metrics monitoring / time-series databases
Prometheus scrapes metrics from targets via HTTP, stores them as labeled time series, exposes PromQL, and supports dashboarding and alerting.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Drop-in Prometheus-compatible scraper/TSDB/PromQL engine with lower resource usage and optional cluster mode.
VictoriaMetrics: fast, cost-effective monitoring solution and time series database
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose when you want a push-oriented centralized metrics/event store and can bring your own collection and alerting.
Scalable datastore for metrics, events, and real-time analytics
Choose when you need an agentless, out-of-the-box monitoring and alerting server without building Prometheus exporters or PromQL.
An AI-powered next-generation open source real-time observability system.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Legacy push-based metrics graphing/querying layer; choose only if you already run the Graphite stack and don't need PromQL/alerting.
A highly scalable real-time graphing system
Older time-series alerting framework; choose only if you need its expression language over an existing OpenTSDB/Graphite backend.
Time Series Alerting Framework
These projects were analysed and named prometheus among their alternatives. The relationship is not symmetric — how prometheus rates them is a separate judgement, made when prometheus is analysed in its own right.
calls prometheus “Choose Prometheus when you want the canonical upstream monitoring and TSDB ecosystem and do not need VictoriaMetrics' extra long-term storage optimizations or MetricsQL extensions.”
VictoriaMetrics: fast, cost-effective monitoring solution and time series database
calls prometheus “Choose if you need a pull-based monitoring stack with PromQL and ecosystem tools, not a SQL-capable IoT database.”
High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios
calls prometheus “Choose Prometheus when you need a self-contained monitoring TSDB with pull-based collection and PromQL instead of SQL on Postgres.”
A time-series database for high-performance real-time analytics packaged as a Postgres extension
calls prometheus “Choose when you want self-contained pull-based metrics collection, TSDB and PromQL alerting rather than an alerting layer over existing storage.”
Nightingale is to monitoring and alerting what Grafana is to visualization.
calls prometheus “Choose it for pull-based monitoring with PromQL when you don't need SQL or industrial IoT device management.”
Apache IoTDB
calls prometheus “Use Prometheus when you want the established pull-based metrics and alerting ecosystem and will assemble log/status-page components separately.”
An AI-powered next-generation open source real-time observability system.
calls prometheus “standard pull-based monitoring system with its own local TSDB; choose it for basic metrics when federation/HA is not required.”
The open-source observability database. One columnar engine for metrics, logs, and traces, on object storage.