Confluent vs Apache Kafka: Key Differences, Pros & Cons

Apache Kafka is an open-source event streaming platform used to publish, store, and process streams of events at scale. But running Kafka in production also means managing brokers, scaling, upgrades, monitoring, security, and the surrounding infrastructure.

Confluent Cloud takes a different approach. Built around Apache Kafka, it adds managed infrastructure and services for connecting, processing, governing, and monitoring streaming data. Confluent Cloud handles much of the operational work that teams would otherwise manage themselves with open-source Kafka.

So the choice between Apache Kafka and Confluent often comes down to control versus operational simplicity: do you want to build and operate the Kafka infrastructure yourself, or use a managed platform with additional services around it?

Estuary fits into this comparison differently. It is a data integration platform for change data capture (CDC) , streaming, and batch data movement rather than a Kafka distribution or alternative message broker. For teams whose main goal is moving operational data between databases, warehouses, and other systems, Estuary can provide a simpler path without requiring Kafka to be the center of the architecture.

In this guide, we’ll compare Apache Kafka, Confluent, and Estuary across architecture, operations, connectors, pricing, and common use cases so you can see where each option fits.

Distributed Streaming Platforms – A Brief Overview

Data streaming platforms continuously capture, store, and process events as they happen. An event could be a database update, an application log, a payment, an IoT reading, or a message produced by another service.

Unlike a traditional batch pipeline, which collects data and processes it on a schedule, streaming systems are designed to make new events available continuously. This makes them useful for use cases such as fraud detection, operational analytics, application events, monitoring, and real-time data pipelines.

Databases and streaming platforms also serve different purposes. A database primarily stores the current state of data so applications can query and update it. An event streaming platform such as Apache Kafka stores ordered streams of events so multiple downstream consumers can process those events independently.

Distributed streaming platforms spread data and processing across multiple machines. This helps them scale throughput, tolerate infrastructure failures, and support multiple producers and consumers without requiring every system to communicate directly with every other system.

In practice, streaming platforms are often used alongside databases rather than replacing them. For example, change data capture (CDC) can capture changes from a database and publish them as a continuous stream for warehouses, applications, search systems, or other downstream consumers.

What Is Apache Kafka?

What Is Apache Kafka?

Apache Kafka is an open-source distributed event streaming platform used to publish, store, and process streams of events at scale. It is commonly used for event-driven applications, log aggregation, streaming analytics, data pipelines, and communication between distributed services.

Kafka organizes events into topics, which are divided into partitions and distributed across brokers in a cluster. Producers write events to topics, while consumers read those events independently and can replay them when needed.

Modern Kafka clusters use KRaft for metadata management and cluster coordination. Starting with Kafka 4.0, ZooKeeper mode was removed, simplifying the architecture by eliminating the need to operate a separate ZooKeeper ensemble.

Kafka also includes tools such as Kafka Connect for integrating Kafka with external systems and Kafka Streams for building stream-processing applications.

Because Apache Kafka is self-managed software, teams are responsible for deploying, scaling, monitoring, upgrading, securing, and maintaining the cluster unless they use a managed Kafka service.

Advantages of Apache Kafka

• High throughput: Kafka is designed to handle large volumes of events across distributed clusters.
• Scalable architecture: Topics can be partitioned across multiple brokers, allowing teams to scale producers, consumers, and storage as workloads grow.
• Durable event storage: Kafka can retain events for a configured period, allowing consumers to replay historical data when needed.
• Multiple independent consumers: Different applications can consume the same event stream without interfering with one another.
• Large ecosystem: Kafka has a mature ecosystem that includes Kafka Connect, Kafka Streams, managed Kafka services, and a broad set of third-party integrations.

Disadvantages of Apache Kafka

• Operational complexity: Self-managing Kafka requires teams to handle deployment, scaling, monitoring, upgrades, security, capacity planning, and failure recovery.
• Connector management: Kafka Connect provides the framework for integrating external systems, but connector availability, quality, licensing, and maintenance can vary by vendor or project.
• Not a complete data integration platform: Kafka moves and stores event streams, but teams may still need additional tools for CDC, transformations, schema management, orchestration, and loading data into downstream systems.
• Resource requirements: Production Kafka clusters can require significant compute, storage, and operational expertise, especially at higher throughput or retention levels.
• Architecture overhead for simple pipelines: For teams that only need to move data from a database into a warehouse or another destination, running Kafka may introduce more infrastructure than necessary.

What Is Confluent Kafka?

Confluent is a commercial data streaming platform built around Apache Kafka. Its managed cloud offering, Confluent Cloud, handles much of the infrastructure work involved in running Kafka and adds services for connectivity, stream processing, governance, security, and monitoring.

Confluent Cloud is available across AWS, Azure, and Google Cloud. It also provides managed Kafka connectors, Schema Registry, Stream Governance, and Apache Flink for stream processing.

Advantages of Confluent

• Managed Kafka infrastructure: Confluent Cloud handles cluster provisioning, upgrades, scaling, and much of the day-to-day operational work.
• Managed connectors: Confluent provides 100+ fully managed connectors for databases, SaaS applications, warehouses, object storage, messaging systems, and other services.
• Stream processing: Confluent Cloud for Apache Flink supports filtering, joins, enrichment, and other real-time processing workloads.
• Governance and schemas: Schema Registry, Stream Governance, lineage, and catalog features help teams manage streaming data across larger environments.
• Multi-cloud support: Confluent Cloud runs across AWS, Azure, and Google Cloud.

