AWS DMS VS Debezium + Kafka

AWS Database Migration Service (DMS) was introduced as a tool to help migrate legacy databases into AWS. While it supports full-load and CDC replication, DMS is not a modern integration platform. It lacks support for modern SaaS APIs, streaming destinations, and developer-friendly deployment models.

• Available in AWS: Works within AWS without provisioning servers.
• Basic CDC support: Handles incremental replication from supported databases.

• Not real-time: DMS is not a streaming system. Latency varies and is hard to monitor in production.
• No extensibility: No community ecosystem, no custom connectors, no plugin support.
• Operational overhead: Task failures are common and require manual troubleshooting. Configuration and credential management are fragile.
• Rigid delivery options: Cannot deliver to modern analytics stacks or streaming endpoints.
• Expensive at scale: Costs add up with replication instance hours, storage, and logging. Lacks predictability for long-term CDC jobs.

Costs are based on replication instance size and duration (e.g., t3.medium ~$0.036/hr), plus storage and logs. Tasks that run continuously or process high volumes can become costly without offering the capabilities of modern platforms. There is no free tier, and pricing becomes opaque with additional monitoring and retries.

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 the right-time data platform that replaces fragmented data stacks with one dependable system for data movement. Teams use it to move data from databases, SaaS apps, files, and streams into warehouses, lakes, operational stores, and AI systems at the cadence they choose: sub-second streaming, near real-time, or scheduled batch. Founded in 2019, Estuary is built on Gazette, an open-source streaming broker developed by the same founding team that lets Estuary mix CDC, streaming, and batch in a single catalog with exactly-once delivery, deterministic recovery, and targeted backfills.

Unlike traditional ELT tools that focus on batch loads, Estuary stores every event in collections that can be reused for multiple destinations. Captured changes are written once to durable storage and fanned out to any number of targets without reloading the source, which reduces load on primary systems and makes replay easy when schemas change. Estuary runs as a multi-tenant cloud service, private data plane, or BYOC, and ships with 200+ fully-managed native connectors plus support for open-source Airbyte, Meltano, and Stitch connectors.

For AI-native workflows, Estuary ships Agent Skills that work with Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI, letting developers create captures, materialize into Snowflake, BigQuery, Redshift, or Databricks, and troubleshoot pipelines through natural-language prompts. A separate MCP server handles docs-aware Q&A inside the same assistants.

Customers include Glossier, which cut data costs by 50%; Xometry, which reduced integration costs by 60% with private deployment; Headset, which cut Snowflake ingestion costs by 40% after replacing Airbyte; and Prodege, which built Apache Iceberg pipelines.

• Right-time pipelines from millisecond streaming to scheduled batch. Choose cadence per pipeline so cost and freshness match each workload. Most ELT tools default to 15-minute or hourly intervals.
• One platform for CDC, batch, and streaming. Replaces the typical 3-4 tool stack of Debezium plus Kafka plus Airbyte plus dbt with a single system, reducing tool sprawl and operational overhead.
• Dependable replication built on Gazette. Exactly-once delivery, deterministic recovery, and targeted backfills keep pipelines stable through schema changes and source failures.
• Efficient log-based CDC with collection reuse. Captures inserts, updates, and deletes once, then fans out to any number of destinations without re-reading the source database, reducing load on production systems.
• Predictable usage-based pricing. $0.50 per GB moved plus $100 per connector instance per month for the first 6 instances, then $50 per instance for additional ones. No MAR-based surprises and no per-row charges.
• Agent-native developer experience. Open-source Agent Skills let Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI build and operate Estuary pipelines from natural language, with an MCP server for docs-aware Q&A in the same tools.

• No graphical transformation UI. Estuary focuses on SQL and TypeScript transformations alongside dbt integration. Teams that need point-and-click visual ETL like Matillion or Informatica PowerCenter will find this a gap, though dbt covers most warehouse-side needs.
• On-premises connectivity is narrower than legacy ETL vendors. For mainframe, SAP ECC on-premises, or other proprietary on-premises systems, vendors like Informatica or Talend may have broader native coverage. Verify legacy on-premises coverage during evaluation.
• Smaller market presence than category incumbents. Fivetran, Informatica (now Salesforce), and Talend (now Qlik) have larger enterprise customer bases and longer procurement track records. Estuary fits teams able to evaluate on technical merit, but buyers requiring a Gartner Magic Quadrant leader may need to factor this in.

Estuary uses a straightforward usage-based pricing model. Data movement is charged at $0.50 per GB sourced or delivered. Connector instances are $100 per month for the first 6 instances, then $50 per month for each additional instance. A Developer tier is free indefinitely up to 10 GB per month and 2 concurrent connector instances, and Cloud-tier customers can request a 30-day free trial.

Use the Estuary pricing calculator to model your specific workload. For larger deployments, Enterprise plans add volume-based discounts, SOC 2 and HIPAA compliance reports, SSO, custom SLA terms, private deployments, and dedicated support.