Best Cloud Storage Services for Apache Kafka

Compare the Top Cloud Storage Services that integrate with Apache Kafka as of July 2025

This a list of Cloud Storage services that integrate with Apache Kafka. Use the filters on the left to add additional filters for products that have integrations with Apache Kafka. View the products that work with Apache Kafka in the table below.

What are Cloud Storage Services for Apache Kafka?

Cloud storage services enable the storage, management, and backup of data on the Internet via the cloud. Compare and read user reviews of the best Cloud Storage services for Apache Kafka currently available using the table below. This list is updated regularly.

  • 1
    MayaData

    MayaData

    MayaData

    OpenEBS with Kubera is the answer. OpenEBS is the most popular open source storage for Kubernetes - and the fastest. Kubera adds an easy to use GUI for OpenEBS Mayastor - and APIs, auto checks and configuration, active directory authentication, built-in performance benchmarking, and additional operators to assist in upgrades and other use cases. Kubera is freely available and is delivered by MayaData when providing 24/7 support for customers to reduce operational costs and complexity. Kubera Propel is a cloud-native declarative data plane written in Rust. It’s built on the open source OpenEBS Mayastor. Kubera Propel combines breakthrough technologies like NVMe, SPDK, and emerging new storage features in the Linux kernel. OpenEBS managed by Kubera Propel has been shown independent benchmarks to deliver extremely low latency performance for data bases and other workloads running on Kubernetes.
    Starting Price: $100 per node per month
  • 2
    Pravega

    Pravega

    Pravega

    Distributed messaging systems such as Kafka and Pulsar have provided modern Pub/Sub infrastructure well suited for today’s data-intensive applications. Pravega further enhances this popular programming model and provides a cloud-native streaming infrastructure, enabling a wider swath of applications. Pravega streams are durable, consistent, and elastic, while natively supporting long-term data retention. Pravega solves architecture-level problems that former topic-based systems Kafka and Pulsar have failed to solve, such as auto-scaling of partitions or maintaining high performance for a large number of partitions. It enhances the range of supported applications by efficiently handling both small events as in IoT and larger data as in videos for computer vision/video analytics. By providing abstractions beyond streams, Pravega also enables replicating application state and storing key-value pairs.
  • Previous
  • You're on page 1
  • Next