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Comparing Mozart Data with Rows.com and Weld

You’re comparing Mozart Data vs Rows.com vs Weld. Explore how they differ on connectors, pricing, and features. Ed Logo

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Weld vs Mozart Data vs Rows.com

WeldMozart DataRows.com
Connectors200+150+
Price$99 / 5M Active RowsStarts around $1,000/mo (includes Snowflake + ETL up to 250k MAR)Free / starts at $39/user/mo for Pro
Free tier
LocationEUSan Francisco, CA, USAUS
Extract data (ETL)
Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL)
Transformations
AI Assistant
On-Premise
Orchestration
Lineage
Version control
Load to/from ExcelVia CSV/Flat File uploads
Load to/from Google Sheets
Two-Way Sync
dbt Core Integration
dbt Cloud Integration
OpenAPI / Developer API
G2 rating4.84.6

Overview

Mozart Data in Short

Mozart Data is a managed data stack provider that bundles ETL (using embedded Fivetran/Portable connectors), a fully managed Snowflake warehouse, and dbt-based transformations under one subscription—aiming to get teams from zero to insights in under an hour without coding.

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Pros

  • Out-of-the-box Snowflake data warehouse with connectors and dbt transforms in one package.

  • 150+ connectors (via embedded Fivetran + Portable) configured behind the scenes so you don’t manage separate tools.

  • Very fast onboarding—your data stack is live in under an hour without any code.

  • Dedicated customer support and onboarding assistance (Mozart Assist) helps users set up and maintain pipelines.

Cons

  • Pricing includes both warehouse usage and data volume (Monthly Active Rows), so costs rise with scale—often more expensive than self-managed ELT at high volumes.

  • Less flexibility for bespoke connector logic—if a connector is missing, you must submit a request and wait for their team.

  • Smaller community and fewer third-party tutorials compared to standalone tools like Airbyte or dbt.

Reviews & Quotes

Mozart Data Reviews (G2):

What I like about Mozart Data

Mozart Data gave us a turnkey stack with Snowflake, connectors, and transformations all configured. We were running dashboards in under a week without DevOps overhead.

What I dislike about Mozart Data

Costs can escalate quickly with high data volumes, and adding niche connectors often requires a request to their team (no self-serve).

Overview

Rows.com in Short

Rows.com is a next-generation spreadsheet platform that natively integrates with hundreds of SaaS APIs, databases, and BI tools. It automates data imports (ELT) directly into your spreadsheet, lets you build dashboards and reports, and supports scripting and automation—all inside a familiar spreadsheet interface.

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Pros

  • Native connectors to 500+ SaaS tools (partnerships with major APIs)

  • Automates ELT directly into the spreadsheet—no separate pipeline to maintain

  • Built-in charting, collaboration, and dashboard capabilities

  • Supports JavaScript and Python formulas for advanced automation

  • Free plan available with core features

Cons

  • Heavy API-dependent sheets can lag when importing large datasets

  • Some enterprise connectors and advanced automations require paid plans

  • Not designed for full data-warehouse scale—best for mid-size data tables

Reviews & Quotes

Rows.com review on G2:

What I like about Rows.com

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.

What I dislike about Rows.com

The basic dashboard is not very clear. Need to understand the features and divide screens to jump between spaces.

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.

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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.

Feature-by-Feature Comparison

Feature
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Mozart Data

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Rows.com

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Weld

Ease of Use & Interface

Side-by-side

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Mozart Data

Mozart Data abstracts away infrastructure: users pick sources via a web UI, configure destinations, and their warehouse and pipelines spin up automatically. Minimal learning curve for non-technical teams.

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Rows.com

Rows.com feels like a modern spreadsheet but with built-in API connectors. Non-technical users can browse pre-built integrations, while power users can script custom workflows in JavaScript or Python.

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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.

Pricing & Affordability

Side-by-side

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Mozart Data

Mozart’s bundled pricing (data volume + warehouse compute) starts at ~$1,000/month for small usage, which can be competitive for teams that value time saved over cost. However, high-volume users may find it pricier than DIY stacks.

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Rows.com

A free tier covers most basic import needs. Paid plans start at $39/user/month for Pro, which unlocks advanced integrations, higher API quotas, and dedicated support. Enterprise pricing is custom.

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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.

Feature Set

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Mozart Data

Includes managed Snowflake, automated ETL connectors (via Fivetran + Portable), a dbt transformation layer, and monitoring dashboards. Supports scheduling, incremental loads, and basic orchestrations without separate tools.

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Rows.com

Rows.com offers ELT connectors, in-sheet data transformations (via formulas or scripts), scheduling/automation, native charting and dashboards, real-time collaboration, and Jira/Slack notifications. It bridges spreadsheet flexibility with ETL and BI tooling.

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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.

Flexibility & Customization

Side-by-side

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Mozart Data

While Mozart Data handles most common use cases seamlessly, it limits custom code in pipelines. Advanced users can still bring their own SQL or dbt models, but building new connectors requires raising a request—no self-serve SDK.

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Rows.com

Users can write custom JavaScript/Python functions directly in cells, chain API calls across sheets, and build automated workflows (e.g., “every morning, pull this data and send a Slack alert”). It’s highly customizable for spreadsheet-centric data work.

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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.

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