You’re comparing Weld vs IBM DataStage vs Matia. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | IBM DataStage | Matia |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Enterprise licensing (custom, usually six-figure annual) | Custom, unified platform license |
| Free tier | No | No | No |
| Location | DK, (EU) | US | US |
| Connectors & Sync | |||
| Connectors | 200+ | 200+ | 200+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | Yes |
| Two-Way Sync | Yes | No | Yes |
| 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 | Yes | Yes |
| Version control | Yes | Yes | No |
| On-Premise | No | Yes | No |
| OpenAPI / Developer API | Yes | No | No |
| Integrations | |||
| Load to/from Excel | Yes | Yes | No |
| Load to/from Google Sheets | Yes | No | No |
| Ratings | |||
| G2 rating | 4.8 | 4 | 4.9 |
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
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).”
Overview
Matia is a unified DataOps platform that combines data ingestion, ELT, reverse ETL, data observability, and data cataloging into one cloud-based interface. It aims to simplify data workflows by reducing the need for multiple tools, offering an integrated approach to pipeline monitoring, metadata management, and data activation.
Unified platform combining ingestion, transformations, observability, and catalog features
New connectors added quickly based on customer needs
Built-in observability for anomaly detection and pipeline monitoring
Native catalog and lineage for metadata visibility
Responsive support and rapid product updates
Newer platform with features still maturing
Cloud-only SaaS with no on-prem deployment option
Smaller community and fewer third-party tutorials
Pricing is not publicly listed
All-in-one approach may lack depth in specialized areas
Matia Homepage:
“Matia unifies ETL, observability, catalog, and reverse ETL so teams can focus on driving actionable insights and accelerating innovation.”
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.
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.
Matia offers a modern, integrated interface that brings ingestion, observability, and cataloging together. Users find it straightforward to set up, though some advanced capabilities are still evolving due to the platform’s early stage.
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.
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.
Matia offers a modern, integrated interface that brings ingestion, observability, and cataloging together. Users find it straightforward to set up, though some advanced capabilities are still evolving due to the platform’s early stage.
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.
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.
Matia’s pricing is available only through sales. While there is no free tier, some teams find value in the ability to consolidate multiple tools into one unified platform.
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.
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.
Matia’s pricing is available only through sales. While there is no free tier, some teams find value in the ability to consolidate multiple tools into one unified platform.
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.
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.
The platform includes ingestion pipelines, ELT transformations, reverse ETL, observability, anomaly detection, and a data catalog with lineage. Its feature set covers the full data lifecycle, though it is still developing compared to more established tools.
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.
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.
The platform includes ingestion pipelines, ELT transformations, reverse ETL, observability, anomaly detection, and a data catalog with lineage. Its feature set covers the full data lifecycle, though it is still developing compared to more established tools.
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.
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.
Matia supports custom connector requests and configurable quality rules. As a managed platform, it prioritizes ease of use over deep infrastructure customization.
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.
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.
Matia supports custom connector requests and configurable quality rules. As a managed platform, it prioritizes ease of use over deep infrastructure customization.
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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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