Disadvantages of Confluent

• Higher cost than self-managed Kafka: You pay for the managed infrastructure and additional platform services rather than operating open-source Kafka yourself.
• Pricing can be complex: Costs can depend on cluster type, throughput, connectors, stream processing, governance, networking, and other services used.
• Still requires Kafka knowledge: Confluent removes much of the infrastructure management, but teams still need to understand topics, partitions, consumer groups, schemas, and streaming architecture.
• Can be more platform than you need: If your main requirement is simply replicating data from databases or SaaS systems into a warehouse, a dedicated data integration platform may require less architectural overhead.

What Is Estuary?

Estuary is a data integration platform for change data capture (CDC), streaming, and batch data movement. Unlike Apache Kafka or Confluent, Estuary is not a Kafka distribution or message broker. It is designed to capture data from operational systems, transform it when needed, and deliver it to databases, warehouses, lakes, streaming platforms, and other destinations.

Estuary supports 200+ managed connectors across databases, SaaS applications, cloud storage, warehouses, and streaming systems. For supported databases, it can use log-based CDC to capture inserts, updates, and deletes continuously rather than repeatedly scanning entire source tables.

Advantages of Estuary

• CDC, streaming, and batch in one platform: Teams can use continuous or scheduled data movement without maintaining separate architectures for each pattern.
• 200+ managed connectors: Supports databases, SaaS applications, data warehouses, object storage, and streaming platforms.
• Kafka integration: Estuary can capture from or deliver data to Kafka when Kafka is already part of the architecture.
• Managed operations: Connector infrastructure, scaling, recovery, and pipeline monitoring are handled by the platform rather than requiring teams to operate Kafka clusters themselves.
• Flexible deployment: Estuary supports managed cloud, private, and BYOC deployment options.

Disadvantages of Estuary

• Not a general-purpose event broker: Teams building event-driven microservices or using Kafka as a shared application messaging backbone may still need Kafka or another messaging platform.
• Managed platform tradeoff: Estuary provides less low-level infrastructure control than running Kafka directly.
• Connector-based pricing: Costs depend on both data volume and active connector instances, so pricing should be modeled against the expected workload.

Estuary is a better fit when the main problem is moving and synchronizing data between systems rather than building a general-purpose event streaming backbone. For example, a team replicating PostgreSQL changes into Snowflake may be able to use Estuary directly without deploying Kafka, Kafka Connect, and additional CDC infrastructure.

Is Kafka An ETL Tool?

Traditional ETL has all but faded from the mainstream with it not being suitable for the demands of the data-driven future. Instead, businesses have begun adopting newer data pipeline architectures. This includes real-time data streaming ETL which allows data movement and transformation at a larger scale.

Kafka is the primary tool most companies use when setting up real-time ETL pipelines. 

To summarize: Kafka isn’t necessarily an ETL tool, but it can be used to create them.

Confluent is a great example of how Kafka can be used to create a platform that resembles ETL in some ways but is still a broader framework overall.

This is also where Estuary comes into the frame. Built from the ground up as a real-time ELT, the platform can help with transformations, and low-latency materializations, and provide instant CDC (Change Data Capture). With its primary focus on end-to-end pipelines built with data connectors, the Estuary user interface is more similar to modern ELT or ETL Tools.

Apache Kafka vs Confluent vs Estuary

Which One Should You Choose?

Choose Apache Kafka if you need a general-purpose event streaming backbone and want full control over the infrastructure.

Choose Confluent if you want Kafka without managing most of the underlying infrastructure and also need managed connectors, governance, and stream processing.

Choose Estuary if your main goal is continuously moving and synchronizing data between operational systems and analytical destinations without making Kafka the center of the architecture.

Pricing: Apache Kafka vs Confluent vs Estuary

Pricing works very differently across these three options.

Apache Kafka

Apache Kafka is open source, so there is no software license fee. However, self-managed Kafka still has infrastructure and operating costs, including compute, storage, networking, monitoring, upgrades, and engineering time.

For larger production environments, these operational costs can be significant even though Kafka itself is free to use.

Confluent

Confluent Cloud uses consumption-based pricing. Costs can include Kafka compute capacity, data transfer, storage, managed connectors, stream processing, governance, and networking.

The total cost therefore depends on the cluster tier, throughput, retention, connectors, and additional Confluent services being used. Teams should model pricing against their expected workload rather than comparing only the starting cluster price.

Estuary

Estuary pricing is based primarily on data movement and active connector instances. The current Cloud plan is priced at $0.50 per GB of data moved plus $0.14 per connector hour, with lower connector pricing available as usage grows.

Estuary also offers a free tier with 10 GB of data movement per month and up to two concurrent connector instances, making it possible to test smaller pipelines before moving to a paid plan.

How the Pricing Models Differ

Apache Kafka gives teams the most infrastructure control, but the cost of operating the platform is largely their responsibility. Confluent turns Kafka into a managed service, with pricing spread across multiple infrastructure and platform components.

Estuary is priced around the data integration workload itself: how much data is moved and how many connectors are running. For teams comparing the platforms, the most useful approach is to estimate the full cost of the architecture, including infrastructure, connectors, processing, storage, and engineering time.

Conclusion

Apache Kafka, Confluent, and Estuary solve different parts of the streaming and data movement problem.

Choose Apache Kafka if you need a general-purpose event streaming platform and want full control over the infrastructure.

Choose Confluent if you want Kafka with managed infrastructure, connectors, governance, and stream processing.

Choose Estuary if your main goal is moving and synchronizing data across databases, warehouses, SaaS applications, and other systems using CDC, streaming, or batch pipelines without managing Kafka infrastructure yourself.

The right choice depends on whether you are building an event streaming backbone or primarily solving a data integration problem.

To test out Estuary, start your free trial, or contact our team for a free consultation.