You’re comparing Weld vs dlt (Data Load Tool) vs Rows. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Feature | Weld | dlt (Data Load Tool) | Rows |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Free (open-source) | Free; Pro from $39/user/month |
| Free tier | No | No | Yes |
| Location | DK, (EU) | DE | US |
| Connectors & Sync | |||
| Connectors | 200+ | 60+ | — |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | Yes | No |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | Yes | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | No | Yes |
| Lineage | Yes | No | No |
| Version control | Yes | Yes | No |
| On-Premise | No | Yes | No |
| OpenAPI / Developer API | Yes | Yes | No |
| Integrations | |||
| Load to/from Excel | Yes | No | Yes |
| Load to/from Google Sheets | Yes | No | Yes |
| Ratings | |||
| G2 rating | 4.8 | — | — |
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
dlt (data load tool) is an open-source Python library for building ELT pipelines using a code-first approach. Pipelines are defined in Python and can be scheduled through tools such as Airflow, Dagster, Prefect, or basic cron jobs. dlt includes pre-built connectors, automatic schema evolution, incremental loading, normalization, retry logic, and supports popular destinations such as BigQuery, Snowflake, Redshift, Databricks, and DuckDB. It is designed for engineering teams that want flexibility without the overhead of managing a full ETL platform.
Open-source and free to use
Flexible, Python-based pipeline development
Automatic schema inference and incremental loading
60+ pre-built connectors with SDK for custom sources
Works with any orchestration tool (Airflow, Prefect, Dagster, cron)
No graphical UI; requires Python skills
No fully managed SaaS version
Limited transformation features without dbt or Python logic
Monitoring/observability must be set up separately
Smaller ecosystem compared to more mature platforms
A reviewer on Medium:
“dlt is lightweight, customizable, and removes a lot of the boilerplate around API ingestion. With just a few lines of Python, we were able to create robust pipelines that handle schema changes and incremental loads seamlessly.”
“High volume, low latency, hard-to-build stuff is complicated.”
Overview
[Rows.com](http://rows.com/) is a next-generation spreadsheet platform that integrates natively with hundreds of SaaS APIs, databases, and analytics tools. It enables automated ELT directly into spreadsheets, provides built-in reporting and dashboards, and supports scripting for more advanced workflows.
Native connectors to 500+ SaaS APIs and data sources.
Automated ELT directly into the spreadsheet.
Built-in charting, dashboards, and collaboration features.
Supports JavaScript and Python formulas for flexible automation.
Free plan available with core spreadsheet and integration features.
Large or API-heavy sheets can lag with high-volume data.
Advanced connectors and automation features require paid plans.
Not built for warehouse-scale datasets; best suited for mid-size tables.
[Rows.com](http://rows.com/) review on G2:
“Creating tables from Excel or Google Sheets imports is incredible. Creating charts and visuals in just a few clicks and navigating your workspace is PERFECT.”
“The basic dashboard is not very clear. Need to understand the features and divide screens to jump between spaces.”
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.
dlt is code-first and does not offer a graphical UI. It is easy to work with for Python developers but inaccessible for non-technical users.
[Rows.com](http://rows.com/) offers a modern spreadsheet UI with built-in API connectors. Non-technical users can rely on pre-built integrations, while technical users can create custom logic using JavaScript or Python formulas.
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.
dlt is code-first and does not offer a graphical UI. It is easy to work with for Python developers but inaccessible for non-technical users.
[Rows.com](http://rows.com/) offers a modern spreadsheet UI with built-in API connectors. Non-technical users can rely on pre-built integrations, while technical users can create custom logic using JavaScript or Python formulas.
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.
dlt is fully open-source with no licensing costs. Users only pay for the infrastructure on which they run their pipelines, making it cost-effective for engineering teams.
A generous free tier supports most basic import and spreadsheet needs. Pro plans start at $39/user/month for increased API quotas, automations, and advanced integrations. Enterprise plans are available on request.
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.
dlt is fully open-source with no licensing costs. Users only pay for the infrastructure on which they run their pipelines, making it cost-effective for engineering teams.
A generous free tier supports most basic import and spreadsheet needs. Pro plans start at $39/user/month for increased API quotas, automations, and advanced integrations. Enterprise plans are available on request.
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.
dlt includes pre-built connectors, automatic schema evolution, incremental loading, normalization, and built-in state management. It integrates with major destinations and supports Python-based transformations or dbt.
[Rows.com](http://rows.com/) includes ELT connectors, spreadsheet-based transformations, automation and scheduling, built-in charting and dashboards, collaboration, API-triggered workflows, and integrations with Slack, email, and BI tools.
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.
dlt includes pre-built connectors, automatic schema evolution, incremental loading, normalization, and built-in state management. It integrates with major destinations and supports Python-based transformations or dbt.
[Rows.com](http://rows.com/) includes ELT connectors, spreadsheet-based transformations, automation and scheduling, built-in charting and dashboards, collaboration, API-triggered workflows, and integrations with Slack, email, and BI 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.
Because pipelines are written fully in Python, dlt offers high flexibility and can integrate with any orchestration or monitoring stack. This flexibility requires engineering effort but enables highly customized workflows.
Users can script custom workflows with JavaScript or Python, chain multiple API calls, and set up scheduled or event-based automations. Highly flexible for teams that prefer spreadsheet-centric data operations.
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.
Because pipelines are written fully in Python, dlt offers high flexibility and can integrate with any orchestration or monitoring stack. This flexibility requires engineering effort but enables highly customized workflows.
Users can script custom workflows with JavaScript or Python, chain multiple API calls, and set up scheduled or event-based automations. Highly flexible for teams that prefer spreadsheet-centric data operations.
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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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