Comparing FME with Stitch and Weld



What is FME
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 Stitch
Pros
- Easy to setup and use
- A cost-friendly pricing model that's easy to understand and based on usage
- Integrates with Talend
- Includes transformations with JSON
- Fully managed no-code ELT data pipelines
Cons
- Require deep technical knowledge to get full value out of the platform
- The quality can vary a lot between different connectors as they are not maintained by Stitch
- Depends on the Singer open source framework which can break without notice
A reviewer on Gartner said::
What I like about Stitch
Stitch is an affordable tool for bringing data "as is" from the various data sources (Google Ads, Salesforce or even MySQL) to a data warehouse (Redshift, Snowflake, BigQuery, etc.)
What I dislike about Stitch
What is Weld
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 Stitch: 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.
Stitch
Stitch is relatively easy to set up and use, offering no-code connectors and an affordable pricing model, but users may need deep technical knowledge to leverage its full potential.
FME vs Stitch: 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.
Stitch
Stitch's pricing model is cost-friendly and based on usage, making it accessible for many users. However, costs can increase if data volumes grow significantly.
FME vs Stitch: 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.
Stitch
Stitch offers fully managed no-code ELT data pipelines and integrates with Talend, but relies heavily on the Singer open-source framework, which can be less stable.
FME vs Stitch: 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.
Stitch
Stitch is designed for simplicity and ease of use, but customization is limited and dependent on the capabilities of the open-source Singer framework.
Summary of FME vs Stitch vs Weld
Weld | FME | Stitch | |
---|---|---|---|
Connectors | 200++ | 450+ | 140+ |
Price | $99 / Unlimited usage | Per-seat for FME Desktop ($2,000+/year) and per-core for FME Server (custom) | $100 / 5M rows |
Free tier | No | No | Yes |
Location | EU | Surrey, BC, Canada (Safe Software HQ) | US |
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 | No |
Lineage | Yes | No | No |
Version control | Yes | No | No |
Load data to and from Excel | Yes | Yes | No |
Load data to and from Google Sheets | Yes | No | No |
Two-Way Sync | Yes | No | No |
dbt Core Integration | Yes | No | Yes |
dbt Cloud Integration | Yes | No | No |
OpenAPI / Developer API | Yes | Yes | Yes |
G2 Rating | 4.8 | 4.7 | 4.5 |
Conclusion
You’re comparing FME, Stitch, 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. .
- Stitch: stitch offers fully managed no-code elt data pipelines and integrates with talend, but relies heavily on the singer open-source framework, which can be less stable.. stitch's pricing model is cost-friendly and based on usage, making it accessible for many users. however, costs can increase if data volumes grow significantly..
- 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..