Estuary

Estuary VS Talend

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

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Comparison between Estuary and Talend
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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 Estuary vs Talend 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: Estuary vs Talend

Estuary logo
Estuary
Talend logo
Talend
Database replication (CDC)EstuaryMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batchTalendDB2 (i Series), MariaDB, MySQL, Oracle, Postgres, Progress, SQL Server, Sybase, (Custom)
Operational integrationEstuary

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

Talend

Limited real-time scale

Data migrationEstuary

Intelligent schema inference and evolution support.

Support for most relational databases.

Continuous replication reliability.

Talend
Stream processingEstuary

Real-time ETL in Typescript and SQL

Talend
Operational analyticsEstuary

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

Talend
AI pipelinesEstuary

Pinecone support for real-time data vectorization.

Transformations can call ChatGPT & other AI APIs.

Talend

OpenAI component

Apache Iceberg SupportEstuary

Native Iceberg support, both streaming and batch, supports REST catalog, versioned schema evolution, and exactly-once guarantees.

Talend

Batch + CDC, Iceberg support via Spark connectors, complex setup; not native or turnkey.

Number of connectorsEstuary200+ high performance connectors built by EstuaryTalend50+ managed connectors; 1000 API-based connections
Streaming connectorsEstuaryCDC, Kafka, Kinesis, Pub/SubTalendCDC, Kafka, Kinesis, Azure Storage Queue, PubSub, RabbitMQ, AMQP, JMS, MQTT
3rd party connectorsEstuary

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

Talend
Custom SDKEstuary

SDK for source and destination connector development.

Talend

Talend Component Kit

Request a connectorEstuary

Connector requests encouraged. Swift response.

Talend
Batch and streamingEstuaryBatch and streamingTalendStreaming and batch support
Delivery guaranteeEstuaryExactly once (streaming, batch, mixed)TalendExactly once
ELT transformsEstuary

dbt Cloud integration

Talend

Dbt only

ETL transformsEstuary

Real-time, SQL and Typescript

Talend

tMaps transformations. SQL function. Works with dbt

Load write methodEstuaryAppend only or update in place (soft or hard deletes)TalendSoft and hard deletes, append and update in place (with work)
DataOps supportEstuary

API and CLI support for operations.

Declarative definitions for version control and CI/CD pipelines.

Talend

CLI, API

Schema inference and driftEstuary

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

Talend

Not without coding, but can be done.

Store and replayEstuary

Can backfill multiple targets and times without requiring new extract.

User-supplied cheap, scalable object storage.

Talend
Time travelEstuary

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

Talend
SnapshotsEstuary

Full or incremental

Talend

N/A

Ease of useEstuary

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

Talend

Can have a steep learning curve

Deployment optionsEstuaryOpen source, public cloud, private cloudTalendOn premises (self-hosted), private cloud, public cloud
SupportEstuary

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

Slack community.

Talend

Depends on pricing tier

Performance (minimum latency)Estuary< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline.TalendSub-second loading at low volumes. Requires bulk mode to scale.
ReliabilityEstuaryHighTalendHigh
ScalabilityEstuaryHigh 5-10x scalability of others in productionTalendHigh but requires bulk-mode loading
SOC2Estuary

SOC 2 Type II with no exceptions

Talend
Data source authenticationEstuaryOAuth 2.0 / API Tokens SSH/SSLTalendOAuth / HTTPS / SSH / SSL / API Tokens
EncryptionEstuaryEncryption at rest, in-motionTalendEncryption at rest, in-motion
HIPAA complianceEstuary

HIPAA compliant with no exceptions

Talend

HIPAA BAA compliant

Vendor costsEstuary

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.

Talend

Opaque pricing that can be based on data volume, job executions, and duration, depending on pricing tier

Data engineering costsEstuary

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

Talend

Steep learning curve and requires work to implement features like upserts

Admin costsEstuary

“It just works”

Talend

Start streaming your data for free

Build a Pipeline

Estuary

Estuary introductory image

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.

Pros

  • 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.

Cons

  • 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.

Estuary 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.

Talend

Talend introductory image

Talend, now part of Qlik, has two main products—Talend Data Fabric and Stitch, which is ELT. Talend Data Fabric is a data integration platform that, like Informatica, is broader than ETL. It also includes data quality and data governance capabilities.

Talend also had an open-source solution, Talend Open Studio, that could help you kickstart your first data integration and ETL projects. It was discontinued by Qlik in 2024.

You could use Talend Open Studio for data processes that require lightweight workflows. The majority of enterprise data pipelines would find Data Fabric more suitable.

Pros

  • ETL platform: Data Fabric has rich transformation, data mapping, and data quality features that help with building data pipelines.
  • Real-time and batch: Real-time support includes streaming CDC.
  • Strong monitoring and analytics: Like Informatica, Talend has built up good visibility for operations.

Cons

  • Learning curve: Talend has an older UI that takes time to learn. Building transforms can take time.
  • Limited Open Studio features: While Open Studio is free, it’s also limited. Other open source options are less limited in their capabilities.
  • Limited connectors: Talend claims 1000+ connectors. But it lists 50 or so databases, file systems, applications, messaging, and other systems it supports. The rest are Talend Cloud Connectors, which you create as reusable objects.
  • High costs: Talend isn't transparent about pricing and doesn't list their current rates. Different tiers may relate to data volume, job executions, and duration. Ultimately, it costs more than most pay-as-you-go tools, as well as Stitch.

Talend Pricing

Pricing quotes are only available upon request. Potential clients should study the pricing tiers carefully, as lower tiers may not include common or desired functionality, like CDC capabilities.

Talend will likely be a higher cost option than many other ELT vendors, especially low-cost platforms like Estuary and Rivery.

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

    Getting started with Estuary is simple. Sign up for a free account.

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  • Docs

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

    Learn more
  • Community

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

    Join Slack Community
  • Estuary 101

    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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