You’re comparing Weld vs Azure Data Factory vs Etlworks Integrator. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | Azure Data Factory | Etlworks Integrator |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Pay per activity run + data movement; ~ $0.25 per DIU-hour for data flows | Credit-based (e.g., ~$0.10 per credit; volume discounts available) |
| Free tier | No | Yes | Yes |
| Location | DK, (EU) | Azure Global (multi-region) | Pittsburgh, PA, USA |
| Connectors & Sync | |||
| Connectors | 200+ | 90+ | 300+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | No |
| 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 | Yes | Yes |
| Lineage | Yes | Yes | No |
| Version control | Yes | Yes | No |
| On-Premise | No | No | Yes |
| OpenAPI / Developer API | Yes | No | Yes |
| Integrations | |||
| Load to/from Excel | Yes | Yes | Yes (via file connectors) |
| Load to/from Google Sheets | Yes | No | Yes (Google Sheets) |
| Ratings | |||
| G2 rating | 4.8 | 4.4 | 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
Azure Data Factory (ADF) is Microsoft’s cloud-based data integration service for building ETL and ELT pipelines. It provides a visual pipeline designer, 90+ built-in connectors for Azure, SaaS, and on-premises sources, and supports transformations through Mapping Data Flows, Azure Databricks, stored procedures, and Azure Functions. ADF includes orchestration, monitoring, Git integration, and hybrid connectivity via a self-hosted integration runtime.
90+ built-in connectors including Azure SQL, Cosmos DB, Oracle, SAP, Salesforce, and custom REST endpoints.
Visual pipeline orchestration with debugging, parameterization, and Git integration for CI/CD workflows.
Hybrid integration support through Self-Hosted Integration Runtime for on-premises and private network systems.
Tight integration with Azure Databricks, Azure Synapse, Azure Functions, and ML services for flexible compute and transformations.
Complex pricing model—billed per activity run, DIU-hours for data flows, and cross-region data movement.
UI performance can slow when working with large pipelines; error messages are often generic.
Mapping Data Flows run on Spark, which increases the learning curve for advanced transformations.
Gartner Peer Review:
“Its flexibility in connecting diverse data sources and integration with the Azure ecosystem are standout advantages.”
“Some features are too rigid. Lack of detailed error messages can plague a workstream during setup.”
Overview
Etlworks Integrator is a cloud-based ETL and data integration platform offering more than 300 connectors across databases, SaaS applications, files, and big-data systems. It supports batch and streaming pipelines, change data capture (CDC), SQL- or JavaScript-based transformations, and workflow orchestration. The platform can run fully in the cloud or use on-premise runtimes to securely access internal systems.
300+ connectors covering databases, cloud storage, SaaS apps, and streaming platforms.
Supports both batch and streaming (CDC) pipelines with customizable schedules and triggers.
Transformation using SQL, JavaScript, or built-in functions, including validation and error handling.
Cloud-based platform with optional on-premise agents for secure internal connectivity.
UI complexity: large flows with many steps can be difficult to navigate.
Credit-based subscription model can make cost forecasting difficult for variable workloads.
Smaller community and lower brand recognition compared to market-leading ETL tools.
Etlworks Integrator Features:
“Etlworks Integrator’s breadth of connectors and flexible transformation engine (SQL/JavaScript) let us integrate data from dozens of sources quickly.”
“The UI can be overwhelming for beginners, and pricing (credit-based) can be hard to predict for varying workloads.”
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.
ADF provides a drag-and-drop pipeline builder that is approachable for basic data movement. Advanced Mapping Data Flows rely on Spark behind the scenes, requiring additional learning. Git integration (Azure DevOps or GitHub) supports collaboration and versioning.
Etlworks Integrator’s Flow Designer provides a canvas for building pipelines with sources, transformations, and destinations. While flexible, complex flows can become visually dense, and new users typically face a learning curve.
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.
ADF provides a drag-and-drop pipeline builder that is approachable for basic data movement. Advanced Mapping Data Flows rely on Spark behind the scenes, requiring additional learning. Git integration (Azure DevOps or GitHub) supports collaboration and versioning.
Etlworks Integrator’s Flow Designer provides a canvas for building pipelines with sources, transformations, and destinations. While flexible, complex flows can become visually dense, and new users typically face a learning curve.
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.
ADF uses pay-as-you-go pricing based on activity runs, data flow compute (DIUs), and data movement. Costs can vary significantly depending on volume and schedule frequency, making upfront cost estimation more complex.
Pricing follows a credit-based model tied to data volume and processing. While flexible, predicting usage costs requires monitoring workload patterns. A free trial is available with limited credits.
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.
ADF uses pay-as-you-go pricing based on activity runs, data flow compute (DIUs), and data movement. Costs can vary significantly depending on volume and schedule frequency, making upfront cost estimation more complex.
Pricing follows a credit-based model tied to data volume and processing. While flexible, predicting usage costs requires monitoring workload patterns. A free trial is available with limited credits.
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.
ADF includes pipeline orchestration, visual mapping data flows, hybrid connectivity, triggers (schedule, event, tumbling window), monitoring via Azure Monitor, SSIS lift-and-shift, and integration with Synapse, Databricks, and Functions.
Supports 300+ connectors, CDC replication, batch and streaming ETL, SQL/JavaScript transformations, error handling, task scheduling, webhook triggers, and secure on-premise gateways. Offers REST API automation and flow export/import.
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.
ADF includes pipeline orchestration, visual mapping data flows, hybrid connectivity, triggers (schedule, event, tumbling window), monitoring via Azure Monitor, SSIS lift-and-shift, and integration with Synapse, Databricks, and Functions.
Supports 300+ connectors, CDC replication, batch and streaming ETL, SQL/JavaScript transformations, error handling, task scheduling, webhook triggers, and secure on-premise gateways. Offers REST API automation and flow export/import.
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.
ADF pipelines can call custom .NET activities, Databricks notebooks, stored procedures, Azure ML endpoints, and Azure Functions. It supports parameterized templates, branching, and custom logic, though many advanced scenarios rely on complementary Azure services.
Users can embed custom JavaScript, call external services, configure on-premise runtimes, and parameterize flows. Pipelines can be exported for version control, and the open API enables integration into external orchestration or automation frameworks.
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.
ADF pipelines can call custom .NET activities, Databricks notebooks, stored procedures, Azure ML endpoints, and Azure Functions. It supports parameterized templates, branching, and custom logic, though many advanced scenarios rely on complementary Azure services.
Users can embed custom JavaScript, call external services, configure on-premise runtimes, and parameterize flows. Pipelines can be exported for version control, and the open API enables integration into external orchestration or automation frameworks.
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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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