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Head-to-head · Time-Series Databases

Apache IoTDB vs Prometheus

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Apache IoTDB #1 in Time-Series Databases 9.3/10 Open source ✓ 3 of 3 features Visit Apache IoTDB
Prometheus #5 in Time-Series Databases 8.8/10 Free plan Free plan✓ 1 of 3 features Visit Prometheus

Apache IoTDB leads on 2 checks, Prometheus on 0, and 2 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreApache IoTDB · 9.3/10
  • Free planonly Prometheus
  • Most featuresApache IoTDB · 3 of 3

Apache IoTDB scores higher on our rubric for time-series databases: 9.3 against 8.8 out of 10; our editors rank them #1 and #5.

On deployment, Apache IoTDB gives you Both where Prometheus offers Self_hosted. Apache IoTDB offers sql support; Prometheus doesn't. Apache IoTDB offers horizontal scaling; Prometheus doesn't.

Apache IoTDB is the better fit for ioT data with SQL and many ingestion options. Prometheus is the better fit for prometheus-native monitoring metrics.

  • Apache IoTDB fits best

    IoT data with SQL and many ingestion options

  • Prometheus fits best

    Prometheus-native monitoring metrics

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Side by side

Feature Apache IoTDB 9.3/10 Visit ↗ Prometheus 8.8/10 Visit ↗
At a glance
Editor score 9.3 8.8
Ranking #1 in Time-Series Databases #5 in Time-Series Databases
Best for IoT data with SQL and many ingestion options Prometheus-native monitoring metrics
Pricing model Free Free
Starting price Open source Free plan
Free plan Not checked yet ✓ (best)
Free trial — —
Deployment Cloud, Self-hosted, Desktop Self-hosted
Platforms Web, Windows, Linux Web, Windows, macOS, Linux
Support Community, Docs Community, Docs
Integrations 4 integrations 8 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Apache IoTDB 3/3 · Prometheus 1/3
SQL support ✓ (best) —
Retention policies ✓ ✓
Horizontal scaling ✓ (best) —
Specs
Deployment Both Self_hosted
Query languages IoTDB-SQL; Tree Mode SQL; Table Mode SQL PromQL
Ingestion methods INSERT SQL; Java JDBC; Java, C++, Python, and Go native APIs; REST API; MQTT; CSV; TsFile HTTP pull scraping; Pushgateway; Remote Write; OpenMetrics backfilling
Our review
Pros
  • High-throughput real-time and batch ingestion across multiple interfaces
  • SQL with tree and table models, filtering, aggregation, and grouping
  • TTL retention, cluster scaling, and broad analytics integrations
  • PromQL supports selecting and aggregating labeled time-series data.
  • HTTP scraping, service discovery, and exporters support metric collection.
  • Recording rules, Alertmanager, federation, and remote storage extend monitoring.
Cons
  • Self-managed deployment places operations and maintenance on the organization
  • Uses IoTDB SQL dialects rather than a single conventional SQL model
  • Its broad ingestion and integration surface may exceed smaller IoT projects
  • No SQL support for querying time-series data.
  • Local storage is single-node rather than horizontally scaled.
  • Broader architectures require federation or remote-storage integrations.
Our verdict

Apache IoTDB is an open-source, IoT-native time-series database for organizations managing large volumes of device and sensor data. It suits industrial use cases across energy, transportation, aerospace, manufacturing, and general IoT…

Read the review →

Prometheus is an open-source monitoring and alerting toolkit for infrastructure, applications, and services, particularly in dynamic and microservice-oriented environments. It collects numeric metrics through HTTP-based scraping and stores…

Read the review →
  1. Apache IoTDBTime-Series Databases 9.3Open source
  2. PrometheusTime-Series Databases 8.8Free plan

Strengths and trade-offs

  • Apache IoTDB — where it wins

    • High-throughput real-time and batch ingestion across multiple interfaces
    • SQL with tree and table models, filtering, aggregation, and grouping
    • TTL retention, cluster scaling, and broad analytics integrations

    Where it doesn't

    • Self-managed deployment places operations and maintenance on the organization
    • Uses IoTDB SQL dialects rather than a single conventional SQL model
    • Its broad ingestion and integration surface may exceed smaller IoT projects

  • Prometheus — where it wins

    • PromQL supports selecting and aggregating labeled time-series data.
    • HTTP scraping, service discovery, and exporters support metric collection.
    • Recording rules, Alertmanager, federation, and remote storage extend monitoring.

    Where it doesn't

    • No SQL support for querying time-series data.
    • Local storage is single-node rather than horizontally scaled.
    • Broader architectures require federation or remote-storage integrations.

More comparisons

All Time-Series databases comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update