Time Series Databases

Best Time Series Databases include:

Prometheus, InfluxDB, kdb+, AVEVA Historian, VictoriaMetrics Community, Amazon Timestream, QuestDB, Apache Druid, Graphite and QuasarDB.

All Products

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Prometheus is a service monitoring and time series database, which is open source.

IBM Informix

Informix is an embedded relational database offering from IBM.


Riak is a NoSQL database from Basho Technologies in Bellevue, Washington.


kdb+ is a time series database from kx headquartered in Palo Alto, California, a division of First Derivatives.


The InfluxDB is a time series database from InfluxData headquartered in San Francisco. As an observability solution, it is designed to provide real-time visibility into stacks, sensors and systems. It is available open source, via the Cloud as a DBaaS option, or through an Enterprise…

VictoriaMetrics Community

VictoriaMetrics is a high-performance monitoring solution and time series database

Apache Druid

Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each…

Raima Database Manager (RDM)

The Raima Database Manager (RDM) from Raima Inc in Seattle, Washington is a relational database management system.

AVEVA Historian

AVEVA Historian, formerly from Wonderware, is a time-series optimized data store, allowing the user to capture and store high-fidelity industrial big data, to unlock trapped potential for operational improvements.


QuestDB is an open source time series database. It implements SQL and exposes a Postgres wire protocol, a REST API, and supports ingestion with InfluxDB line protocol.

M3 Open Source Metrics Engine (M3DB)

M3 is a Prometheus compatible, metrics engine that provides visibility, consisting of 3 simple components for ingestion and streaming aggregation, a timeseries database, and a real-time query engine. It is available free and open source under the Apache 2.0 license, and was originally…

Circonus IRONdb

IRONdb is a time series database developed by Circonus headquartered in Fulton, designed to handle unlimited volumes of telemetry data, and billions of metric streams. It enables users to identify areas of opportunity and challenge in real time, providing forensic, predictive, and…

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KairosDB is an open source time series database.

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McObject in Federal Way, Washington offers eXtremeDB, an in-memory embedded relational database for IoT connected devices and time series analyses.

Axibase TIme Series Database

Axibase headquartered in Cupertino offers a time series database.

IBM Informix on Cloud

IMB Informix on Cloud is a DBaaS deployment of IBM's Informix database, available from IBM Cloud.

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QuasarDB is a time series database from the company of the same name in Paris.

Azure Time Series Insights

Microsoft's Azure Time Series Insights is a managed time series data analysis service for IoT.

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TDengine™ is an open-source, cloud-native time-series database (TSDB) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables the real-time ingestion, processing, and monitoring of petabytes of data per day, generated by billions of sensors and data…

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CrateDB is an open-source, distributed SQL database for relational and time-series data, from Crate.io headquartered in San Francisco. A solution for machine data, the vendor states CrateDB is purpose-built for the need to scale volume, variety and velocity of data while running…

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Kinetica, from the company of the same name headquartered in San Francisco, is an analytic database for fusing data across streams and data lakes to unlock value from spatial and temporal data at scale and speed. Kinetica helps companies drive outcomes from machine data that includes…

Grafana Mimir
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Mimir is an open source, horizontally scalable, highly available, multi-tenant TSDB for long-term storage for Prometheus.

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OpenTSDB is an open source time series database.

Amazon Timestream

Amazon AWS offers the Timestream managed time-series database service.

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Timescale headquartered in New York provides their eponymous time series database TimescaleDB in community and enterprise editions.

Learn More About Time Series Databases

What are Time Series Databases?

Time Series Databases are designed to collect and store data points that belong to a time series, meaning that the data is associated with timestamps. Time series data includes information that is collected continuously over time such as sensor readings, website data, stock prices, and other types of information collected over time. Time Series Databases are also equipped with specialized algorithms engineered to quickly query data to perform complex statistical analyses.

Time Series Databases and their analytical functionalities are used by companies for a variety of reasons. Many internet-based companies use Time Series Databases to capture behavioral data to produce user-specific advertisements. Such companies can also use these databases to monitor the health of their network and other associated systems. Companies that have physical machinery for which sensor data is constantly taken can also use these Time Series databases to predict when maintenance will be needed.

Time Series Databases Features

Most time series databases will include the following functionality:

  • Built-in Data Analytics
  • Real-time Insights
  • BI Tool Integration
  • Data Compression
  • Security
  • Data Manipulation
  • Querying Language
  • Data Retention Policies
  • Graphic User Interface
  • Data storage

Time Series Databases Comparison

When choosing a Time Series Database, prospective buyers will want to consider what type of data they will be storing. Some tools are optimized for large datasets that may come from IoT devices, but lack in their ability to store and analyze historical data. Timescale precision is another factor that will also help determine which tool is right.

Furthermore, Time Series Databases are optimized for storing and querying data that is associated with timestamps. Buyers should consider other kinds of databases (such as Relational Databases) if their data is not structured in this way.

Storage needs are another important consideration when choosing the right Time Series Database. Timestamped databases can often be quite large; therefore, one may need a tool that can easily downsample or compress the data to maintain enough storage capacity.

Pricing Information

Many Time Series Database providers offer a free version with limited features. For paid products, prospective buyers typically have two payment options. Buyers can choose a pay-as-you-go plan where the price is determined by the total amount of storage required, the query count, the number of writes, and that amount of transferred data. Flat-rate monthly payment plans geared toward larger enterprise use cases are also available.

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Frequently Asked Questions

What do time series databases do?

Time Series Databases store continuously collected information that is associated with timestamps. This type of information is called time-series or historical data. Examples include website usage data, sensor readings, historical stock prices, etc. These tools also provide the means to query and analyze the data for myriad purposes.

What are the benefits of using time series databases?

The datasets collected and stored by Time Series Databases can often be very large (often measured in petabytes). These databases are specifically designed to handle large data sets and analyze them with great speed. Implementing a Time Series Database over a standard database can save an organization time and money, especially in terms of IT resources. Time Series Databases also provide strong analyses of the data, driving better business decisions.

What are the best time series databases products?

Some popular Time Series Databases are:

How much do time series databases cost?

Many open source offerings exist for Time Series Databases (such as Druid, InfluxDB, Prometheus, among others). For paid products, prospective buyers can expect to pay either based on the amount of data stored, queried, transferred, and written or based on a monthly flat rate.