You’re comparing Weld vs Census vs Google Cloud Dataflow. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Feature | Weld | Census | Google Cloud Dataflow |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Free tier available; Pro plans start around $350/month | Billed per vCPU-second, memory, and storage; ~$0.0106 per vCPU-minute, with additional streaming costs |
| Free tier | No | Yes | No |
| Location | DK, (EU) | US | GCP Global (multi-region) |
| Connectors & Sync | |||
| Connectors | 200+ | 130+ | 30+ |
| Extract data (ETL) | Yes | No | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | Yes | No |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | No | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | Yes | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | No |
| Lineage | Yes | No | No |
| Version control | Yes | Yes | No |
| On-Premise | No | No | No |
| OpenAPI / Developer API | Yes | Yes | No |
| Integrations | |||
| Load to/from Excel | Yes | No | Yes (via CSVs in Cloud Storage) |
| Load to/from Google Sheets | Yes | Yes | No |
| Ratings | |||
| G2 rating | 4.8 | 4.5 | 4.5 |
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
Census is a reverse ETL platform that syncs data from cloud data warehouses into operational tools such as Salesforce, HubSpot, Marketo, Google Sheets, and Slack. It works directly on warehouse tables or dbt models and focuses on reliable, incremental syncing to support operational analytics and data activation use cases.
Warehouse-native: syncs directly from tables or dbt models
No-code field mapping with live previews
Broad destination support across CRM, marketing, and productivity tools
Incremental upserts help maintain clean, deduplicated records
Deep dbt integration and support for analytics-as-code workflows
Flexible scheduling with cron, triggers, and API-based execution
Reverse ETL only—requires a separate tool for ingestion and transformation
Usage-based pricing can become costly at high volumes
Complex logic must be handled upstream in the warehouse or dbt
Sync performance depends on destination API limits
Cloud-only platform without on-prem deployment options
Census Overview (G2):
“Census is the fastest and most reliable reverse ETL platform with strong uptime and responsive support. It delivers transformed data directly from the warehouse into operational tools without requiring complex pipelines.”
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.”
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.
Census offers a clean interface with intuitive field mapping, previews, and validation steps. Non-technical users can configure operational syncs with minimal setup.
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.
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.
Census offers a clean interface with intuitive field mapping, previews, and validation steps. Non-technical users can configure operational syncs with minimal setup.
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.
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.
Census includes a free tier for basic use, while paid plans scale with usage. Costs can increase with high sync volumes, making it more suitable for teams with predictable operational data needs.
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.
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.
Census includes a free tier for basic use, while paid plans scale with usage. Costs can increase with high sync volumes, making it more suitable for teams with predictable operational data needs.
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.
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.
Census focuses on reverse ETL functionality, including incremental syncs, no-code mapping, dbt model syncing, scheduling, monitoring, and alerting. It provides an API and CLI for integration into GitOps workflows.
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.
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.
Census focuses on reverse ETL functionality, including incremental syncs, no-code mapping, dbt model syncing, scheduling, monitoring, and alerting. It provides an API and CLI for integration into GitOps workflows.
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.
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.
Users can sync any warehouse table or SQL model, schedule syncs flexibly, and configure failure handling. Customization is primarily driven by upstream SQL and dbt transformations, aligning with analytics-engineering workflows.
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.
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.
Users can sync any warehouse table or SQL model, schedule syncs flexibly, and configure failure handling. Customization is primarily driven by upstream SQL and dbt transformations, aligning with analytics-engineering workflows.
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.
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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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