The best open source alternative to QuestDB is InfluxDB. If that doesn't suit you, we've compiled a ranked list of other open source QuestDB alternatives to help you find a suitable replacement. Other interesting open source alternatives to QuestDB are: TDengine and Timescale.
QuestDB 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 QuestDB.
InfluxDB handles high-velocity, high-resolution time series data at scale, built for telemetry, edge devices, IoT, and physical AI workloads.

InfluxDB is a time series database built specifically for systems that generate continuous, high-resolution data. Think industrial sensors, satellite telemetry, power grids, and infrastructure monitoring. General-purpose databases weren't designed for this kind of workload, and the performance difference shows at scale.
The core strength is ingest speed. InfluxDB handles millions of data points per second without sacrificing query latency or blowing up storage costs. It uses efficient compression and stores data in Parquet format, which keeps footprint manageable even over long retention windows. Cold data gets automatically evicted and streamed into data lakes, warehouses, or AI/ML pipelines, so you're not paying hot-storage prices for data you rarely touch.
Key capabilities include:
Deployment is flexible. You can run it self-managed on-prem or at the edge, or use the fully managed cloud offering. Both options share the same engine, so you're not locked into a single environment.
Compared to alternatives like QuestDB, TDengine, or Timescale, InfluxDB has the largest installed base in its category, with over 1 million live open source instances and more than 2,800 contributors. It's ranked the top time series database by DB-Engines.
The tool targets engineers building monitoring systems, physical AI pipelines, aerospace telemetry platforms, and industrial data historians. If your workload involves continuous sensor streams and you need both fast writes and fast reads, InfluxDB is built around exactly that problem.
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.
Purpose-built database for Industry 4.0 and IoT that enables real-time ingestion, storage, and analysis of massive sensor data with high compression

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

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:
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.