What is Apache Kafka?

Apache Kafka is an open source distributed event streaming platform. It was originally developed by LinkedIn and later donated to the Apache Software Foundation. Kafka handles large volumes of real-time data and enables the publishing, storing, and processing of streams of records efficiently.

Its architecture is based on topics, partitions, and brokers, allowing for high throughput and fault tolerance, making it suitable for building data pipelines and streaming analytics systems. Kafka’s main use cases include real-time analytics, log aggregation, data integration between systems, and event sourcing.

The platform offers durability and scalability, capable of supporting thousands of clients and petabytes of data with low latency. Its community support and integration with many data processing ecosystems have made it a popular choice for organizations aiming for scalable and reliable data streaming solutions.

This is part of a series of articles about Apache Kafka

What Is Confluent Kafka?

Confluent Kafka refers to the distribution of Kafka provided by Confluent, a company founded by the original creators of Apache Kafka. While Confluent includes the open source Kafka core, it extends functionality with additional tools, fully managed services, and enterprise-focused features. These include monitoring, security, schema management, and interfaces aimed at reducing administrative complexity and accelerating development.

Beyond the basic event streaming capabilities, Confluent Platform bundles extra components like Confluent Control Center, ksqlDB for real-time stream processing, and Confluent connectors.

Apache Kafka vs. Confluent Kafka: Key differences

1. Licensing and cost

Apache Kafka

Apache Kafka is released under the Apache License 2.0, making it free to use, modify, and distribute. This open source license allows individuals and organizations to implement Kafka without incurring licensing costs. While Kafka itself is free, there are costs associated with the infrastructure needed to deploy and scale Kafka, such as compute resources, storage, and network bandwidth.

Additionally, maintenance costs arise from managing Kafka clusters, including hardware, operational resources, and monitoring. Kafka users must also consider the cost of securing the platform, which can require additional resources for setting up encryption, authentication, and authorization.

Confluent Kafka

Confluent Kafka offers both open source and commercial options. The open source version includes the core Kafka features, but Confluent’s enterprise-level offerings come with added value.

The Confluent Platform introduces features such as Confluent Control Center (for cluster monitoring), ksqlDB (for real-time stream processing), and Schema Registry (for data format management). For these features, Confluent offers subscription-based pricing. These subscription fees are generally based on factors like cluster size, number of users, data throughput, and support requirements.

Confluent Cloud, which provides a fully managed Kafka service in the cloud, operates on a pay-as-you-go pricing model based on the resources consumed (such as storage, throughput, and API calls). This eliminates the need for organizations to handle infrastructure management, though it incurs recurring cloud-based costs.

2. Deployment models

Apache Kafka

Kafka offers flexibility in deployment models, but this comes at the cost of complexity in setup and management. Apache Kafka is generally deployed in self-managed environments, either on-premises or in the cloud. Users are responsible for configuring and managing Kafka brokers, ZooKeeper (for metadata and coordination), and ensuring high availability and scalability of their clusters. Kafka clusters typically require careful planning for resource allocation, replication factor, and partitioning to optimize performance and durability.

For scaling, users need to manually handle partition rebalancing, fault tolerance, and ensure that the underlying infrastructure can support the growing data demands. Kafka deployments can be customized extensively, which is suitable for teams with strong DevOps capabilities but may be cumbersome for others.

Confluent Kafka

Confluent Kafka simplifies deployment by offering managed solutions. Confluent Cloud abstracts much of the Kafka setup and maintenance, allowing teams to focus on developing applications rather than managing infrastructure. It handles provisioning, scaling, upgrades, and failure recovery, providing performance without the need for deep knowledge of Kafka’s internals.

For on-premises or hybrid deployments, Confluent offers Confluent Platform, which includes pre-configured Kafka and a suite of tools to ease deployment and management. This can save setup time and simplify administration by providing integrated components like Confluent Hub for connectors and Control Center for monitoring.

