Debezium Alternatives: What to Use Instead, and When to Stay

The right Debezium alternative depends on what is hurting. If the pain is running Kafka Connect, keep Debezium and run it without Kafka (Debezium Server or the embedded Engine). If the pain is operating CDC at all, use a managed service: Confluent Cloud's managed connectors still run the Debezium engine, while AWS DMS, Google Datastream, Fivetran, Estuary and Striim are separate products. If you only need MySQL or PostgreSQL changes in one place, the database's own replication or a small tool such as Maxwell can be enough.

Debezium is not a fading project. It is Apache 2.0 licensed, hosted by the Commonhaus Foundation, and its latest stable release, 3.7.0.Final, shipped on 29 September 2026. That is why most change data capture tools that people shortlist against it are a different way to run, buy or avoid the same log reading, not better log reading. This guide covers the common reasons teams look, then the options and their real trade-offs. It is written for engineers choosing among CDC tools, including the "Debezium vs Fivetran" and "Debezium vs Airbyte" questions.

• Kafka Connect load. Debezium's usual home is Kafka Connect, so a CDC pipeline needs a Kafka cluster, a Connect cluster and, for current releases, Kafka Connect 3.1 or later. If the changes only need to reach a queue or a cache, that is a lot of infrastructure. See what Kafka is used for to check you need it for more than CDC.
• Snapshot pain. A new connector snapshots every captured table before it streams. Debezium's answer is incremental snapshots: chunks of 1,024 rows by default, run alongside streaming, resumable after an interruption, started with an execute-snapshot signal. They need a signaling table (signal.data.collection), and if you manage the PostgreSQL publication yourself, the publication must include that table. Teams who skip this setup find out during the first re-snapshot.
• Schema changes. The MySQL, SQL Server, Oracle and Db2 connectors keep table definitions in a schema history topic that must not be partitioned. The connector rebuilds table structures from it on restart, so if it is lost or incomplete the connector cannot start cleanly. Downstream, an incompatible column change reaches consumers as a broken event unless a schema registry rejects it first.
• Replication slots and logs. On PostgreSQL a stopped or lagging connector holds WAL on the primary until the disk fills. Debezium's documentation recommends heartbeat.interval.ms and watching confirmed_flush_lsn against restart_lsn in pg_replication_slots. Our replication slot support case is the typical outcome of ignoring that.
• Connector gaps. Debezium covers a long database list, but check yours. SQL Server 2025 is not on the 3.7 tested-versions list (2017, 2019 and 2022 are), Cassandra 3 is no longer supported in 3.7, and SaaS applications such as Salesforce are outside its scope.

Three of the five problems above are about the runtime, not the capture, so try these first.

Debezium Server is a ready-to-run application that streams changes straight to Amazon Kinesis, Google Cloud Pub/Sub, Apache Pulsar, Azure Event Hubs, Redis Streams, NATS JetStream, RabbitMQ Streams, Kafka, an HTTP endpoint, JDBC or Apache Iceberg, among others, and can keep offsets and schema history in files. It needs Java 21 or later, and each instance runs exactly one connector. Debezium Engine embeds the connector in your own Java application, and the Quarkus extensions, Spring Integration and Apache Camel build on it. The cost is in the failure model: after a crash the application may see events it already handled, up to several batches depending on batch size and the offset flush interval (60 seconds by default), so handlers must be idempotent. For Kubernetes, the Debezium Operator and Debezium Platform declare pipelines as resources.

Whichever runtime you pick, the operational basics are the same: a heartbeat on PostgreSQL, JMX metrics scraped into Prometheus (Debezium's maintainers acknowledged in May 2026 that it ships no built-in monitoring UI), a signal table set up before you need it, and idempotent consumers. Our Kafka event-driven architecture guide covers the outbox pattern, the most common reason to run CDC at all.

Licences and pricing models below are from each vendor's own site as of October 2026. Prices change, so each model is named, not quoted.

These are different categories, which is why the comparison disappoints both sides. Debezium emits a stream of row changes in commit order for services to react to. Fivetran and Airbyte are ELT products that land tables in a warehouse on a schedule. Airbyte's documentation is explicit that it does not treat CDC sources as infinite streaming sources: syncs run on a schedule and read the log from the last position to the sync start. If your consumer is a warehouse and you have no Kafka team, they are usually simpler than Debezium plus a sink. If your consumers are services, caches or search indexes that need events within seconds, they are not substitutes.

• Consumers are services: stay on Debezium. Pick Kafka Connect if you already run Kafka, Debezium Server if you do not.
• Consumer is a warehouse: a managed ELT tool or your cloud's native service (Datastream, DMS, Fabric) is less work than Debezium plus Kafka plus a sink.
• One MySQL or PostgreSQL source, one destination: database-native replication or Maxwell before anything heavier.
• Regulated or air-gapped: self-run, open-source options keep data inside your boundary; vendor SaaS does not.
• Whatever you pick: prove the failure paths, not the demo. Stop the consumer for an hour, run a DDL change, and restore a snapshot.

AceMQ's Debezium support engineers run connectors on PostgreSQL, MySQL, SQL Server and Oracle, including snapshots, slot and WAL incidents and schema history recovery, and our Kafka support team covers the Connect cluster underneath. For one insurance carrier we replaced a Kafka CDC pipeline with standalone Debezium publishing to RabbitMQ; the case study describes the approach.

• Debezium: releases and tested versions
• Debezium: Debezium Server
• Debezium: Debezium Engine (failure handling)
• Debezium: PostgreSQL connector (incremental snapshots, WAL disk space)
• Debezium: MySQL connector (schema history topic)
• Debezium blog: What Nobody Explains About Debezium in 2026
• Confluent Cloud: PostgreSQL CDC Source V2 (Debezium)
• AWS DMS: Kafka as a target
• AWS DMS: pricing
• Google Cloud: Datastream overview
• Google Cloud: Datastream pricing
• Microsoft Learn: Fabric mirroring from SQL Server, limitations
• Microsoft Learn: change event streaming (preview)
• Airbyte: change data capture
• Airbyte: license FAQ (ELv2)
• Fivetran: pricing
• Estuary: repository and BSL licence
• Striim: pricing
• Qlik: Replicate data capture
• Apache Flink CDC: sources overview
• PostgreSQL: logical replication restrictions
• Maxwell's daemon: repository
• Canal: repository