List of the Top 9 Time Series Databases for Amazon Web Services (AWS) in 2025

InfluxDB is a specialized data platform crafted to manage all types of time series data, encompassing users, sensors, applications, and infrastructure, allowing for the seamless collection, storage, visualization, and transformation of insights into actionable strategies. It features a comprehensive library of over 250 open-source Telegraf plugins, simplifying the process of importing and monitoring data from a variety of systems. By empowering developers, InfluxDB facilitates the creation of innovative IoT, monitoring, and analytics applications and services. Its adaptable architecture can accommodate various implementations, whether in the cloud, at the edge, or on-premises. Moreover, its versatility, ease of access, and an array of supporting tools such as client libraries and APIs enable developers of all experience levels to swiftly create applications and services utilizing time series data. The platform is optimized for enhancing developer productivity and efficiency, allowing builders to concentrate on the essential features that add value to their internal projects and provide their applications with a competitive advantage. To assist newcomers, InfluxData provides complimentary training through InfluxDB University, ensuring that anyone can quickly acquire the skills needed to leverage this powerful platform effectively.

Telegraf serves as an open-source server agent designed to efficiently gather metrics from various sensors, stacks, and systems. Acting as a plugin-centric agent, it not only collects but also transmits metrics and events from a diverse array of sources including systems, databases, and IoT devices. Engineered in Go, it compiles into a single binary, requiring no external dependencies and consuming minimal memory. Telegraf supports a vast range of input sources, allowing for the seamless writing of data to numerous output destinations. With its plugin architecture, it is effortlessly extendable for both data collection and output purposes. Additionally, Telegraf boasts over 300 plugins developed by community data experts, making the collection of metrics from your endpoints a straightforward task. This flexibility and community support make Telegraf an invaluable tool for monitoring and performance analysis.

VictoriaMetrics is an economical and scalable option for monitoring that also functions as a time series database. Additionally, it is suitable for the long-term storage of Prometheus data. This solution operates as a standalone executable without requiring any external dependencies. Configuration is managed through clear command-line flags along with sensible defaults. Notably, it offers a global query view, enabling multiple Prometheus instances or other data sources to input data into VictoriaMetrics. Users can later retrieve this data with a single query. It effectively addresses challenges related to high cardinality and high churn rates by implementing a series limiter, ensuring optimal performance and reliability. Furthermore, its user-friendly setup makes it accessible for a wide range of applications.

Instaclustr, a company focused on Open Source-as-a-Service, ensures dependable performance at scale. Our services encompass database management, search functionalities, messaging solutions, and analytics, all within a reliable, automated managed environment that has been tested and proven. By partnering with us, organizations can direct their internal development and operational efforts towards building innovative applications that enhance customer experiences. As a versatile cloud provider, Instaclustr collaborates with major platforms including AWS, Heroku, Azure, IBM Cloud, and Google Cloud Platform. In addition to our SOC 2 certification, we pride ourselves on offering round-the-clock customer support to assist our clients whenever needed. This comprehensive approach to service guarantees that our clients can operate efficiently and effectively in their respective markets.

Apache Druid stands out as a robust open-source distributed data storage system that harmonizes elements from data warehousing, timeseries databases, and search technologies to facilitate superior performance in real-time analytics across diverse applications. The system's ingenious design incorporates critical attributes from these three domains, which is prominently reflected in its ingestion processes, storage methodologies, query execution, and overall architectural framework. By isolating and compressing individual columns, Druid adeptly retrieves only the data necessary for specific queries, which significantly enhances the speed of scanning, sorting, and grouping tasks. Moreover, the implementation of inverted indexes for string data considerably boosts the efficiency of search and filter operations. With readily available connectors for platforms such as Apache Kafka, HDFS, and AWS S3, Druid integrates effortlessly into existing data management workflows. Its intelligent partitioning approach markedly improves the speed of time-based queries when juxtaposed with traditional databases, yielding exceptional performance outcomes. Users benefit from the flexibility to easily scale their systems by adding or removing servers, as Druid autonomously manages the process of data rebalancing. In addition, its fault-tolerant architecture guarantees that the system can proficiently handle server failures, thus preserving operational stability. This resilience and adaptability make Druid a highly appealing option for organizations in search of dependable and efficient analytics solutions, ultimately driving better decision-making and insights.

