Comparing FME with Mozart Data and Weld
Carolina Russ6 min read
What is FME
FME (by Safe Software) is a data integration and transformation platform primarily focused on spatial and GIS data, but it also supports a wide range of non-spatial ETL. It provides a graphical workspace where users can build data pipelines, handling over 450 formats and applications, with strong data quality and validation capabilities.
Pros
- Supports 450+ data formats, making it ideal for GIS and non-GIS integration.
- Graphical Workspaces with extensive transformer library for spatial (coordinate reprojection, topology) and non-spatial transformations (joins, data cleansing).
- FME Server enables automated scheduling, breakout clustered processing, and REST API for triggering workflows.
- Strong data validation and quality features—users can apply conditional checks and notifications when data doesn’t meet criteria.
Cons
- High licensing costs for desktop (FME Desktop) and server components; often priced per core for server deployments.
- Primarily geared toward GIS/spatial use cases; non-spatial ETL use is possible but the interface and transformers are optimized for spatial workflows.
- Large learning curve for complex workspaces—dragging many transformers can become unwieldy visually.
FME Product Overview:
What I like about FME
FME’s ability to handle complex spatial transformations and 450+ formats is unmatched. The drag-and-drop workspace builder drastically speeds up geospatial ETL.
What I dislike about FME
Licensing can be expensive for smaller organizations. Focus on spatial means some general ETL features are less polished than GIS-specific functions.
What is Mozart Data
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.
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).
What is Weld
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
- Premium quality connectors and reliability
- User-friendly and easy to set up
- AI assistant
- Very competitive and easy-to-understand pricing model
- Reverse ETL option
- Lineage, orchestration, and workflow features
- Advanced transformation and SQL modeling capabilities
- Ability to handle large datasets and near real-time data sync
- Combines data from a wide range of sources for a single source of truth
Cons
- Requires some technical knowledge around data warehousing and SQL
- Limited features for advanced data teams
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.
FME vs Mozart Data: Ease of Use and User Interface
FME
FME’s Workbench is a desktop application where users connect Reader and Writer transformers to map and transform data. While powerful for spatial, the GUI can feel cluttered for workflows with hundreds of transformers.
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.
FME vs Mozart Data: Pricing Transparency and Affordability
FME
FME Desktop licenses start around $2,000/year. FME Server pricing is per-core (often $20k+/core for an annual license). Expensive for small teams, but justified where spatial data integration is critical.
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.
FME vs Mozart Data: Comprehensive Feature Set
FME
Supports reading/writing 450+ formats (GIS, CAD, JSON, XML, databases), transformer library (spatial & non-spatial), workflow orchestration via FME Server, automation (event-based, scheduled), and REST API endpoints for triggering.
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.
FME vs Mozart Data: Flexibility and Customization
FME
Users can embed Python, R, or Shell scripts within transformers for custom logic. FME Server can be deployed in any environment (on-prem, AWS, Azure) and scaled horizontally. However, no built-in data catalog or lineage; separate tools needed.
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.
Summary of FME vs Mozart Data vs Weld
| Weld | FME | Mozart Data | |
|---|---|---|---|
| Connectors | 200++ | 450+ | 150+ |
| Price | $99 / Unlimited usage | Per-seat for FME Desktop ($2,000+/year) and per-core for FME Server (custom) | Starts around $1,000/mo (includes Snowflake + ETL up to 250k MAR) |
| Free tier | No | No | Yes |
| Location | EU | Surrey, BC, Canada (Safe Software HQ) | San Francisco, CA, USA |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | No |
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | Yes | No |
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | No | No |
| Version control | Yes | No | No |
| Load data to and from Excel | Yes | Yes | Yes |
| Load data to and from Google Sheets | Yes | No | Yes |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | No | Yes |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | Yes | No |
| G2 Rating | 4.8 | 4.7 | 4.6 |
Conclusion
You’re comparing FME, Mozart Data, Weld. Each of these tools has its own strengths:
- FME: supports reading/writing 450+ formats (gis, cad, json, xml, databases), transformer library (spatial & non-spatial), workflow orchestration via fme server, automation (event-based, scheduled), and rest api endpoints for triggering. . fme desktop licenses start around $2,000/year. fme server pricing is per-core (often $20k+/core for an annual license). expensive for small teams, but justified where spatial data integration is critical. .
- 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. . 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 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.. weld offers a straightforward and competitive pricing model, starting at $99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
