Time-series databases are optimized for append-heavy, time-indexed data. Three popular choices cover different sweet spots. Picking wrong leads to either painful ops or a missing feature.

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TimescaleDB

Postgres extension + custom time-aware compression. SQL native. Continuous aggregates (incrementally updated materialized views). Best for: applications that already use Postgres, business analytics on time-series, multi-year retention with high cardinality.

InfluxDB

Purpose-built TSDB with its own query language (Flux/InfluxQL). Excellent compression. Time-series first-class. Best for: dedicated IoT/telemetry workloads, dashboards needing custom downsampling, when SQL is not a requirement.

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Prometheus

Pull-based monitoring system; stores its own time-series. Limited retention (15d default, configurable). Cluster-internal queries via PromQL — great for ops, less for general analytics. Best for: monitoring infrastructure metrics, alerting, short-term operational data.

Decision

Application metrics with SQL + reporting needs → TimescaleDB. Pure IoT/sensor data with high write volume + downsampling needs → InfluxDB. Kubernetes / service health monitoring → Prometheus. They can coexist — VictoriaMetrics is a Prometheus-compatible alternative that handles long-term storage.

VictoriaMetrics as the 'and'

VictoriaMetrics is API-compatible with Prometheus but designed for long-term retention and high cardinality. Use Prometheus for collection + alerting, ship to VictoriaMetrics for query and storage. Replaces the 'we need years of Prom data' problem.

Pick by query language and retention needs. TimescaleDB for SQL + Postgres familiarity, Influx for pure TSDB, Prometheus for ops monitoring.