StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Kafka Tools
  5. Confluent vs Kafka Manager

Confluent vs Kafka Manager

OverviewComparisonAlternatives

Overview

Kafka Manager
Kafka Manager
Stacks70
Followers173
Votes1
Confluent
Confluent
Stacks337
Followers239
Votes14

Confluent vs Kafka Manager: What are the differences?

Introduction

This Markdown provides a detailed comparison between Confluent and Kafka Manager, outlining the key differences between the two. Markdown formatting is used to present the information in a format that can be easily utilized on a website.

  1. Deployment and Scalability: Confluent is a fully managed Kafka service offered by Confluent Inc., providing a cloud-based solution for deploying and scaling Kafka clusters. On the other hand, Kafka Manager is an open-source tool that allows managing multiple Kafka clusters. While Confluent offers a seamless deployment experience for Kafka, Kafka Manager is more suitable for managing Kafka clusters on your own infrastructure.

  2. User Interface and Monitoring: Confluent includes a user-friendly web-based interface, allowing users to easily manage and monitor their Kafka clusters. It provides comprehensive monitoring capabilities, real-time metrics, and an intuitive user experience. In contrast, Kafka Manager also offers a web-based user interface but with fewer monitoring features and a less polished user experience.

  3. Management Features: Confluent provides a wide range of management features, such as automatic scaling, fault tolerance, data replication, and automated backups. It also offers advanced security features like data encryption and access control. On the other hand, Kafka Manager focuses more on basic management features like creating and managing topics, partitions, and producers/consumers.

  4. Integration with the Confluent Ecosystem: One of the key differences is that Confluent is part of a larger Confluent Ecosystem, which includes additional services like Confluent Platform, Confluent Hub, and Schema Registry. This integration enables seamless integration with other Confluent components and provides a comprehensive ecosystem for Kafka-based applications. Kafka Manager, being an open-source tool, doesn't have the same level of integration with the Confluent ecosystem.

  5. Support and Documentation: Confluent offers professional support and enterprise-grade documentation. They provide timely support and assistance to their customers, ensuring smooth operations and faster issue resolution. In contrast, Kafka Manager being an open-source tool, relies more on community support and may have limited documentation and support options.

  6. Pricing Model: Confluent follows a subscription-based pricing model, offering different tiers with varying levels of features and support. The pricing includes the cost of managed infrastructure, technical support, and additional services. Kafka Manager, being an open-source tool, is free to use but requires self-management of infrastructure and relies on community support.

In summary, Confluent provides a fully managed Kafka service with comprehensive features, seamless integration with the Confluent ecosystem, and professional support. Kafka Manager, on the other hand, is an open-source tool suitable for managing Kafka clusters on your own infrastructure, offering basic management features and community support.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Kafka Manager
Kafka Manager
Confluent
Confluent

This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

Manage multiple clusters;Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution);Run preferred replica election;Generate partition assignments (based on current state of cluster);Run reassignment of partition (based on generated assignments)
Reliable; High-performance stream data platform; Manage and organize data from different sources.
Statistics
Stacks
70
Stacks
337
Followers
173
Followers
239
Votes
1
Votes
14
Pros & Cons
Pros
  • 1
    Better Insights for Kafka cluster
Pros
  • 4
    Free for casual use
  • 3
    Dashboard for kafka insight
  • 3
    No hypercloud lock-in
  • 2
    Zero devops
  • 2
    Easily scalable
Cons
  • 1
    Proprietary
Integrations
Kafka
Kafka
Microsoft SharePoint
Microsoft SharePoint
Java
Java
Python
Python
Salesforce Sales Cloud
Salesforce Sales Cloud
Kafka Streams
Kafka Streams

What are some alternatives to Kafka Manager, Confluent?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase