Estuary

AWS DMS VS Estuary Flow

Read this detailed 2025 comparison of AWS DMS vs Estuary Flow. Understand their key differences, core features, and pricing to choose the right platform for your data integration needs.

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Comparison between AWS DMS and Estuary Flow
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Table of Contents

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Introduction

Do you need to load a cloud data warehouse? Synchronize data in real-time across apps or databases? Support real-time analytics? Use generative AI?

This guide is designed to help you compare AWS DMS vs Estuary Flow across nearly 40 criteria for these use cases and more, and choose the best option for you based on your current and future needs.

Comparison Matrix: AWS DMS vs Estuary Flow

AWS DMS logo
AWS DMS
Estuary Flow logo
Estuary Flow
Database replication (CDC)AWS DMSOracle, SQL Server, MySQL, PostgreSQL, MongoDB, etc. (full load and CDC, but limited to AWS-centric use cases)Estuary FlowMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batch
Operational integrationAWS DMS

DMS is not a general-purpose data integration platform. No support for SaaS connectors, event streaming, or cross-cloud delivery.

Estuary Flow

Real-time ETL data flows ready for operational use cases.

Data migrationAWS DMS

Mainly built for one-time lift-and-shift database migrations. Lacks real pipeline orchestration or reusability.

Estuary Flow

Great schema inference and evolution support.

Support for most relational databases.

Continuous replication reliability.

Stream processingAWS DMS

Not supported. No event streaming or integration with systems like Kafka or Kinesis.

Estuary Flow

Real-time ETL in Typescript and SQL

Operational analyticsAWS DMS

Only works for CDC into AWS-native targets like Redshift. Limited visibility and control over latency and freshness.

Estuary Flow

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

AI pipelinesAWS DMS

Not supported. DMS cannot deliver to vector databases or support real-time AI/ML pipelines.

Estuary Flow

Pinecone support for real-time data vectorization.

Transformations can call ChatGPT & other AI APIs.

Number of connectorsAWS DMSLimited to 30+ legacy database and message queue endpointsEstuary Flow150+ high performance connectors built by Estuary
Streaming connectorsAWS DMSNo streaming or pub/sub support. Only proprietary CDC for supported databases.Estuary FlowCDC, Kafka, Kinesis, Pub/Sub
3rd party connectorsAWS DMS

Not extensible. No community ecosystem or third-party integrations.

Estuary Flow

Support for 500+ Airbyte, Stitch, and Meltano connectors.

Custom SDKAWS DMS

Not supported. No ability to build or extend connectors.

Estuary Flow

SDK for source and destination connector development.

Request a connectorAWS DMS
Estuary Flow

Connector requests encouraged. Swift response.

Batch and streamingAWS DMSNot true streaming. Delivers data in small CDC bursts with added latency.Estuary FlowBatch and streaming
Delivery guaranteeAWS DMSAt-least-once. Requires manual deduplication at the destination.Estuary FlowExactly once (streaming, batch, mixed)
ELT transformsAWS DMS

Limited to basic column renaming, filtering, and casting. No enrichment or joins.

Estuary Flow

dbt integration

ETL transformsAWS DMS

Not supported at all. Transformations must be handled entirely outside of DMS.

Estuary Flow

Real-time, SQL and Typescript

Load write methodAWS DMSInsert, update, delete; no support for soft deletes or log-based replay.Estuary FlowAppend only or update in place (soft or hard deletes)
DataOps supportAWS DMS

No versioning, no CI/CD support, no “as code” pipelines. Monitoring limited to CloudWatch metrics.

Estuary Flow

API and CLI support for operations.

Declarative definitions for version control and CI/CD pipelines.

Schema inference and driftAWS DMS

Basic mapping with minimal customization. Complex schemas require manual tuning.

Estuary Flow

Real-time schema inference support for all connectors based on source data structures, not just sampling.

Store and replayAWS DMS

No staging or persistence. If a pipeline fails, the only option is to re-run the full job.

Estuary Flow

Can backfill multiple targets and times without requiring new extract.

