AWS DMS VS Confluent

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.

Confluent Cloud is a fully managed service built on top of Apache Kafka, the distributed streaming platform. Confluent Cloud abstracts away some of Kafka’s operational complexity, making it easier for organizations to leverage real-time data streaming. Confluent also offers tools such as ksqlDB and Schema Registry to simplify stream processing and schema management.

• Managed Service: Confluent Cloud eliminates the need for operational management, including Kafka cluster setup, scaling, and maintenance.
• Wide Ecosystem: Confluent integrates seamlessly with a variety of cloud services, databases, and messaging systems.
• Enterprise Features: Confluent Cloud offers additional enterprise features, including Confluent Schema Registry, ksqlDB for stream processing, and connectors.
• Scalability: As a managed Kafka offering, Confluent scales elastically to accommodate high throughput with little manual intervention.

• Cost Complexity: While Confluent Cloud provides a simplified pricing model, usage-based billing can quickly become expensive as data volumes increase, especially for organizations that have high-throughput streaming needs.
• Vendor Lock-In: Relying on Confluent Cloud can lead to vendor lock-in, as migrating to a different Kafka provider or setting up self-hosted Kafka clusters could require significant effort.
• Operational Limits: Although much of the Kafka infrastructure is managed, some complex Kafka configurations and optimizations may not be accessible or customizable in Confluent Cloud.

Confluent Cloud's pricing is usage-based, with separate charges for data ingress, egress, storage, and additional services like the Schema Registry and ksqlDB. Throughput prices are variable depending on the pricing tier and total volume. Additional costs apply for partitions, connectors, and the use of advanced features. For small to mid-sized use cases, the cost is manageable, but at scale, expenses can rise quickly.

Estuary is the right time data platform that replaces fragmented data stacks with one dependable system for data movement. Instead of juggling separate tools for CDC, batch ELT, streaming, and app syncs, teams use Estuary 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, near real time, or scheduled.

The company was founded in 2019, built on Gazette, a battle tested streaming storage layer that has powered high volume event workloads for years. That foundation lets Estuary mix CDC, streaming, and batch in a single catalog and gives customers exactly once delivery, deterministic recovery, and targeted backfills across all of their pipelines.

Unlike traditional ELT tools that focus on batch loads into a warehouse, Estuary stores every event in collections that can be reused for multiple destinations and use cases. Once a change is captured, it is written once to durable storage and then fanned out to any number of targets without reloading the source. This reduces load on primary systems, provides consistent history for analytics and AI, and makes it easy to replay or reprocess data when schemas or downstream models change.

Estuary can run as a multi tenant cloud service, as a private data plane inside the customer’s cloud, or in a BYOC model where the customer owns the infrastructure and Estuary manages the control plane. This gives security and compliance teams the control they expect from in house systems with the convenience of a managed platform.

Estuary also has broad packaged and custom connectivity, making it one of the top ETL tools. The platform ships with a growing set of high quality native connectors for databases, warehouses, lakes, queues, SaaS tools, and AI targets. Estuary also supports many open source connectors where needed, so teams can consolidate around one system while still covering niche sources and destinations. Customers consistently highlight predictable pricing, strong reliability, and partner level support as key reasons they choose Estuary instead of Fivetran, Airbyte, or DIY stacks.

Estuary Flow is highly rated on G2, with users highlighting its real-time capabilities and ease of use.

• Right time pipelines: Estuary lets you choose the cadence of each pipeline, from sub second streaming to periodic batch, so cost and freshness match the workload.
• One platform for all data movement: Handles CDC, batch loads, and streaming in one product, which reduces tool sprawl and simplifies operations.
• Dependable replication: Exactly once delivery, deterministic recovery, and targeted backfills keep pipelines stable even when sources or schemas change.
• Efficient CDC: Log based CDC captures inserts, updates, and deletes once and reuses them for many destinations, reducing load on operational databases.
• High scale architecture: Gazette and collections support large, continuous data streams with reliable throughput across multiple targets.
• Modern transforms: Supports SQL and TypeScript based transformations in motion, and integrates cleanly with dbt for warehouse side ELT.
• Flexible deployment choices: Available as cloud SaaS, private data plane, or BYOC, giving enterprises strong control over data residency and security.
• Predictable total cost of ownership: Transparent pricing based on data volume and connector instances avoids MAR based surprises and is easy to forecast.
• Fast time to value: A guided UI, CLI, and templates help most teams build their first dependable pipelines in hours instead of weeks.
• Partner level support: Customers report quick connector delivery, responsive troubleshooting, and SLAs that make Estuary feel like an extension of their team.

• On premises connectors: Estuary has 200+ native connectors and supports 500+ Airbyte, Meltano, and Stitch open source connectors. But if you need on-premises app or data warehouse connectivity, make sure you have all the connectivity you need.
• Graphical ETL: Estuary has been more focused on SQL and dbt than graphical transformations. While it does infer data types and convert between sources and targets, there is currently no graphical transformation UI.

Of the various ELT and ETL vendors, Estuary is the lowest total cost option. Estuary only charges $0.50 per GB of data moved from each source or to each target, and $100 per connector per month. Rivery, the next lowest cost option, is the only other vendor that publishes pricing of 1 RPU per 100MB, which is $7.50 to $12.50 per GB depending on the plan you choose. Estuary becomes the lowest cost option by the time you reach the 10s of GB/month. By the time you reach 1TB a month Estuary is 10x lower cost than the rest.