All Tools
S
MonitoringFreeOpen Source
STATSD
Simple, lightweight metric aggregation for applications
MIT
ABOUT
AI applications generate high-cardinality metrics — request latencies, token counts, cache hit rates, queue depths, error rates — that need to be collected without impacting application performance. StatsD provides a lightweight UDP-based protocol and daemon that receives metrics from thousands of application instances, aggregates them over configurable flush intervals, and forwards the aggregated data to backend stores like Graphite, Prometheus, or Datadog. Its stateless, fire-and-forget design means even high-throughput inference services can emit metrics with near-zero overhead.
INTEGRATION GUIDE
1. Track LLM inference latency (time-to-first-token, tokens-per-second) as StatsD timers aggregated across replicas
2. Monitor model serving error rates and request queue depths with StatsD gauges and counters
3. Collect cache hit/miss ratios for embedding caches and KV caches in production AI services
4. Aggregate GPU utilization and memory pressure metrics across a cluster of inference nodes
5. Instrument batch inference pipelines to track throughput and job completion rates per model version
TAGS
monitoringmetricsobservabilityaggregationinstrumentation