You’re comparing Weld vs FME vs IBM DataStage. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Feature | Weld | FME | IBM DataStage |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | FME Desktop ~$2,000+/year per seat; FME Server per-core (custom pricing) | Enterprise licensing (custom, usually six-figure annual) |
| Free tier | No | No | No |
| Location | DK, (EU) | Surrey, BC, Canada | US |
| Connectors & Sync | |||
| Connectors | 200+ | 450+ | 200+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | No | Yes |
| Version control | Yes | No | Yes |
| On-Premise | No | Yes | Yes |
| OpenAPI / Developer API | Yes | Yes | No |
| Integrations | |||
| Load to/from Excel | Yes | Yes (Excel reader/writer) | Yes |
| Load to/from Google Sheets | Yes | No | No |
| Ratings | |||
| G2 rating | 4.8 | 4.7 | 4 |
Overview
Weld is a unified ELT and data activation platform that combines ingestion, modeling, transformations, orchestration, lineage, and reverse ETL in a single SaaS interface. With premium in-house–built connectors, an intuitive UI, and near real-time syncs, Weld enables both technical and non-technical users to create and manage data workflows efficiently. Weld also includes an AI assistant to support SQL modeling, generate transformations, and streamline repetitive tasks. Teams can ingest data from a wide range of sources—including marketing platforms, CRMs, databases, Google Sheets, Excel, and APIs—into their cloud data warehouse and activate it back into business tools.
Lineage, orchestration, and workflow features included by default
Handles large datasets and near real-time data sync
ELT and reverse ETL in one platform
User-friendly interface with minimal setup required
Flat, predictable monthly pricing model
200+ in-house–built, high-quality connectors
AI assistant for modeling and transformations
Some SQL knowledge is useful for advanced modeling
Optimized for cloud-warehouse workflows (Snowflake, BigQuery, Redshift, etc.)
Feature set is streamlined for modern ELT/activation use cases
A reviewer on G2 said:
Overview
FME (by Safe Software) is a data integration and transformation platform with a strong focus on spatial and GIS data. It also supports a wide range of non-spatial ETL through a graphical workspace. With support for over 450 formats and applications, FME is well-suited for organizations needing advanced spatial transformations, validation, and complex data workflows.
Supports 450+ data formats, including extensive GIS, CAD, database, and file types.
Graphical Workbench with a large transformer library for spatial and non-spatial transformations.
FME Server adds automation, scheduling, job orchestration, REST APIs, and distributed processing.
Built-in data validation and quality tools, enabling conditional checks and notifications.
Licensing costs for FME Desktop and FME Server can be high, especially for small organizations.
Primarily optimized for spatial workflows; non-spatial ETL is supported but not the main focus.
Complex workspaces can become visually cluttered and require experience to manage efficiently.
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
IBM DataStage (part of IBM InfoSphere Information Server) is a high-performance ETL and data integration platform that supports parallel processing and massive data volumes. It provides a visual design interface (DataStage Designer) to build data flows, along with features for metadata management, data lineage, and enterprise governance. DataStage can run on-premise or on cloud (via IBM Cloud Pak for Data) and integrates with IBM’s data quality and master data management solutions.
Parallel processing engine for high-throughput ETL, optimized for large data volumes.
Robust metadata management, data lineage, and governance via InfoSphere platform integration.
Supports on-premise, virtualized, and containerized (Cloud Pak) deployments for flexibility.
Extensive transformation library (data cleansing, lookups, joins) and connectivity (files, databases, mainframes, Hadoop).
High total cost of ownership: perpetual licensing and specialized administration needed.
User interface and development experience feel dated compared to modern cloud ETL tools.
Steep learning curve for job optimization (partitioning, parallel directives) and advanced features.
G2 Reviews:
“Best data integration tool on the market with a wide range of connectors and advanced data integration and quality features.”
“I quite like the platform as a whole, but I believe it can improve regarding data lineage (it should indeed improve now with the arrival of Manta to the IBM portfolio).”
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
FME Workbench offers a desktop UI for visually designing data flows using Readers, Writers, and Transformers. It is powerful for spatial data but can become cluttered when handling large, complex pipelines.
DataStage Designer provides a visual canvas to build ETL jobs, but the interface is relatively old-school. Job parameters, parallelism, and performance tuning require specialized training. Monitoring and debugging use InfoSphere consoles.
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
FME Workbench offers a desktop UI for visually designing data flows using Readers, Writers, and Transformers. It is powerful for spatial data but can become cluttered when handling large, complex pipelines.
DataStage Designer provides a visual canvas to build ETL jobs, but the interface is relatively old-school. Job parameters, parallelism, and performance tuning require specialized training. Monitoring and debugging use InfoSphere consoles.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
FME Desktop licensing typically starts around $2,000 per year. FME Server is licensed per core and can exceed $20k per core annually, making it more suitable for mid-sized and enterprise GIS teams.
DataStage has high licensing costs (perpetual + support) and often requires dedicated hardware. Best suited for large enterprises with extensive ETL needs; cost-prohibitive for small/medium businesses.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
FME Desktop licensing typically starts around $2,000 per year. FME Server is licensed per core and can exceed $20k per core annually, making it more suitable for mid-sized and enterprise GIS teams.
DataStage has high licensing costs (perpetual + support) and often requires dedicated hardware. Best suited for large enterprises with extensive ETL needs; cost-prohibitive for small/medium businesses.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Supports 450+ formats, spatial and non-spatial transformations, workflow orchestration via FME Server, event- or schedule-based automation, REST APIs, and strong validation capabilities.
Features include: visual job design, parallel processing (MPP), pushdown optimization (offloading to DB/Hadoop), data quality integration, metadata-driven development, and enterprise governance. Also supports REST and mainframe data sources.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Supports 450+ formats, spatial and non-spatial transformations, workflow orchestration via FME Server, event- or schedule-based automation, REST APIs, and strong validation capabilities.
Features include: visual job design, parallel processing (MPP), pushdown optimization (offloading to DB/Hadoop), data quality integration, metadata-driven development, and enterprise governance. Also supports REST and mainframe data sources.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Users can embed Python, R, or Shell scripts for advanced logic. FME Server supports deployment on-prem or in cloud environments and scales horizontally. Data lineage and cataloging are not built in and require separate systems.
Custom logic can be written via routines (BASIC, Java, or Python) and embedded in jobs. DataStage can integrate with external schedulers (Control M) and monitoring tools. However, it’s not open-source, so feature evolution is tied to IBM’s roadmap.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Users can embed Python, R, or Shell scripts for advanced logic. FME Server supports deployment on-prem or in cloud environments and scales horizontally. Data lineage and cataloging are not built in and require separate systems.
Custom logic can be written via routines (BASIC, Java, or Python) and embedded in jobs. DataStage can integrate with external schedulers (Control M) and monitoring tools. However, it’s not open-source, so feature evolution is tied to IBM’s roadmap.
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CUSTOMER STORIES
“One of the biggest impacts has been unlocking new ways to buy media. Before, we didn’t have the data to back up strategic decisions – now we do.”
“Before Weld, we had to rely on custom Python scripts and manual processes that were time-consuming and error-prone.”
“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”
“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”
“Before Weld, our data infrastructure was limited and we relied heavily on Excel files and custom Python scripts.”
“Weld has saved us a ton of time, from not having data ready to having a fully functional data warehouse and connectors.”
“By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance”
“We didn’t have a good data setup, so we lacked the business insights we needed. Weld has allowed us to set up a structured data infrastructure and access insights quickly.”
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