by user tier, region and build SHA freely.
Cost-aware ingest
See exactly which emitters drive your bill, down to the metric name. Pre-aggregate at the edge and drop what nobody queries.
Your metrics already know the answer
Give them a platform that listens. Baselines, detectors and cost-aware ingest — live in an afternoon.