Comparing AWS Glue with Mozart Data and Weld

You’re comparing AWS Glue vs Mozart Data vs Weld. Explore how they differ on connectors, pricing, and features.

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Weld vs AWS Glue vs Mozart Data

	Weld	AWS Glue	Mozart Data
Connectors	200+	50+	150+
Price	$99 / 5M Active Rows	$0.44 per DPUs-hour (development endpoints) + per-job costs	Starts around $1,000/mo (includes Snowflake + ETL up to 250k MAR)
Free tier
Location	EU	AWS Global (multi-region)	San Francisco, CA, USA
Extract data (ETL)
Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL)
Transformations
AI Assistant
On-Premise
Orchestration
Lineage
Version control
Load to/from Excel		Via JDBC to S3 CSVs	Via CSV/Flat File uploads
Load to/from Google Sheets
Two-Way Sync
dbt Core Integration
dbt Cloud Integration
OpenAPI / Developer API
G2 rating	4.8	4.1	4.6

Overview

AWS Glue in Short

AWS Glue is a fully managed, serverless ETL service from AWS that automates data discovery, cataloging, and transformation using the Glue Data Catalog and PySpark. It integrates natively with AWS services like S3, Redshift, RDS, and DynamoDB, and supports third-party sources via JDBC. Glue offers both batch and streaming ETL, along with visual tools like Glue Studio and low-code options like DataBrew. It automatically scales based on workload, supports job scheduling and orchestration, and provides monitoring through CloudWatch. Ideal for AWS-centric teams, Glue simplifies large-scale data integration with minimal infrastructure management.

Pros

Serverless, no infrastructure to manage; Glue provisions compute as needed (Apache Spark under the hood).
Built-in Data Catalog for schema discovery, versioning, and integration with Athena and Redshift Spectrum.
Supports Python (PySpark) and Scala ETL scripts with mapping and transformation APIs for complex logic.
Deep integration with AWS ecosystem (CloudWatch monitoring, IAM for security, S3 triggers).

Cons

Cost can be unpredictable for long-running or high-concurrency jobs (billed per Data Processing Unit-hour).
Debugging PySpark jobs in Glue requires jumping between AWS console logs and code; local testing is limited compared to local Spark.
On-premises or multi-cloud data sources require additional setup (Glue has JDBC connectors but network config can be complex).

Reviews & Quotes

G2 Reviews:

What I like about AWS Glue

“My team build a framework to fetch data from different platform through AWS Glue and stores them in S3 in the file format mention by us. That make our integration and fetching data a lot easier.”

What I dislike about AWS Glue

“Does not support xml file formats.”

Read full review

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.

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).”

Read full review

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.

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.”

Read full review

Feature-by-Feature Comparison

AWS Glue

Mozart Data

Weld

Feature

AWS Glue

Mozart Data

Weld

Ease of Use & Interface

Side-by-side

AWS Glue

AWS Glue Studio provides a visual job authoring interface where you can drag-and-drop nodes to transform data, but deeper customizations still require PySpark code. The console UI can be intimidating for new users.

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.

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.

Ease of Use & Interface

Side-by-side

Pricing & Affordability

Side-by-side

AWS Glue

Glue charges per Data Processing Unit (DPU)-hour; for example, running a small job for one hour costs ~$0.44 * number of DPUs used. While serverless, large or long-running jobs can become costly if not optimized.

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.

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.

Pricing & Affordability

Side-by-side

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

Side-by-side

AWS Glue

Features include automated schema discovery (Glue Data Catalog), PySpark/Scala job generation, job scheduling & triggers, DataBrew for visual data prep, and Glue Workflows for orchestration. Also supports streaming ETL via Glue streaming jobs.

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.

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.

Feature Set

Side-by-side

Flexibility & Customization

Side-by-side

AWS Glue

Glue allows custom PySpark scripts, supports Python libraries via wheel files, and you can integrate with AWS Lambda for custom triggers. However, debugging and local runs can be challenging compared to self-managed Spark.

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.

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.