Comparing Estuary with Google Cloud Dataflow and Weld

You’re comparing Estuary vs Google Cloud Dataflow vs Weld. Explore how they differ on connectors, pricing, and features.

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Weld vs Estuary vs Google Cloud Dataflow

	Weld	Estuary	Google Cloud Dataflow
Connectors	200+	200+	30+
Price	$99 / 5M Active Rows	$0.50/GB consumed + per-connector fee	Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs
Free tier
Location	EU	New York, NY, USA	GCP Global (multi-region)
Extract data (ETL)
Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL)
Transformations
AI Assistant
On-Premise
Orchestration
Lineage
Version control
Load to/from Excel			Via CSV in Cloud Storage
Load to/from Google Sheets
Two-Way Sync
dbt Core Integration
dbt Cloud Integration
OpenAPI / Developer API
G2 rating	4.8	4.8	4.5

Overview

Estuary in Short

Estuary Flow is a real-time ETL/ELT and data integration platform for both batch and streaming pipelines. It provides sub-100ms latency using Change Data Capture (CDC), supports automated schema evolution, and allows users to build entire pipelines with low- or no-code connectors in minutes. It can target data warehouses (e.g., Snowflake, BigQuery), BI tools, and operational systems for analytics, operations, and AI use cases.

Pros

Purpose-built for real-time CDC and streaming ETL with sub-100ms latency.
Automatic schema evolution with exactly-once delivery guarantees.
200+ no-code connectors for databases, SaaS apps, and message queues.
Flexible deployment: public cloud, private cloud, or self-hosted (BYOC).

Cons

Premium pricing model ($0.50/GB consumed + connector fees) can be expensive for small teams.
Still growing connector catalog; niche or very new APIs may require custom work.
Smaller community compared to older open-source tools, meaning fewer community-built resources.

Reviews & Quotes

Estuary Pricing Page:

What I like about Estuary

“Estuary’s real-time, no-code model is magical—getting data instantly with minimal effort and near-zero pipeline maintenance. Plus, their support is fantastic.”

What I dislike about Estuary

“Pricing can be high for lower-volume teams, and some less-common connectors are still in development, which limits immediate use cases for niche sources.”

Read full review

Overview

Google Cloud Dataflow in Short

Google Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam. It enables users to write ETL pipelines in Java or Python, which Dataflow executes on Google’s serverless infrastructure with autoscaling. It integrates natively with Pub/Sub, BigQuery, Cloud Storage, and other GCP services for end-to-end data processing.

Pros

Unified batch + streaming model via Apache Beam SDK (Java/Python).
Serverless autoscaling with dynamic work rebalancing for cost and performance optimization.
First-class integration with GCP services: Pub/Sub, BigQuery I/O connectors, Cloud Storage, Spanner, etc.
Built-in exactly-once processing semantics and windowing capabilities for streaming ETL.

Cons

Steep learning curve if unfamiliar with Apache Beam’s abstractions (PCollections, DoFns, pipelines).
Monitoring and debugging streaming pipelines can be complex—metrics and logs often require cross-referencing.
Cost can rise quickly for large-scale streaming (billed per vCPU-second and memory). Efficient pipeline tuning is critical.

Reviews & Quotes

G2 Reviews:

What I like about Google Cloud Dataflow

“Google cloud dataflow is automatically optimize and manages resources for you this platform supports multiple programming languages including Python, java and SQL and makes it easy for developers to focus on writing codes”

What I dislike about Google Cloud Dataflow

“It is costly as compared to other solutions”

Read full review

Overview

Weld in Short

Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.

Pros

Lineage, orchestration, and workflow features
Ability to handle large datasets and near real-time data sync
ETL + reverse ETL in one
User-friendly and easy to set up
Flat monthly pricing model
200+ connectors (Shopify, HubSpot, etc.)
AI assistant

Cons

Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses

Reviews & Quotes

A reviewer on G2 said:

What I like about Weld

“First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.”

What I dislike about Weld

“Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.”

Read full review

Feature-by-Feature Comparison

Estuary

Google Cloud Dataflow

Weld

Feature

Estuary

Google Cloud Dataflow

Weld

Ease of Use & Interface

Side-by-side

Estuary

Estuary’s UI is intuitive: users can add connectors, configure CDC streams, and specify destinations in a few clicks. Complex transformations can be written in SQL or TypeScript directly in the Flow editor, but most tasks are handled via no-code connectors.

Google Cloud Dataflow

Dataflow pipelines are defined programmatically in Java or Python (Apache Beam). There is no drag-and-drop UI; developers use the Cloud Console or CLI to monitor, but pipeline creation and debugging happen in code and SDKs.

Weld

Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.

Ease of Use & Interface

Side-by-side

Pricing & Affordability

Side-by-side

Estuary

While Estuary provides a 10 GB/month free tier and a 30-day trial, its consumption-based pricing ($0.50/GB + connector fees) can become costly at scale. Teams processing hundreds of GBs per month should budget accordingly.

Google Cloud Dataflow

Charges for each pipeline based on vCPU-second, memory, and persistent disk usage. Streaming jobs are billed continuously. Without careful optimization (autoscaling, batching), costs can escalate. However, for high-throughput workloads, serverless autoscaling can be cost-effective versus self-managed clusters.

Weld

Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.

Pricing & Affordability

Side-by-side

Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.

Feature Set

Side-by-side

Estuary

Key features include real-time CDC (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via SQL/TypeScript or dbt. It also supports Kafka-compatibility and private storage for data replay.

Google Cloud Dataflow

Features include: Batch & streaming unified model, windowing & triggers, exactly-once semantics, dynamic work rebalancing, and data-driven autoscaling. Supports FlexRS (spot pricing for batch) and integration with Dataflow SQL for SQL-based pipelines.

Weld

Weld integrates ELT, data transformations, and reverse ETL all within one platform. It also provides advanced features such as data lineage, orchestration, workflow management, and an AI assistant, which helps in automating repetitive tasks and optimizing workflows.

Feature Set

Side-by-side

Flexibility & Customization

Side-by-side

Estuary

Estuary allows custom TypeScript transforms in-stream or SQL in-destination. Pipelines can be managed via CLI (flowctl) and integrated into CI/CD. While most connectors are no-code, custom connectors can be built using the open-source Flow SDK.

Google Cloud Dataflow

Users write custom transforms (ParDo, Map, GroupBy), can integrate UDFs, and use side inputs. Complex workloads requiring custom logic (stateful processing, custom connectors) are fully supported via Beam SDK. Cloud features like VPC, IAM, and KMS integrate security.

Weld

Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.