You’re comparing Weld vs Google Cloud Dataflow vs Rivery. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Feature | Weld | Google Cloud Dataflow | Rivery |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Billed per vCPU-second, memory, and storage; ~$0.0106 per vCPU-minute, with additional streaming costs | $0.75 per credit (100MB replicated = 1 credit) |
| Free tier | No | No | No |
| Location | DK, (EU) | GCP Global (multi-region) | US |
| Connectors & Sync | |||
| Connectors | 200+ | 30+ | 200+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | Yes |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | No | Yes |
| Lineage | Yes | No | No |
| Version control | Yes | No | No |
| On-Premise | No | No | No |
| OpenAPI / Developer API | Yes | No | Yes |
| Integrations | |||
| Load to/from Excel | Yes | Yes (via CSVs in Cloud Storage) | No |
| Load to/from Google Sheets | Yes | No | Yes |
| Ratings | |||
| G2 rating | 4.8 | 4.5 | 4.7 |
Overview
Weld is a unified ELT and data activation platform that combines ingestion, modeling, transformations, orchestration, lineage, and reverse ETL in a single SaaS interface. With premium in-house–built connectors, an intuitive UI, and near real-time syncs, Weld enables both technical and non-technical users to create and manage data workflows efficiently. Weld also includes an AI assistant to support SQL modeling, generate transformations, and streamline repetitive tasks. Teams can ingest data from a wide range of sources—including marketing platforms, CRMs, databases, Google Sheets, Excel, and APIs—into their cloud data warehouse and activate it back into business tools.
Lineage, orchestration, and workflow features included by default
Handles large datasets and near real-time data sync
ELT and reverse ETL in one platform
User-friendly interface with minimal setup required
Flat, predictable monthly pricing model
200+ in-house–built, high-quality connectors
AI assistant for modeling and transformations
Some SQL knowledge is useful for advanced modeling
Optimized for cloud-warehouse workflows (Snowflake, BigQuery, Redshift, etc.)
Feature set is streamlined for modern ELT/activation use cases
A reviewer on G2 said:
Overview
Google Cloud Dataflow is a fully managed batch and stream data processing service built on Apache Beam. It enables developers to write pipelines in Python or Java using Beam’s unified programming model, which Dataflow executes on serverless, autoscaling infrastructure. It integrates natively with GCP services including Pub/Sub, BigQuery, and Cloud Storage, supporting large-scale ETL workloads with dynamic scaling and built-in streaming features.
Unified batch and streaming data processing model via Apache Beam SDK.
Serverless execution with autoscaling and dynamic work rebalancing.
Native integration with Pub/Sub, BigQuery, Cloud Storage, Spanner, and more.
Supports exactly-once processing, windowing, triggers, and stateful operations for streaming workloads.
Steep learning curve due to Apache Beam concepts (PCollections, DoFns, pipelines).
Debugging and monitoring streaming jobs can be complex and requires multiple console tools.
Costs can rise quickly for high-throughput streaming workloads without careful optimization.
G2 Reviews:
“Google Cloud Dataflow automatically optimizes and manages resources. It supports multiple programming languages including Python and Java, making it easy for developers to focus on writing code.”
“It can be costly compared to other solutions, especially for long-running streaming pipelines.”
Overview
Rivery is a cloud-based ELT and data orchestration platform designed to help teams build data pipelines with minimal engineering effort. It provides pre-built connectors, support for custom API extraction, and post-load transformations through "Logic Rivers," which allow SQL- and Python-based transformation workflows. While Rivery focuses on automation and usability, the platform relies on a credit-based pricing model and lacks some advanced observability features found in more enterprise-focused tools.
User-friendly, no-code/low-code interface
Supports custom API integrations through a native GUI
Reverse ETL capabilities
Python and SQL-based transformations via Logic Rivers
Responsive customer support
Pricing can be difficult to predict due to credit-based model
Limited real-time / on-the-fly transformation options (no ETL)
Documentation quality is inconsistent
Interface can become cumbersome for large, complex pipelines
Error handling and monitoring features are less advanced compared to enterprise tools
As a user on G2 puts it::
“As a data analyst, I find the tool really easy to use; it's intuitive how you connect to the different data sources and create your data pipelines.”
