I need a database solution suitable for storing large amounts of time-series data for IoT devices. What are my options?
For storing large volumes of IoT time-series data, you need a database optimized for high write throughput, efficient storage, and fast range queries. Here are the primary options, categorized by deployment model: ### **Dedicated Time-Series Databases** - **InfluxDB** – Open-source (InfluxDB OSS) and cloud (InfluxDB Cloud). Purpose-built for metrics and events. Uses a custom storage engine (TSM) and SQL-like query language (Flux or SQL). Excellent for real-time analytics and downsampling. - **TimescaleDB** – PostgreSQL extension that adds hypertables for automatic partitioning by time. Combines full SQL support with time-series optimizations (compression, continuous aggregates). Good if you need relational joins or existing PostgreSQL ecosystem. - **Prometheus** – CNCF project, pull-based monitoring system with its own TSDB. Ideal for metrics from dynamic environments (Kubernetes). Limited to single-node by default, but can scale with Thanos or Cortex.
Brands named
| # | Brand | Share of voice |
|---|---|---|
| 1 | TimescaleDB | |
| 2 | PostgreSQL | |
| 3 | Flux | |
| 4 | Prometheus |
Rank by assistant
| Brand | Avg | Mistral | DeepSeek |
|---|---|---|---|
| TimescaleDB | #2.0 | 2 | 2 |
| PostgreSQL | #3.0 | 3 | 3 |
| Flux | #1.0 | 1 | 1 |
| Prometheus | #4.0 | - | 4 |
A cell is the order that engine named the brand in its own answer. A dash means it was not named.
The answers
Mistral
For storing large amounts of time-series data from IoT devices, you have several database solutions to consider. Here are some options: 1. **InfluxDB**: InfluxDB is an open-source time series database developed by InfluxData. It's designed to handle high write loads and provides features like data retention policies, continuous queries, and a powerful query language called Flux. 2. **TimescaleDB**: TimescaleDB is an open-source, time-series SQL database optimized for fast insertions and complex queries. It's built on PostgreSQL and offers features like time-based partitioning and continuous aggregates. 3. **OpenTSDB**: OpenTSDB is a distributed, scalable, and time-series database. It's built on top of HBase, a distributed NoSQL database, and provides a time-stamped data model. 4. **Kdb+**: Kdb+ is a high-performance, in-memory time-series database developed by KX Systems. It's known for its speed and efficiency in handling large volumes of data. 5. **CrateDB**: CrateDB is an open-source, distributed time-series database that combines the power of SQL with the scalability of NoSQL databases. It's designed for real-time analytics and high write loads. 6. **Amazon Timestream**: Amazon Timestream is a fully managed time-series database service provided by Amazon Web Services (AWS). It's designed to handle large amounts of time-stamped data and provides features like data comp…
DeepSeek
For storing large volumes of IoT time-series data, you need a database optimized for high write throughput, efficient storage, and fast range queries. Here are the primary options, categorized by deployment model: ### **Dedicated Time-Series Databases** - **InfluxDB** – Open-source (InfluxDB OSS) and cloud (InfluxDB Cloud). Purpose-built for metrics and events. Uses a custom storage engine (TSM) and SQL-like query language (Flux or SQL). Excellent for real-time analytics and downsampling. - **TimescaleDB** – PostgreSQL extension that adds hypertables for automatic partitioning by time. Combines full SQL support with time-series optimizations (compression, continuous aggregates). Good if you need relational joins or existing PostgreSQL ecosystem. - **Prometheus** – CNCF project, pull-based monitoring system with its own TSDB. Ideal for metrics from dynamic environments (Kubernetes). Limited to single-node by default, but can scale with Thanos or Cortex.
Measured 2026-10-01
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