Confluent VS Debezium + Kafka

Confluent is the data streaming platform built around Apache Kafka. The company was started in 2014 by Jay Kreps, Neha Narkhede, and Jun Rao, the three LinkedIn engineers who created Kafka back in 2011. In December 2025, IBM announced an $11 billion all-cash acquisition of Confluent, and the deal closed on March 17, 2026. Confluent now operates as a wholly-owned IBM subsidiary inside IBM's software portfolio, alongside earlier IBM purchases like Red Hat and HashiCorp.

The product line is broader than most teams realize. Confluent Cloud is the fully managed SaaS offering, available on AWS, Azure, and Google Cloud. Confluent Platform is the self-managed version for teams that want to run Kafka on their own infrastructure. There is also Confluent Private Cloud, and WarpStream, an object-storage-based Kafka alternative that Confluent acquired in September 2024. WarpStream's diskless architecture is the cost-optimized path for storage-heavy workloads, while standard Confluent Cloud is the higher-performance path.

Around Kafka itself, Confluent ships a fairly complete ecosystem: Schema Registry for data governance, 120+ pre-built connectors, Confluent Cloud for Apache Flink for stream processing, and Tableflow for landing Kafka topics directly into Apache Iceberg or Delta Lake tables. ksqlDB is still available for SQL-based stream processing, though Flink has clearly become the primary engine on the platform. The customer base is large: more than 6,500 enterprises, including over 40% of the Fortune 500.

Recent product direction leans heavily into AI infrastructure. Confluent Intelligence, Streaming Agents, and the Real-Time Context Engine target agentic AI workflows where models and agents need continuously updated, governed context to operate on.

• Most complete Kafka ecosystem on the market. If you want Schema Registry, Flink, Tableflow for Iceberg, ksqlDB, and 120+ connectors from a single vendor, Confluent has the broadest set.
• Multiple deployment paths. Confluent Cloud (SaaS), Confluent Platform (self-managed), Confluent Private Cloud, and WarpStream's diskless architecture cover most preferences from fully managed to fully self-hosted.
• Managed Apache Flink. Confluent Cloud for Apache Flink supports Flink SQL plus Table API for Java and Python, with native integration to Schema Registry, Connectors, and Tableflow.
• WarpStream for high-volume retention. Object-storage architecture meaningfully reduces storage costs versus traditional Kafka brokers when retention windows are long.

• IBM acquisition uncertainty. The IBM deal only closed in March 2026, and customers are still watching how Confluent's pricing, packaging, and roadmap get integrated into IBM's broader portfolio. Worth weighing if long-term roadmap stability is critical to the buying decision.
• Pricing has a lot of dimensions. Charges land separately on data ingress, egress, storage, partitions, connectors, Schema Registry, Flink, and Tableflow. The model is transparent but not easy to forecast without modeling several dimensions at once, and bills tend to compound as throughput grows.
• Kafka itself is not simple. Even with the managed service, partitions, consumer groups, replication, schema evolution, and tuning are not things teams learn in a weekend. Teams new to Kafka usually spend real time on architecture and operational know-how.
• Lock-in once you are deep. Moving off Confluent Cloud to self-hosted Kafka or another managed provider means migrating topics, connectors, schemas, Flink jobs, and operational tooling. It is doable but it is not a small project.

Confluent Cloud uses usage-based pricing with separate dimensions for data ingress, egress, storage, partitions, connectors, and add-on services like Schema Registry, Flink, and Tableflow. There are multiple cluster types (Basic, Standard, Enterprise, Dedicated, Freight) at different price points. WarpStream uses its own pricing model based on its object-storage design, which often comes in lower for storage-heavy workloads. Confluent Platform is licensed separately for self-managed deployments. Small and mid-sized deployments are manageable, but at higher throughput or with several add-on services running, total cost tends to grow quickly and usually needs to be modeled across multiple dimensions before commit.

Debezium started within Red Hat following the release of Kafka, and Kafka Connect. It was inspired in part by Martin Kleppmann’s presentations on CDC and turning the database inside out.

Debezium is the open-source option for general-purpose replication, and it does many things right for replication, from scaling to incremental snapshots (make sure you use DDD-3 and not whole snapshotting). If you are committed to open source, have the specialized resources needed, and need to build your own pipeline infrastructure for scalability or other reasons, Debezium is a great choice.

Otherwise, think twice about using Debezium because it will be a big investment in specialized data engineering and admin resources. While the core CDC connectors are solid, you will need to build the rest of your data pipeline including:

• The many non-CDC source connectors you will eventually need. You can leverage all the Kafka Connect-based connectors to over 100 different sources and destinations. But they have a long list of limits (see confluent docs on limits).
• Data schema management and evolution - while the Kafka Schema Registry does support message-level schema evolution, the number of limitations on destinations and the translation from sources to message makes this much harder to manage.
• Kafka does not save your data indefinitely. There is no replay/backfilling service that manages previous snapshots and allows you to reuse them, or do time travel. You will need to build those services.
• Backfilling and CDC happens on the same topic. So if you need to redo a snapshot, all destinations will get it. If you want to change this behavior you need to have separate source connectors and topics for each destination, which adds costs and source loads.
• You will need to maintain your Kafka cluster(s), which is no small task.

