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Open Source InfluxDB Alternatives

A curated collection of the 3 best open source alternatives to InfluxDB.

The best open source alternative to InfluxDB is TDengine. If that doesn't suit you, we've compiled a ranked list of other open source InfluxDB alternatives to help you find a suitable replacement. Other interesting open source alternatives to InfluxDB are: Timescale and QuestDB.

InfluxDB alternatives are mainly Relational Databases (SQL) but may also be Time Series Databases. Browse these if you want a narrower list of alternatives or looking for a specific functionality of InfluxDB.

Piotr Kulpinski's profile

Written by Piotr Kulpinski

Purpose-built database for Industry 4.0 and IoT that enables real-time ingestion, storage, and analysis of massive sensor data with high compression

Screenshot of TDengine website

TDengine is a powerful time-series database optimized for industrial IoT applications. It offers 10x higher performance than traditional databases through its distributed architecture and unique data model. Key benefits include:

Cost-effective storage with up to 90% reduction through advanced compression and tiered storage options • Zero-code data integration with built-in connectors for MQTT, Kafka, OPC, PI System and other industrial sources • Comprehensive solution combining database, caching, stream processing and AI capabilities in a single platform • Enterprise-ready features like high availability, horizontal scaling, and SQL support • Built-in AI capabilities through TDgpt for time-series forecasting and anomaly detection

The open-source version has over 700,000 instances deployed worldwide and a thriving community with 23,000+ GitHub stars.

Looking for open source alternatives to other popular services? Check out other posts in the alternatives series and openalternative.co, a directory of open source software with filters for tags and alternatives for easy browsing and discovery.

Extend PostgreSQL for time-series data with automatic partitioning, scalable ingestion, and advanced analytics for mission-critical applications.

Screenshot of Timescale website

Timescale is a powerful open-source database built on PostgreSQL, designed to handle time-series data at scale. It combines the reliability and ecosystem of PostgreSQL with specialized features for time-series workloads, making it ideal for a wide range of applications.

Key benefits of Timescale include:

  • Seamless scalability: Automatically partition and distribute time-series data across multiple nodes, enabling effortless scaling from gigabytes to petabytes.
  • High-performance ingestion: Achieve rapid data ingestion rates, allowing you to handle millions of data points per second with ease.
  • Advanced time-series analytics: Leverage built-in functions and features optimized for time-series analysis, including continuous aggregates, data retention policies, and gap filling.
  • SQL compatibility: Utilize the full power of SQL and PostgreSQL extensions while benefiting from time-series optimizations.
  • Flexible data model: Store and query both time-series and relational data in a single database, simplifying your infrastructure.
  • Cloud-native architecture: Deploy Timescale on-premises or in the cloud, with support for containerized environments and Kubernetes.
  • Active community and enterprise support: Benefit from a vibrant open-source community and optional enterprise-grade support for mission-critical deployments.

Whether you're working on IoT applications, financial analytics, monitoring systems, or any project involving time-stamped data, Timescale provides the tools and performance you need to build scalable, reliable, and efficient time-series applications.

Open-source time-series database offering massive ingestion throughput, millisecond queries, and SQL extensions, designed for optimal performance at any hardware scale.

Screenshot of QuestDB website

QuestDB delivers exceptional performance for time-series data management with features that set it apart:

  • Massive Ingestion Capability: Handles over 4 million rows per second per node, making it ideal for high-frequency data collection
  • SQL Compatibility: Offers powerful time-series extensions while maintaining familiar SQL syntax and PostgreSQL wire protocol
  • Hardware Efficiency: Performs effectively on both minimal hardware (like Raspberry Pi) and enterprise-grade servers
  • Built-in Features: Includes out-of-box support for deduplication, out-of-order indexing, and real-time aggregations
  • Integration Ready: Compatible with popular tools like Grafana, Pandas, Python, and various data streaming platforms

Used by major financial institutions and enterprises for real-time analytics, market data processing, and IoT applications.

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