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VICTORIAMETRICS
High-performance time-series database for observability
Apache-2.0
ABOUT
Traditional time-series databases like Prometheus struggle with high-cardinality metrics at scale — they consume excessive memory, storage, and CPU when ingesting millions of unique time series from distributed AI infrastructure. VictoriaMetrics is a drop-in replacement for Prometheus that handles 10x more data with 10x less storage by using custom compression, efficient index structures, and a clustered architecture. It ingests Prometheus remote writes, Graphite plaintext, InfluxDB line protocol, and OpenTSDB telnet/HTTP, making it a universal backend for any metrics pipeline without rewriting instrumentation.
INTEGRATION GUIDE
1. Replace Prometheus for large-scale ML infrastructure monitoring with 10x less storage and memory overhead
2. Ingest GPU and training job metrics at high cardinality from thousands of inference nodes
3. Store long-term metrics history (months to years) with automated data downsampling and retention
4. Serve as a unified metrics backend for heterogeneous environments mixing Prometheus, StatsD, and Graphite protocols
5. Query infrastructure metrics across hundreds of model deployments with sub-second response times at billion-series scale
TAGS
monitoringtime-seriesmetricsprometheus-compatibleobservability