3. Support

Apache Kafka

As an open source project, Kafka relies on community support. Users can access resources including documentation, user forums, Slack channels, and Stack Overflow discussions. The community is active, with regular contributions from developers around the world. However, the support offered through community channels can be slow and inconsistent, and may not always resolve issues promptly.

For organizations requiring more reliable and dedicated support, they may need to work with third-party service providers or set up in-house expertise. Open source support does not guarantee timely resolution for mission-critical issues.

Confluent Kafka

Confluent provides commercial support for enterprises through Confluent Support, which includes options such as 24/7 help, access to technical account managers, and tailored support for large-scale Kafka deployments. Confluent Cloud customers receive proactive monitoring, automated diagnostics, and troubleshooting assistance.

Support packages range from basic to premium, based on customer needs and the criticality of their use case. Enterprise-level support includes access to exclusive features, like dedicated engineers and on-site support. Confluent’s expert team also offers consulting services, helping organizations design and deploy Kafka architectures optimized for their needs, further reducing operational risks.

4. Operational overhead

Apache Kafka

Operational overhead for Apache Kafka can be significant, especially as clusters grow in size. Deploying and managing Kafka involves configuring and maintaining multiple Kafka brokers, ZooKeeper instances, and ensuring data replication, partitioning, and balancing. Scaling Kafka clusters to handle higher throughput involves manually adding brokers, adjusting partition sizes, and tuning system parameters like producer/consumer configurations and retention policies.

Kafka also requires careful management of system resources to avoid bottlenecks or data loss, as well as regular upgrades and patching. Kafka’s complexity increases with larger deployments, and managing Kafka clusters at scale requires expertise in distributed systems and data engineering. High availability, replication, and fault tolerance all add to the operational burden.

Confluent Kafka

Confluent Kafka reduces operational overhead by automating many of the maintenance tasks involved with managing a Kafka cluster. With Confluent Cloud, many of these tasks (such as scaling, provisioning, and upgrading) are handled automatically, freeing up resources to focus on building applications rather than maintaining infrastructure.

For on-premises deployments, Confluent Platform offers management tools like Confluent Control Center, which simplifies monitoring, troubleshooting, and performance tuning. Automated scaling, backup, and security configuration further ease the administrative workload. For organizations that lack in-house Kafka expertise, Confluent Cloud offers a fully managed service with ongoing monitoring and proactive alerts, reducing the operational burden to nearly zero.

5. Data connectors

Apache Kafka

Kafka’s Kafka Connect framework is the primary tool for integrating Kafka with external systems, such as databases, data lakes, and file systems. It allows users to easily connect Kafka to numerous data sources and sinks, enabling data ingestion and export. While Kafka Connect provides many out-of-the-box connectors for popular data systems, users often need to implement custom connectors for specialized systems, which can increase development time.

The community maintains a broad repository of connectors, but integrating and managing them often requires manual configuration. Additionally, organizations may face challenges in terms of connector compatibility, versioning, and maintaining data consistency across distributed systems.

Confluent Kafka

Confluent Kafka offers a wide range of pre-built Confluent connectors, available through the Confluent Hub, which simplifies the integration process. These connectors cover various data systems, including cloud services, relational and NoSQL databases, analytics tools, and other enterprise systems. Many of these connectors are developed and maintained by Confluent, ensuring they are optimized for Kafka performance and scalability.

By leveraging ksqlDB and Schema Registry, Confluent improves the connector ecosystem, making it easier to handle schema evolution and provide real-time stream processing with SQL-like queries. This reduces the effort required for building and maintaining data pipelines, providing a more flexible solution for complex data architectures.

6. Security

Apache Kafka

Apache Kafka provides fundamental security features, including SSL encryption for data in transit, SASL authentication for user and service identity verification, and ACLs (access control lists) for fine-grained authorization. However, setting up and configuring these features can be complex and requires careful planning. Kafka’s security model is not as user-friendly, and it may be difficult to configure for teams without expertise in security or distributed systems.

