What I like about CloverDX
“Ability to design elegant data flows and work with really dirty data. The visual design ensures that you write proper rules to deal with a variety of data quality issues”
You’re comparing CloverDX vs FME vs Weld. Explore how they differ on connectors, pricing, and features.


Loved by data teams from around the world
| Weld | CloverDX | FME | |
|---|---|---|---|
| Connectors | 200+ | 150+ | 450+ |
| Price | $99 / 5M Active Rows | Subscription or perpetual licensing (custom quotes, typically $20k+ annually) | Per-seat for FME Desktop ($2,000+/year) and per-core for FME Server (custom) |
| Free tier | |||
| Location | EU | Culver City, CA, USA | Surrey, BC, Canada (Safe Software HQ) |
| Extract data (ETL) | |||
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | |||
| Transformations | |||
| AI Assistant | |||
| On-Premise | |||
| Orchestration | |||
| Lineage | |||
| Version control | |||
| Load to/from Excel | Yes (Excel/CSV) | Yes (via Excel reader/writer) | |
| Load to/from Google Sheets | Yes (via API) | ||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.2 | 4.7 |
Overview
CloverDX is an enterprise-grade ETL/ELT platform that emphasizes flexibility, automation, and scalability in designing complex data workflows. It supports both code-based and GUI-driven development, making it suitable for both developers and data engineers. It is also known for its transformation, data quality, and data migration capabilities. CloverDX helps to deliver a seamless onboarding process for clients, saving hours of manual work with automated conversion from any file format.

Metadata-driven: automatic handling of schema drift and impact analysis across pipelines.
Visual Graphical Data Mixer for building data flows, with reusable subgraphs and components.
Supports both batch and streaming ingestion, with connectors to databases, cloud storage, Hadoop, and REST APIs.
Built-in scheduling, monitoring dashboards, alerting, and role-based access control.
High licensing costs make it less suitable for smaller teams or startups.
Designer IDE can feel heavy and less intuitive for simple tasks; learning curve for new users.
Less community presence than open-source tools, so third-party resources and tutorials are limited.
Gartner Peer Review:
“Ability to design elegant data flows and work with really dirty data. The visual design ensures that you write proper rules to deal with a variety of data quality issues”
“Lack of support for AI, Machine learning, Neural networks and ability to run basic regression.”
Overview
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.

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.
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:
“FME’s ability to handle complex spatial transformations and 450+ formats is unmatched. The drag-and-drop workspace builder drastically speeds up geospatial ETL.”
“Licensing can be expensive for smaller organizations. Focus on spatial means some general ETL features are less polished than GIS-specific functions.”
Overview
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.
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
Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses
A reviewer on G2 said:
“Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.”




Side-by-side

CloverDX Designer is an Eclipse-based IDE where developers build data flow graphs. The drag-and-drop canvas is powerful but can feel cluttered for large projects. Reusable components and parameterization help, but initial learning is significant.

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.
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.
Side-by-side
CloverDX Designer is an Eclipse-based IDE where developers build data flow graphs. The drag-and-drop canvas is powerful but can feel cluttered for large projects. Reusable components and parameterization help, but initial learning is significant.
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.
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.
Side-by-side

CloverDX’s pricing is tiered by job servers, connector count, and features—often starting around $20k/year. Best for medium-to-large organizations requiring robust metadata handling and enterprise governance.

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.
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.
Side-by-side
CloverDX’s pricing is tiered by job servers, connector count, and features—often starting around $20k/year. Best for medium-to-large organizations requiring robust metadata handling and enterprise governance.
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.
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.
Side-by-side

Features include: visual data flow designer, metadata-driven transformations, automated schema evolution, batch & streaming support, job scheduling & monitoring, role-based access, and REST/JSON/XML connectors. Also offers advanced data quality and permutation-based testing.

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.
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.
Side-by-side
Features include: visual data flow designer, metadata-driven transformations, automated schema evolution, batch & streaming support, job scheduling & monitoring, role-based access, and REST/JSON/XML connectors. Also offers advanced data quality and permutation-based testing.
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.
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.
Side-by-side

Users can develop custom Java or Groovy components for specialized transformations, extend connectors via REST templates, and integrate with external schedulers. The open API allows embedding Clover DX in other applications.

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.
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.
Side-by-side
Users can develop custom Java or Groovy components for specialized transformations, extend connectors via REST templates, and integrate with external schedulers. The open API allows embedding Clover DX in other applications.
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.
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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