If you are already invested in Kafka as your backbone, it does make good sense to evaluate Debezium.

• Real-Time CDC: Kafka + Debezium captures database changes in real-time.
• Flexibility: Debezium supports multiple databases and allows for flexible configuration options for filtering and handling database changes.
• Scalable Data Streams: Kafka’s distributed architecture ensures that even high-velocity data streams are processed efficiently and can scale horizontally.

• Complex Setup: Managing a self-hosted Kafka cluster alongside Debezium requires significant operational effort, including scaling, monitoring, and ensuring fault tolerance.
• At-Least-Once Delivery: Debezium guarantees at-least-once delivery, meaning duplicate records may need to be handled at the consumer level. This adds complexity to building exactly-once data pipelines.
• High Infrastructure Costs: Running a Kafka cluster and Debezium connectors, especially at scale, can require substantial infrastructure resources, making it more costly than other CDC alternatives.

Kafka itself is open-source and free to use, but the costs associated with deploying and maintaining a Kafka cluster can vary depending on cloud or on-premise infrastructure. Managed Kafka services such as Confluent Cloud can provide a more streamlined, albeit pricier, solution. Debezium is open-source, but operational costs come from the Kafka infrastructure and any associated storage, processing, and egress costs.

Estuary is a managed data integration platform for CDC, streaming, and batch pipelines. It supports database CDC, SaaS and API ingestion, event streams, files, warehouses, data lakes, operational systems, and AI destinations, with delivery ranging from real-time streaming to scheduled batch intervals.

At the center of Estuary is its runtime, where captured data is written to durable collections and can be independently read by multiple destinations and transformations. This separates source capture from downstream delivery and allows data to be reused for additional destinations, backfills, and reprocessing without repeatedly extracting it from the source.

Estuary offers 200+ fully managed connectors, no-code capture and materialization setup through the web application, and streaming transformations using SQL, Python, and TypeScript. It also supports dbt Cloud, materialization triggers, Agent Skills for AI-assisted pipeline development, and Public, Private, and Bring Your Own Cloud (BYOC) deployment options.

• CDC, streaming, and batch in one platform: Estuary supports log-based CDC, SaaS and API ingestion, event streams, files, and scheduled batch movement for analytical and operational use cases.
• Real-time runtime with durable collections: Estuary's runtime separates captures from materializations through durable collections, allowing data to be reused for multiple destinations, transformations, backfills, and replay without re-extracting it from the source.
• Low-latency delivery: Supported real-time pipelines can deliver data with sub-second latency, while delivery can also be scheduled when lower freshness is sufficient.
• No-code setup with advanced transformation options: Pipelines can be configured through the web application, while SQL, Python, and TypeScript transformations, dbt Cloud, APIs, and flowctl support more advanced workflows.
• Built-in monitoring and AI-assisted operations: Estuary includes logs, latency and usage metrics, pipeline alerts, OpenMetrics integrations, and Agent Skills for AI-assisted pipeline development and troubleshooting.
• Flexible deployment and predictable pricing: Public, Private, and BYOC deployments provide different levels of infrastructure control, while pricing is based on data volume and prorated connector task hours rather than Monthly Active Rows.

• Smaller managed connector catalog than some large ELT platforms: Estuary offers 200+ fully managed connectors, but some larger platforms have broader packaged coverage, particularly for long-tail SaaS and legacy enterprise applications. Custom connector development is available with Enterprise.
• No visual transformation canvas: Pipeline setup is no-code, and fields can be selected or renamed in the UI, but more complex streaming transformations use SQL, Python, or TypeScript rather than a drag-and-drop transformation canvas.
• Advanced workflows have a learning curve: Basic capture and materialization pipelines can be built through the web application, but advanced derivations, schemas, CLI workflows, and GitOps-style deployments require more data engineering familiarity. Some Estuary users on G2 also mention a learning curve for more advanced configurations.

Estuary pricing is based on data volume moved and connector instance usage. Data movement is priced at $0.50 per GB. The first six connector instances are priced at $100 per month each, while additional connector instances are $50 per month each, with usage prorated based on active connector instances.

The permanent free tier includes up to 10 GB of data movement per month and two concurrent connector instances, while the 30-day trial provides access to the full Cloud plan.

Enterprise plans can include volume discounts, Private or BYOC deployments, custom SLAs, private networking, dedicated support, and custom connector development.

Because pricing is based on GB moved plus connector usage rather than row-change metrics such as MAR, costs are relatively predictable from data volume and connector usage.