Kafka also lacks integrated key management and advanced auditing capabilities, which organizations must implement independently if needed.

Confluent Kafka

Confluent Kafka includes a more comprehensive and easier-to-use security model. It provides out-of-the-box encryption for both data in transit and data at rest, along with improved authentication (e.g., OAuth, LDAP, Kerberos) and role-based access control (RBAC). The Confluent Control Center offers integrated security monitoring and auditing capabilities, making it easier to track security events and manage access.

Confluent also simplifies the management of sensitive data by integrating key management systems (KMS) and offering more robust security features in Confluent Cloud, which ensures compliance with enterprise security standards and regulatory requirements. This reduces the time and effort needed to implement security measures while providing greater peace of mind for organizations handling sensitive data.

Related content: Read our guide to Kafka management

Tips from the expert

Andrew Mills

Andrew Mills

Senior Solution Architect

Andrew Mills is an industry leader with extensive experience in open source data solutions and a proven track record in integrating and managing Apache Kafka and other event-driven architectures.

In my experience, here are tips that can help you better navigate the choice and implementation of Apache Kafka vs Confluent Kafka:

  1. Evaluate your team’s operational expertise: Open source Apache Kafka provides unparalleled flexibility but requires in-depth knowledge of distributed systems for tasks like cluster setup, scaling, and fault tolerance. If your team has strong DevOps or SRE capabilities, self-managed Kafka may be ideal. Otherwise, consider managed services (e.g., Instaclustr) or Confluent for reducing operational overhead.
  2. Analyze feature dependencies: Determine if your use case demands advanced features like ksqlDB for stream processing, Schema Registry for schema evolution, or Confluent Control Center for monitoring. If these are critical, Confluent may be worth the investment. For teams leveraging open-source tools, alternatives like Apache Flink or Prometheus can often fill these gaps without proprietary lock-in.
  3. Plan for scalability and performance optimization: For high-throughput workloads, ensure your Kafka architecture is designed for scalability—consider partitioning strategies, replication factors, and resource allocation. Managed open-source Kafka services can automate scaling and performance tuning, while Confluent’s managed cloud offering provides similar benefits but at a higher cost and with less flexibility.
  4. Prioritize vendor neutrality and cost predictability: If avoiding vendor lock-in is a priority, stick with open source Kafka or managed services that operate exclusively on open-source technology (e.g., Instaclustr). These solutions provide flat-rate pricing and full control over your infrastructure, unlike Confluent’s tiered pricing and proprietary ecosystem, which can limit flexibility and increase long-term costs.

Pros and cons of Apache Kafka

Apache Kafka is widely used in many organizations for building scalable, high-performance streaming systems. While it offers great flexibility, there are trade-offs to consider.

Pros:

  • Open source and free: No licensing costs for the core Kafka features.
  • Highly scalable: Can handle large volumes of data with low latency, making it suitable for big data applications.
  • Fault tolerant: Kafka’s distributed architecture ensures high availability and durability, even in the face of node failures.
  • Strong community support: The open source nature of Kafka allows access to extensive community resources, forums, and documentation.
  • Flexibility in deployment: Kafka can be deployed on-premises or in the cloud with customizable configurations to meet requirements.

Cons:

  • Complex to set up and manage: Kafka requires in-depth expertise to configure, deploy, and scale clusters effectively.
  • Manual maintenance: Operational overhead can be high as users are responsible for managing infrastructure, scaling, and fault tolerance.
  • Limited enterprise features: Lacks some of the monitoring, security, and management features that can simplify operations for large-scale deployments.
  • Steep learning curve: New users may struggle with Kafka’s complexity, particularly around cluster management and stream processing.

Pros and cons of Confluent Kafka

Confluent Kafka builds upon the core Apache Kafka platform, offering added enterprise features and managed services to reduce complexity. However, there are some trade-offs involved with using Confluent’s extended ecosystem.

