Compare the Top Time Series Databases that integrate with Amazon S3 as of October 2025

This a list of Time Series Databases that integrate with Amazon S3. Use the filters on the left to add additional filters for products that have integrations with Amazon S3. View the products that work with Amazon S3 in the table below.

What are Time Series Databases for Amazon S3?

Time series databases (TSDB) are databases designed to store time series and time-stamped data as pairs of times and values. Time series databases are useful for easily managing and analyzing time series. Compare and read user reviews of the best Time Series Databases for Amazon S3 currently available using the table below. This list is updated regularly.

  • 1
    Cortex

    Cortex

    The Cortex Authors

    Cortex is an open source project that adds horizontal scalability. While Prometheus can scale up to 1 million samples/sec on a single machine, with Cortex horizontal scalability is practically limitless. In a constantly changing environment, you need alternative approaches to monitoring individual VMs or servers. Prometheus' service-discovery driven pull-based metrics system was designed for the dynamic nature of microservices. It lets you easily monitor your whole environment no matter how many moving parts. Instrument your application to create custom metrics using standard Prometheus client libraries, or take advantage of the extensive collection of Prometheus Exporters that collect data from existing applications like MySQL, Redis, Java, ElasticSearch and many more.
  • 2
    Warp 10
    Warp 10 is a modular open source platform that collects, stores, and analyzes data from sensors. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 is both a time series database and a powerful analytics environment, allowing you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system.
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