Amazon FinSpace enhances the deployment of kdb Insights applications on AWS by efficiently managing the essential tasks involved in provisioning, integrating, and securing infrastructure specifically designed for kdb Insights. The platform includes intuitive APIs that allow clients to establish and operate new kdb Insights applications in just a few minutes. Moreover, it provides the flexibility for customers to migrate their existing kdb Insights applications to AWS, enabling them to take advantage of cloud computing benefits while alleviating the costly and cumbersome responsibilities tied to self-managing their infrastructure. KX’s kdb Insights is recognized as a high-performance analytics engine, tailored for analyzing both real-time and extensive historical time-series data, making it a preferred option for Capital Markets clients engaged in critical business functions such as options pricing, transaction cost analysis, and backtesting. In addition, Amazon FinSpace streamlines the deployment process by removing the necessity to integrate over 15 different AWS services to deploy kdb. Consequently, businesses can dedicate more time and resources to their primary operations without being hindered by the complexities of infrastructure management, ultimately leading to improved operational efficiency and effectiveness.

kdb Insights is a cloud-based advanced analytics platform designed for rapid, real-time evaluation of both current and historical data streams. It enables users to make well-informed decisions quickly, irrespective of data volume or speed, and offers a remarkable price-performance ratio, delivering analytics that is up to 100 times faster while costing only 10% compared to other alternatives. The platform features interactive visualizations through dynamic dashboards, which facilitate immediate insights that are essential for prompt decision-making. Furthermore, it utilizes machine learning models to enhance predictive capabilities, identify clusters, detect patterns, and assess structured data, ultimately boosting AI functionalities with time-series datasets. With its impressive scalability, kdb Insights can handle enormous volumes of real-time and historical data, efficiently managing loads of up to 110 terabytes each day. Its swift deployment and easy data ingestion processes significantly shorten the time required to gain value, while also supporting q, SQL, and Python natively, and providing compatibility with other programming languages via RESTful APIs. This flexibility allows users to seamlessly incorporate kdb Insights into their current workflows, maximizing its potential for various analytical tasks and enhancing overall operational efficiency. Additionally, the platform's robust architecture ensures that it can adapt to future data challenges, making it a sustainable choice for long-term analytics needs.

Machbase is an advanced time-series database specifically engineered for the immediate storage and analysis of extensive sensor data originating from various facilities. This database management system (DBMS) is unique in its capability to swiftly process and analyze large datasets, highlighting its remarkable performance. Users can leverage the incredible speed of data processing that Machbase provides. The product is designed to facilitate instant management, storage, and analysis of sensor data. By embedding the DBMS into Edge devices, it ensures quick data storage and querying. Moreover, it excels in both data storage and retrieval when utilized on a single server. With the option to set up multi-node clusters, Machbase enhances both availability and scalability. Alongside these features, it functions as an all-in-one management solution for Edge computing, efficiently handling device management, connectivity, and data processing requirements. In an ever-evolving, data-centric landscape, Machbase emerges as an indispensable resource for sectors that depend on the timely analysis of sensor data, ultimately driving innovation and efficiency. Its robust capabilities position it as a frontrunner in the realm of real-time data solutions.

Circonus IRONdb streamlines the storage and management of infinite telemetry data, effortlessly handling billions of metric streams with remarkable ease. It enables users to swiftly identify both opportunities and challenges as they arise in real time, delivering exceptional capabilities in forensic, predictive, and automated analytics. Leveraging machine learning, the system automatically defines a "new normal" as operational data evolves, adapting to changes seamlessly. Furthermore, Circonus IRONdb integrates smoothly with Grafana, which directly supports its analytics query language, and is also compatible with various other visualization platforms like Graphite-web. To safeguard data integrity, Circonus IRONdb creates multiple copies across a network of IRONdb nodes, ensuring durability and availability. Although system administrators typically manage clustering, they often spend a significant amount of time maintaining its performance and reliability. In contrast, Circonus IRONdb allows operators to configure their clusters for autonomous operation, freeing them to concentrate on strategic initiatives rather than the monotonous tasks of managing time series data storage. This efficient methodology not only boosts productivity but also optimizes the use of available resources, ultimately leading to a more effective operational structure. With these capabilities, Circonus IRONdb stands out as an essential tool for organizations looking to harness the full potential of their telemetry data.