Best In-Memory Databases for Kubernetes
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Compare the Top In-Memory Databases that integrate with Kubernetes as of June 2025
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This a list of In-Memory Databases that integrate with Kubernetes. Use the filters on the left to add additional filters for products that have integrations with Kubernetes. View the products that work with Kubernetes in the table below.
What are In-Memory Databases for Kubernetes?
In-memory databases store data directly in a system’s main memory (RAM) rather than on traditional disk-based storage, enabling much faster data access and processing. This approach significantly reduces latency and increases performance, making in-memory databases ideal for real-time analytics, high-frequency transactions, and applications requiring rapid data retrieval. They are often used in industries like finance, telecommunications, and e-commerce, where speed and scalability are critical. In-memory databases support both SQL and NoSQL models and typically include features for data persistence to avoid data loss during system shutdowns. Ultimately, they provide high-speed performance for time-sensitive applications while ensuring data availability and integrity. Compare and read user reviews of the best In-Memory Databases for Kubernetes currently available using the table below. This list is updated regularly.
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SingleStore
SingleStore
SingleStore (formerly MemSQL) is a distributed, highly-scalable SQL database that can run anywhere. We deliver maximum performance for transactional and analytical workloads with familiar relational models. SingleStore is a scalable SQL database that ingests data continuously to perform operational analytics for the front lines of your business. Ingest millions of events per second with ACID transactions while simultaneously analyzing billions of rows of data in relational SQL, JSON, geospatial, and full-text search formats. SingleStore delivers ultimate data ingestion performance at scale and supports built in batch loading and real time data pipelines. SingleStore lets you achieve ultra fast query response across both live and historical data using familiar ANSI SQL. Perform ad hoc analysis with business intelligence tools, run machine learning algorithms for real-time scoring, perform geoanalytic queries in real time.Starting Price: $0.69 per hour -
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Tarantool
Tarantool
Corporations need a way to ensure uninterrupted operation of their systems, high speed of data processing, and reliability of storage. The in-memory technologies have proven themselves well in solving these problems. For more than 10 years, Tarantool has been helping companies all over the world build smart caches, data marts, and golden client profiles while saving server capacity. Reduce the cost of storing credentials compared to siloed solutions and improve the service and security of client applications. Reduce data management costs of maintaining a large number of disparate systems that store customer identities. Increase sales by improving the speed and quality of customer recommendations for goods or services through the analysis of user behavior and user data. Improve mobile and web channel service by accelerating frontends to reduce user outflow. IT systems of large organizations operate in a closed loop of a local network, where data circulates unprotected. -
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Oracle Coherence
Oracle
Oracle Coherence is the industry leading in-memory data grid solution that enables organizations to predictably scale mission-critical applications by providing fast access to frequently used data. As data volumes and customer expectations increase, driven by the “internet of things”, social, mobile, cloud and always-connected devices, so does the need to handle more data in real-time, offload over-burdened shared data services and provide availability guarantees. The latest release of Oracle Coherence, 14.1.1, adds a patented scalable messaging implementation, support for polyglot grid-side programming on GraalVM, distributed tracing in the grid, and certification on JDK 11. Coherence stores each piece of data within multiple members (one primary and one or more backup copies), and doesn't consider any mutating operation complete until the backup(s) are successfully created. This ensures that your data grid can tolerate the failure at any level: from single JVM, to whole data center.
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