Pros:

  • Managed services: Confluent Cloud offers fully managed Kafka clusters, handling scaling, provisioning, and maintenance, allowing teams to focus on application development.
  • Enhanced features: Includes monitoring (Control Center), real-time stream processing (ksqlDB), and Schema Registry for easier data governance.
  • Enterprise-grade security: Simplified security setup with integrated encryption, advanced authentication, and role-based access control (RBAC).
  • Comprehensive support: Offers 24/7 support, dedicated account managers, and professional services for mission-critical deployments.
  • Pre-built connectors: A wide range of optimized connectors are available for integrating with external systems, reducing custom development time.

Cons:

  • Licensing costs: Commercial offerings, including Confluent Cloud, come with subscription fees based on cluster size, data throughput, and support requirements.
  • Vendor lock-in: Using Confluent’s fully managed service may lead to dependency on their platform, which could limit flexibility in the long term.
  • Limited customization: While the managed service simplifies setup, some users may find it limiting compared to the full control offered by self-managed Kafka deployments.
  • Complexity for small-scale use: For small-scale or non-enterprise use cases, the additional features and cost of Confluent Kafka may not justify the investment.

Confluent Kafka vs. Apache Kafka: How to choose

When deciding between Confluent Kafka and true open source Apache Kafka, there are several factors to consider based on the organization’s needs, technical capabilities, and budget. Below are the key considerations to help guide your decision:

  • Open source freedom: If maintaining full control over your data and infrastructure is a priority, true open-source Apache Kafka is the clear choice. It avoids the proprietary features and vendor lock-in associated with Confluent, giving you the flexibility to innovate on your terms.
  • Cost transparency: For organizations seeking predictable costs, open source Kafka—especially when paired with a managed service—offers flat-rate pricing models. Confluent’s tiered and usage-based pricing can lead to unexpected expenses, particularly for high-throughput workloads.
  • Operational simplicity: If your team lacks the resources or expertise to manage Kafka clusters, Confluent’s managed services may seem appealing. However, managed open source Kafka services provide the same operational ease—handling provisioning, scaling, and maintenance—without the added cost of proprietary licensing.
  • Vendor neutrality: For businesses that value flexibility and want to avoid being locked into a single ecosystem, open source Kafka is the better option. It ensures you can switch providers or bring operations in-house without disruption.
  • Expert support: If enterprise-grade support is critical, both Confluent and managed open source Kafka providers offer 24/7 assistance. However, open source solutions often provide this at a lower cost while maintaining transparency and flexibility.
  • Scalability and performance: For organizations with growing data needs, both options can scale effectively. However, open source Kafka—especially with managed services—delivers high performance without the constraints of proprietary tools, making it ideal for businesses that prioritize long-term scalability.

How to choose:

  • Opt for Confluent Kafka if you need enterprise features like ksqlDB or Control Center and are comfortable with higher costs and vendor lock-in for added convenience.
  • Choose true open source Apache Kafka if you value freedom, cost efficiency, and the ability to customize your infrastructure. Pairing it with a managed service like NetApp Instaclustr can provide the same ease of use as Confluent, without sacrificing flexibility or transparency.

By carefully assessing your priorities—whether it’s cost, control, or convenience—you can make an informed decision that aligns with your business goals.

Managed Apache Kafka services: A superior alternative to Confluent

When considering event streaming platforms, managed Kafka services offer a compelling alternative to Confluent’s proprietary Kafka solution. These services, such as those provided by NetApp Instaclustr, deliver the full power of open source Apache Kafka while eliminating the operational complexity associated with self-managed deployments. Instaclustr combines the full capabilities of Apache Kafka with expert support, proactive monitoring, and a flat-rate pricing model that eliminates the unpredictability of Confluent’s tiered costs

By operating exclusively on open source Kafka, Instaclustr ensures vendor neutrality, giving businesses the freedom to scale and innovate without lock-in. With 24/7 support and seamless management, Instaclustr empowers organizations to focus on growth, not infrastructure headaches—delivering a level of transparency, reliability, and flexibility that Confluent simply can’t match.

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