Confluent Platform vs Apache Kafka: Feature Comparison

Apache Kafka is one of the most widely adopted data streaming platforms in the world and is used extensively by Fortune 100 companies. While Kafka originated as an open-source project, it is also heavily used in corporate and regulated environments where operational maturity, security, and disaster recovery are critical.

Confluent Platform builds on top of open-source Apache Kafka and adds a set of enterprise-grade features aimed at simplifying operations, improving security, and supporting complex production use cases. And now, when IBM has announced its $11B acquisition of Confluent, interest in understanding the differences between open-source Kafka and Confluent Platform is likely to increase.

In this article, we will cover key Confluent Platform features that are not available (or not fully available) in open-source Apache Kafka, focusing on aspects that are especially important for production deployments.

Audit logs

In many organizations – especially regulated industries such as finance, healthcare, or government – security departments require detailed audit logs. These logs are essential for compliance, incident investigation, and forensic analysis.

Open-source Apache Kafka does not provide a built-in audit logging mechanism. While it is possible to approximate auditing by enabling broker logs or using custom authorizers, these solutions are usually incomplete, difficult to maintain, and not standardized.

Confluent Platform provides Audit Logs through its security authorizer, enabled via configuration settings. Authorization events (such as access granted or denied) are written directly to a dedicated Kafka topic. Each audit log entry typically includes:

• Principal (user or service account)
• Operation (READ, WRITE, DESCRIBE, etc.)
• Resource (topic, consumer group, cluster, etc.)
• Timestamp
• Result (allowed or denied)
• Client metadata (IP, listener, protocol)

Because audit logs are stored in Kafka itself, they can be easily integrated with SIEM systems, long-term storage, or monitoring tools. This feature is often a hard requirement in regulated environments and is a strong differentiator compared to open-source Kafka.

Role-Based Access Control (RBAC)

Open-source Apache Kafka provides Access Control Lists (ACLs) as its primary authorization mechanism. While ACLs are powerful and flexible, they can become difficult to manage in large environments with:

• Hundreds of topics
• Many teams and applications
• Frequent onboarding and offboarding

Confluent Platform introduces Role-Based Access Control (RBAC), which simplifies authorization by grouping permissions into predefined roles. Instead of managing hundreds or thousands of ACL entries, administrators assign roles to users or service accounts.

Examples of predefined roles include:

• DeveloperRead
• DeveloperWrite
• ResourceOwner
• SecurityAdmin
• ClusterAdmin

RBAC significantly reduces operational complexity and the risk of misconfiguration. It also integrates seamlessly across the Confluent ecosystem, including:

• Kafka brokers
• Schema Registry
• Kafka Connect
• ksqlDB
• Control Center

This unified security model makes RBAC particularly attractive for large organizations with multiple teams and shared Kafka infrastructure.

Cluster Linking

For multi-cluster and disaster recovery scenarios, open-source Kafka typically relies on MirrorMaker 2. While functional, MirrorMaker 2 operates as a Kafka Connect-based solution and introduces additional operational overhead and latency.

Confluent Platform offers Cluster Linking, a replication mechanism built directly into the broker layer. Because it operates at the byte level and does not rely on Kafka Connect, Cluster Linking:

• has lower latency,
• uses fewer resources,
• is easier to operate and monitor.

One of the most important advantages of Cluster Linking is the ability to replicate consumer group offsets. This is critical in disaster recovery (DR) scenarios. If the active cluster fails, consumers can switch to the passive (DR) cluster and continue processing from nearly the same offsets, without manual offset resets or data reprocessing.

Cluster Linking supports common use cases such as:

• active–passive DR,
• data migration between clusters,
• hybrid and multi-cloud architectures.

Stretched Cluster 2.5 and Observers

In open-source Kafka, multi–data center (DC) deployments typically follow one of these patterns:

• active–passive,
• active–active,
• three–data center quorum-based clusters.

The three-DC approach requires Kafka brokers (or ZooKeeper/KRaft controllers) and storage in all three locations, which:

• increases infrastructure costs,
• requires very low inter-DC latency,
• adds operational complexity.

Confluent Platform introduces Stretched Cluster 2.5, which offers a middle ground between active–active and three-DC architectures:

• data is stored in two data centers,
• KRaft controllers are deployed across three data centers,
• quorum is maintained via 3 or 5 KRaft nodes.

This design provides:

• automatic failover (no manual intervention),
• lower storage costs compared to three-DC clusters,
• better disaster recovery characteristics than active–passive setups.

Observers

Confluent also adds Observers, which extend Kafka’s replication model. In open-source Kafka, partitions consist of leaders and followers, and all replicas participate synchronously in ISR (in-sync replicas).

Observers are asynchronous replicas that:

• do not participate in ISR under normal conditions,
• can be promoted to full replicas when certain conditions are met (e.g., ISR falls below min.insync.replicas).

This mechanism allows data to be stored safely across multiple data centers without sacrificing availability or increasing write latency. Observers enable more flexible replication strategies and make it possible to enforce durable cross-DC storage while keeping performance predictable.

Read also Understanding in-sync replicas and replication factor in Confluent Stretched Cluster 2.5

Self-Balancing Cluster

In large Kafka clusters, routine operations such as:

• adding new brokers,
• decommissioning old ones,
• increasing partition counts,
  can be operationally expensive. Rebalancing partitions typically requires manual intervention or external tools, and mistakes can lead to uneven load, hot partitions, or performance degradation.

Confluent Platform introduces Self-Balancing Clusters, which continuously run in the background and automatically:

• redistribute partitions,
• balance leadership,
• optimize disk and network usage.

The process is incremental and non-disruptive, reducing operational risk and significantly simplifying cluster management at scale.

Control Center

While open-source Kafka relies on third-party tools (such as Grafana, Prometheus, or custom scripts), Confluent Platform includes Control Center, a fully integrated management and monitoring UI.

Control Center provides:

• end-to-end data flow visibility,
• consumer lag monitoring,
• cluster health and performance metrics,
• security and RBAC visibility,
• Kafka Connect and ksqlDB management.

Although Grafana dashboards are powerful and open source, Control Center offers a Kafka-aware, opinionated view of the platform that is especially valuable for operations and troubleshooting.

Commercial support + Commercial Connectors

Confluent Platform includes enterprise-grade support, which can be crucial for mission-critical systems. This includes:

• SLAs,
• access to Kafka experts,
• faster resolution of complex production issues.

In addition, Confluent offers commercial Kafka Connect connectors for systems such as:

• mainframes,
• enterprise databases,
• SaaS platforms.

These connectors are typically better tested, better supported, and more reliable than community alternatives.

Apache Kafka vs. Confluent – Summary

Confluent Platform introduces several powerful features that go beyond open-source Apache Kafka, particularly in the areas of:

• security (Audit Logs, RBAC),
• disaster recovery (Cluster Linking, Stretched Cluster 2.5, Observers,)
• operations (Self-Balancing Clusters, Control Center).

Not every organization needs these features, and open-source Kafka remains an excellent choice for many use cases. However, in regulated environments, large-scale deployments, or systems with strict availability and security requirements, these features are well worth considering.

At SoftwareMill, we provide Apache Kafka services on a daily basis, helping organizations design, build, and operate event-driven platforms that scale safely and reliably. As a Confluent Elite Partner, we support companies across the entire Kafka lifecycle: from architecture and implementation to optimization and long-term operation.

Reviewed by: Grzegorz Kocur