User-supplied cheap, scalable object storage.

Time travelAWS DMS

Not supported. No access to historical versions or rewind functionality.

Estuary Flow

Can restrict the data materialization process to a specific date range.

SnapshotsAWS DMS

Supports initial full-load only. Not useful for ongoing use cases or fast reloads.

Estuary Flow

Full or incremental

Ease of useAWS DMS

Integrated into AWS, but operations can be resource-intensive.

Estuary Flow

Low- and no-code pipelines, with the option of detailed streaming transforms.

Deployment optionsAWS DMSOnly deployable as a managed AWS service. No hybrid or BYOC support.Estuary FlowOpen source, public cloud, private cloud
SupportAWS DMS

Depends on your AWS account tier.

Estuary Flow

Fast support, engagement, time to resolution, including fixes.

Slack community.

Performance (minimum latency)AWS DMSLatency ranges from seconds to minutes. No sub-second streaming or guarantees.Estuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline.
ReliabilityAWS DMSMedium. Failures are not automatically retried and require manual reconfigurations.Estuary FlowHigh
ScalabilityAWS DMSManual scaling only. No autoscaling or elastic provisioning.Estuary FlowHigh 5-10x scalability of others in production
SOC2AWS DMS

AWS DMS itself is not SOC 2 certified. It inherits AWS platform compliance but lacks service-specific attestations.

Estuary Flow

SOC 2 Type II with no exceptions

Data source authenticationAWS DMSHTTPS / SSH / SSLEstuary FlowOAuth 2.0 / API Tokens SSH/SSL
EncryptionAWS DMSEncryption at rest, in-motionEstuary FlowEncryption at rest, in-motion
HIPAA complianceAWS DMS

HIPAA compliance is not explicitly guaranteed for AWS DMS. Customers must architect and validate HIPAA-compliant solutions manually using the broader AWS ecosystem.

Estuary Flow

HIPAA compliant with no exceptions

Vendor costsAWS DMS

Charged per hour per replication instance, plus log and storage usage. Hard to predict costs for long-running tasks.

Estuary Flow

2-5x lower than the others, becomes even lower with higher data volumes. Also lowers cost of destinations by doing in place writes efficiently and supporting scheduling.

Data engineering costsAWS DMS

Frequent engineering involvement to debug replication failures, latency issues, and configuration mismatches.

Estuary Flow

Focus on DevEx, up-to-date docs, and easy-to-use platform.

Admin costsAWS DMS

Admin effort required for pipeline setup, task recovery, and credential rotation.

Estuary Flow

“It just works”

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AWS DMS

Amazon DMS - ETL Tool

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.

Pros

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

Cons

  • 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.

AWS DMS Pricing

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.

Estuary Flow

Estuary introductory image

Estuary was founded in 2019. But the core technology, the Gazette open source project, has been evolving for a decade within the Ad Tech space, which is where many other real-time data technologies have started.

Estuary Flow is the only real-time and ETL data pipeline vendor in this comparison. There are some other ETL and real-time vendors in the honorable mention section, but those are not as viable a replacement for Fivetran. Estuary Flow is also a great option for batch sources and targets.

Where Estuary Flow really shines is in any combination of change data capture (CDC), real-time and batch ETL or ELT, and loading multiple destinations with the same pipeline. Estuary Flow currently is the only vendor to offer a private cloud deployment, which is the combination of a dedicated data plane deployed in a private customer account that is managed as SaaS by a shared control plane. It combines the security and dedicated compute of on-prem with the simplicity of SaaS.

CDC works by reading record changes written to the write-ahead log (WAL) that records each record change exactly once as part of each database transaction. It is the easiest, lowest latency, and lowest-load for extracting all changes, including deletes, which otherwise are not captured by default from sources. Unfortunately ELT vendors like Airbyte, Fivetran, Meltano, and Hevo all rely on batch mode for CDC. This puts a load on a CDC source by requiring the write-ahead log to hold onto older data. This is not the intended use of CDC and can put a source in distress, or lead to failures.

