Best Time Series Databases (2026): 11 Options Compared

Every server, website and connected machine produces a steady stream of numbers: how busy it's been, how warm it's running, how many people visited in the last minute. Store that in an ordinary database and it slows to a crawl as the months pile up, which is exactly what time series databases are designed to avoid.

The eleven time series databases below are listed alphabetically and in no particular order, and every description comes from the vendor's own website, read this week. If you're mainly tracking business numbers rather than machine data, my marketing metrics cheat sheet may be all you need.

Time series databases side by side

I checked the free plan column on each tool's own website, as of Oct 2026. Yes means the site names a free plan, a free version, a free allowance or an open source version. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to start for free, so check the vendor's page before you sign up.

Tool	Type	Good for	Free plan
Amazon Timestream	Managed time series database on AWS	Time series inside an AWS setup	Not stated
Apache IoTDB	Industrial IoT time series database	Factory and sensor data	Yes
CrateDB	European real-time analytics database	Operational data with full-text and spatial	Yes
GreptimeDB	Open source observability database	Metrics, logs and traces together	Yes
InfluxDB	Widely used time series database	A large community and ecosystem	Yes
kdb+	Time series analytics platform for capital markets	Trading and financial market data	Trial only
Prometheus	Open source monitoring and time series database	Monitoring apps and servers	Yes
QuestDB	Low-latency time series database on open formats	Fast queries on streaming data	Yes
TDengine	Time series database and industrial data platform	Replacing traditional data historians	Yes
TimescaleDB	Time series on PostgreSQL	Teams who already know Postgres	Yes
VictoriaMetrics	Open source observability and metrics	Simple, efficient metrics storage	Yes

Eleven time series databases to consider

Amazon Timestream

Best for: Teams on AWS who want a managed time series service, including the option to run open source InfluxDB databases there.

Apache IoTDB

Best for: Industrial and manufacturing teams who need a lightweight time series database for sensor data with high compression and throughput.

CrateDB

Best for: Analysts who want an open source, European real-time analytics database that handles time series alongside full-text and spatial data.

GreptimeDB

Best for: Platform teams who want one open source database for metrics, logs and traces instead of a separate store for each.

InfluxDB

Best for: Companies who want a long-established time series database with a big open source community and plenty of integrations.

kdb+

Best for: Banks, hedge funds and trading firms that need very fast analysis of market data, from a vendor focused on capital markets.

Prometheus

Best for: Engineering teams who want open source metrics and monitoring for their systems and services, with a time series database built in.

QuestDB

Best for: Developers who need very low-latency queries on live and historical time series, stored on open formats they can take elsewhere.

TDengine

Best for: Industrial teams looking for a modern alternative to traditional data historians, with a free tier and an open source edition.

TimescaleDB

Best for: PostgreSQL users who want time series and real-time analytics on the database they know, from the team now trading as Tiger Data.

VictoriaMetrics

Best for: Engineers who want a simple, efficient open source metrics store with an enterprise option if their monitoring grows.

Where to start for your project

• Monitoring apps and servers: Prometheus or VictoriaMetrics.
• Teams who already know PostgreSQL: TimescaleDB.
• Factory, sensor and IoT data: Apache IoTDB or TDengine.
• Trading and market data: kdb+.
• Metrics, logs and traces together: GreptimeDB.
• A managed service on AWS: Amazon Timestream.

Seasonal patterns can fool your dashboard

The Office for National Statistics explains that data collected over time form a time series. It notes that seasonal effects, such as retail sales rising each December because of Christmas, can obscure the real movements in the data.

The same happens with website traffic, energy use and sales. Whichever database you choose, plan how you'll compare like with like, such as this December against last December, before you build the dashboards people will act on.

Monitoring databases versus analytics databases

Monitoring-focused databases are built to collect metrics from servers and apps, alert on problems and keep recent data fast. They're the usual choice for engineering teams.

Analytics-focused databases handle long histories and heavier queries, such as comparing years of sensor data. They suit industrial, financial and research work where the past matters as much as the present.

Questions to ask before you choose a time series database

1. How many data points arrive each second?
2. How long do I need to keep the history?
3. Does my team prefer SQL or another query language?
4. Will it connect to my dashboards and alerting tools?
5. Can I run it myself, or only in the cloud?
6. How well does it compress older data?

Signs you've outgrown an ordinary database

• Dashboards take ages to load. Use a time-based store.
• Storage grows every week. Compress older data.
• You keep deleting history to cope. Roll it up instead.
• Sensors send data every second. Pick a database built for it.

Do you make a time series database? Tool makers can ask to be considered for a featured place on this list.

My plain advice on choosing a time series database

Match the database to the job, not the hype. If you're monitoring servers, start with a monitoring-focused tool your team can run easily. If you're analysing years of sensor or market data, choose one built for long histories.

Decide early how long you'll keep detailed data and when you'll roll it up into summaries, because that choice drives storage more than anything else. For turning the numbers into decisions, my guide to using product analytics shows how to read the trends.

Frequently asked questions

What is a time series database?

A time series database stores data points stamped with a time, such as sensor readings or app metrics, and is built to query them quickly by period.

Are time series databases free?

Many are open source or have free tiers, and others are managed services with trials. The free plan column above shows what each vendor's site says.

Can I use a normal SQL database for time series data?

For small amounts, yes. As data grows, a time series database usually handles writes, compression and time-based queries better.

What's the difference between monitoring and analytics?

Monitoring watches what's happening now and raises alerts. Analytics looks back over longer periods to find patterns.

Do time series databases work with dashboards?

Most connect to popular dashboard and alerting tools. Check the integrations each one lists before you choose.

How long should I keep time series data?

It depends on your needs. Many teams keep detailed data for a short period and summarised data for much longer.