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DirectoryData & AnalyticsStreaming & Real-Time AnalyticsStrimzi

Strimzi

Open Source

About Strimzi

Strimzi provides a way to run an Apache Kafka cluster on Kubernetes in various deployment configurations. For development, a cluster can be set up in Minikube in a few minutes. For production, the cluster can be tailored using features such as rack awareness to spread brokers across availability zones, and Kubernetes taints and tolerations to run Kafka on dedicated nodes. Kafka can be exposed outside Kubernetes using NodePort, Load balancer, Ingress and OpenShift Routes, and secured using TLS. Kafka topics, users, Kafka MirrorMaker and Kafka Connect can be managed using Custom Resources. The project is a Cloud Native Computing Foundation incubating project.

Products

  • Strimzi Kafka Operators: Used to deploy and configure an Apache Kafka cluster on Kubernetes.
  • HTTP Bridge: Provides a RESTful interface for HTTP clients.

Used for

  • Running an Apache Kafka cluster on Kubernetes in various deployment configurations.
  • Setting up a cluster in Minikube for development.
  • Spreading brokers across availability zones using rack awareness.
  • Running Kafka on dedicated nodes using Kubernetes taints and tolerations.
  • Managing Kafka topics, users, Kafka MirrorMaker and Kafka Connect using Custom Resources.

From Strimzi's own website, read 2026-10-23. How we build listings.

How to evaluate Streaming & Real-Time Analytics

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Strimzi. It comes from our Streaming Data & Event Processing buyer guide.

25%
Throughput, Latency & Durability
Sustained throughput and tail latency at your partition count, ordering and exactly-once / at-least-once delivery guarantees, replication and multi-AZ durability, retention limits, and behavior under broker failure and rebalancing
20%
Operating Model & Architecture
Self-managed vs. fully managed vs. serverless vs. bring-your-own-cloud, KRaft (post-ZooKeeper) operations, diskless / object-storage-backed options, autoscaling, partition rebalancing, and upgrade and patching burden on your team
20%
Kafka-API Compatibility & Portability
Fidelity of the Kafka protocol (consumer groups, transactions, compaction), reuse of existing clients, connectors and tooling, lock-in to a single cloud, and the realistic effort to migrate in or out
15%
Stream Processing & Ecosystem
Native or integrated processing (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB), connector catalog (CDC, sinks, sources), schema registry and governance, and direct landing into Iceberg / Delta lakehouse tables
10%
Security, Governance & Compliance
Encryption in transit and at rest, mTLS / SASL / OAuth and RBAC, audit logging, private networking, data residency and BYOC data-boundary control, and SOC 2 / ISO 27001 / GDPR coverage
10%
Cost Model & Economics at Scale
Pricing unit (capacity / throughput / consumption), inter-AZ and egress charges, storage and retention cost, processing billed separately, and how the bill behaves as volume, partitions, and connectors grow

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

strimzi.io
CategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics
DeploymentKubernetes-based

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