Estuary Flow has a unique architecture where it streams and stores streaming or batch data as collections of data, which are transactionally guaranteed to deliver exactly once from each source to the target. With CDC it means any (record) change is immediately captured once for multiple targets or later use. Estuary Flow uses collections for transactional guarantees and for later backfilling, restreaming, transforms, or other compute. The result is the lowest load and latency for any source, and the ability to reuse the same data for multiple real-time or batch targets across analytics, apps, and AI, or for other workloads such as stream processing, or monitoring and alerting.

Estuary Flow also has broad packaged and custom connectivity, making it one of the top ETL tools. It has 150+ native connectors that are built for low latency and/or scale. While this number may seem low, these are high-quality, standardized connectors. In addition, Estuary is the only vendor to support Airbyte, Meltano, and Stitch connectors, which easily adds 500+ more connectors. Getting official support for the connector is a quick “request-and-test” with Estuary to make sure it supports the use case in production. Most of these connectors are not as scalable as Estuary-native, Fivetran, or some ETL connectors, so it’s important to confirm they will work for you. Flow’s support for TypeScript and SQL transformations also enables ETL.

Pros

  • Modern data pipeline: Estuary Flow has the best support for schema drift, evolution, and automation, as well as modern DataOps.
  • Modern transforms: Flow is also both low-code and code-friendly with support for SQL and TypeScript (with Python on the way) for ETL, and dbt for ELT.
  • Lowest latency: Several ETL vendors support low latency. But of these Estuary can achieve the lowest, with sub-100ms latency. ELT vendors generally are batch only. 
  • High scale: Unlike most ELT vendors, leading ETL vendors do scale. Estuary is proven to scale with one production pipeline moving 7GB+/sec at sub-second latency.
  • Most efficient: Estuary alone has the fastest and most efficient CDC connectors. It is also the only vendor to enable exactly-and-only-once capture, which puts the least load on a system, especially when you’re supporting multiple destinations including a data warehouse, high performance analytics database, and AI engine or vector database.
  • Deployment options: Of the ETL and ELT vendors, Estuary is currently the only vendor to offer open source, private cloud, and public multi-tenant SaaS.
  • Reliability: Estuary’s exactly-once transactional delivery and durable stream storage makes it very reliable.
  • Ease of use: Estuary is one of the easiest to use tools. Most customers are able to get their first pipelines running in hours and generally improve productivity 4x over time. 
  • Lowest cost: For data at any volume, Estuary is the clear low-cost winner in this evaluation. Rivery is second.
  • Great support: Customers consistently cite great support as one of the reasons for adopting Estuary.

Cons

  • On premises connectors: Estuary has 150+ 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.

Estuary Flow Pricing

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.

How to choose the best option

For the most part, if you are interested in a cloud option, and the connectivity options exist, you may choose to evaluate Estuary.

Modern data pipeline: Estuary has the broadest support for schema evolution and modern DataOps.

Lowest latency: If low latency matters, Estuary will be the best option, especially at scale.

Highest data engineering productivity: Estuary is among the easiest to use, on par with the best ELT vendors. But it also has delivered up to 5x greater productivity than the alternatives.

Connectivity: If you're more concerned about cloud services, Estuary or another modern ELT vendor may be your best option. If you need more on-premises connectivity, you might consider more traditional ETL vendors.

Lowest cost: Estuary is the clear low-cost winner for medium and larger deployments.

Streaming support: Estuary has a modern approach to CDC that is built for reliability and scale, and great Kafka support as well. It's real-time CDC is arguably the best of all the options here. Some ETL vendors like Informatica and Talend also have real-time CDC. ELT-only vendors only support batch CDC.

Ultimately the best approach for evaluating your options is to identify your future and current needs for connectivity, key data integration features, and performance, scalability, reliability, and security needs, and use this information to a good short-term and long-term solution for you.

GETTING STARTED WITH ESTUARY

  • Free account

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  • Docs

    Make sure you read through the documentation, especially the get started section.

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  • Community

    I highly recommend you also join the Slack community. It's the easiest way to get support while you're getting started.

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  • Estuary 101

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