AI-Powered
Metrics
Ingest millions of metrics per second from any source — infrastructure, applications, custom business KPIs — with AI that finds anomalies before your dashboards do.
Selected Metrics
AI Insight
CPU spike (+48%) correlates with db.query.p99_ms regression. Likely caused by inefficient query in payment-service deployed 14 min ago.
Forecasted peak
89%
in ~22 min
Trend
↑ Rising
last 20 min
Alert threshold
85%
will breach
Built for Scale, Designed for Insight
From raw time-series to AI-correlated insights, TigerOps Metrics handles every layer of your observability stack.
Custom Metrics
Ingest StatsD, Prometheus, OpenTelemetry, or our SDK. Tag with unlimited dimensions for infinite drill-down flexibility.
Anomaly Detection
Dynamic baselines that adapt to seasonality, deployments, and traffic patterns — no manual threshold tuning required.
Forecasting
ML-powered forecasting predicts resource exhaustion and performance degradation before they happen, with configurable horizons.
Correlation Analysis
Automatically surfaces which metrics move together during incidents — cutting root cause time from hours to seconds.
High Cardinality Support
Handle 100M+ unique series without breaking a sweat. Our columnar engine is designed for modern microservice environments.
15-Month Retention
Full-resolution data retained for 15 months with intelligent downsampling. Never lose historical context for capacity planning.
Collect From Anywhere
Drop-in integrations with the tools your team already uses.
Frequently Asked Questions
What is high-cardinality metrics support and why does it matter?
High cardinality means a metric has many unique label combinations — for example, a request latency metric tagged by user ID, endpoint, region, and status code. Most systems struggle or charge extra beyond a few hundred thousand unique series. TigerOps handles 100M+ series per account with no sampling, so you can tag your metrics freely without worrying about cardinality explosions.
How does AI anomaly detection work for metrics?
The anomaly detection engine builds a dynamic baseline for each metric series that accounts for time-of-day patterns, day-of-week seasonality, and deployment-induced shifts. When a value deviates beyond the learned confidence band, an anomaly is flagged — no manual threshold configuration required. The model retrains continuously as your traffic patterns evolve.
Can I ingest metrics from my existing Prometheus setup?
Yes. TigerOps can scrape your Prometheus endpoints directly using standard remote_write configuration, or you can point your existing Prometheus server to forward metrics via remote_write to the TigerOps ingest endpoint. No agent replacement is needed — your existing instrumentation continues to work as-is.
How long are metrics retained and at what resolution?
TigerOps retains metrics at full resolution for the first 30 days, then applies intelligent downsampling (1-minute rollups) for months 2 through 15. You keep 15 months of history for capacity planning and trend analysis without paying full-resolution storage costs for older data.
What is the query latency for historical metrics?
Interactive dashboard queries against recent data typically return in under 1 second. Queries spanning months of history return within a few seconds thanks to pre-computed rollups. Our columnar storage engine is optimised for time-series aggregation, so even complex multi-series queries over long windows remain fast.
Start Collecting Metrics in Minutes
One line of config. No sampling. No cardinality limits. Full AI anomaly detection from day one.
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