“For first-time users, it would be good to have some demo buttons; still, if you are familiar with terms, you'll manage to navigate between windows.”
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
Dataflow pipelines are authored programmatically in Java or Python through Apache Beam. There is no drag-and-drop UI, developers write, test, and debug pipelines in code and monitor them via Cloud Console. This provides flexibility but requires engineering skill.
Rivery is generally easy to use and designed for fast pipeline building, though working with larger workflows can feel cluttered.
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
Dataflow pipelines are authored programmatically in Java or Python through Apache Beam. There is no drag-and-drop UI, developers write, test, and debug pipelines in code and monitor them via Cloud Console. This provides flexibility but requires engineering skill.
Rivery is generally easy to use and designed for fast pipeline building, though working with larger workflows can feel cluttered.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
Dataflow uses per-vCPU-second and memory pricing. Streaming pipelines incur continuous charges. Autoscaling and FlexRS discount options help reduce cost, but inefficient pipelines can lead to high spend, particularly for real-time workloads.
Rivery uses a credit-based pricing model that can become expensive as data volumes grow, and costs may be difficult to estimate in advance.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
Dataflow uses per-vCPU-second and memory pricing. Streaming pipelines incur continuous charges. Autoscaling and FlexRS discount options help reduce cost, but inefficient pipelines can lead to high spend, particularly for real-time workloads.
Rivery uses a credit-based pricing model that can become expensive as data volumes grow, and costs may be difficult to estimate in advance.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Key features include the unified batch and streaming model, windowing, triggers, exactly-once semantics, autoscaling, dynamic work rebalancing, FlexRS for discounted batch processing, and Dataflow SQL for SQL-based pipeline authoring. Integrates closely with Pub/Sub and BigQuery.
Rivery offers ELT, Reverse ETL, custom API extraction, and SQL/Python transformations. However, observability, lineage, and advanced governance features are limited.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Key features include the unified batch and streaming model, windowing, triggers, exactly-once semantics, autoscaling, dynamic work rebalancing, FlexRS for discounted batch processing, and Dataflow SQL for SQL-based pipeline authoring. Integrates closely with Pub/Sub and BigQuery.
Rivery offers ELT, Reverse ETL, custom API extraction, and SQL/Python transformations. However, observability, lineage, and advanced governance features are limited.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Custom transformations, UDFs, and stateful processing are supported through Apache Beam. Pipelines can integrate with VPC, IAM, and KMS for security. Advanced workloads requiring custom logic or connectors are fully supported through Beam’s programming APIs.
The platform offers flexibility through its Logic Rivers and custom API connectors, but lacks deeper customization options and advanced development workflows found in more engineering-focused tools.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Custom transformations, UDFs, and stateful processing are supported through Apache Beam. Pipelines can integrate with VPC, IAM, and KMS for security. Advanced workloads requiring custom logic or connectors are fully supported through Beam’s programming APIs.
The platform offers flexibility through its Logic Rivers and custom API connectors, but lacks deeper customization options and advanced development workflows found in more engineering-focused tools.
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CUSTOMER STORIES
“One of the biggest impacts has been unlocking new ways to buy media. Before, we didn’t have the data to back up strategic decisions – now we do.”
“Before Weld, we had to rely on custom Python scripts and manual processes that were time-consuming and error-prone.”
“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”
“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”
“Before Weld, our data infrastructure was limited and we relied heavily on Excel files and custom Python scripts.”
“Weld has saved us a ton of time, from not having data ready to having a fully functional data warehouse and connectors.”
“By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance”
“We didn’t have a good data setup, so we lacked the business insights we needed. Weld has allowed us to set up a structured data infrastructure and access insights quickly.”
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