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Weld vs Airbyte vs IBM DataStage

You’re comparing Weld vs Airbyte vs IBM DataStage. Explore how they differ on connectors, pricing, and features. Ed Logo

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Weld vs Airbyte vs IBM DataStage

FeatureWeldAirbyteIBM DataStage
Core Platform
Price
$79 / 5M Active Rows
Standard from $10/mo; Plus & Pro are custom (capacity-based)
Enterprise licensing (custom, usually six-figure annual)
Free tier
No
Yes
No
Location
DK, (EU)
US
US
Connectors & Sync
Connectors
200+
600+
200+
Extract data (ETL)
Yes
Yes
Yes
Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL)
Yes
Yes
No
Two-Way Sync
Yes
No
No
Transformations & AI
Transformations
Yes
No
Yes
AI Assistant
Yes
Yes
No
dbt Core Integration
Yes
Yes
No
dbt Cloud Integration
Yes
Yes
No
Governance & DevOps
Orchestration
Yes
No
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
Yes
Load to/from Google Sheets
Yes
Yes
No
Ratings
G2 rating
4.8
4.2
4

Overview

Weld in Short

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.

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Pros

  • 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

Cons

  • 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

Reviews & Quotes

A reviewer on G2 said:

What I like about Weld

Weld’s graphical interface is intuitive and easy to work with, even for teams with limited SQL experience. Its flexibility across sources—from databases to Google Sheets and APIs—made onboarding smooth, and performance across larger workloads was consistently strong. Support was responsive and helpful throughout our setup and ongoing use.

Overview

Airbyte in Short

Airbyte is an open-source data integration platform known for its large connector library and flexibility. It offers both self-hosted and fully managed cloud versions, with support for ELT, Reverse ETL, and custom connector development. Airbyte is especially popular with engineering-heavy teams due to its extensibility, dbt integration, and strong developer tooling. However, it typically requires more setup and ongoing maintenance compared to more polished no-code tools.

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Pros

  • Open-source and highly extensible

  • Large number of connectors (600+)

  • Managed cloud option

  • Supports ELT and Reverse ETL

  • Strong dbt Core & dbt Cloud integration

  • Engineering-focused with advanced customization options

  • Growing ecosystem including AI-assisted tools like Connection AI Copilot

Cons

  • More suited for advanced or engineering-heavy teams

  • Requires more setup and ongoing maintenance

  • Connector quality varies due to open-source contributions

  • Not ideal for non-technical users

Reviews & Quotes

In a review from Confessions of a Data Guy, he shares::

What I like about Airbyte

If you don't have workloads that currently use DBT or fit well into that model, this probably isn’t the tool for you.

Overview

IBM DataStage in Short

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.

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Pros

  • 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).

Cons

  • 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.

Reviews & Quotes

G2 Reviews:

What I like about IBM DataStage

Best data integration tool on the market with a wide range of connectors and advanced data integration and quality features.

What I dislike about IBM DataStage

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).

Feature-by-Feature Comparison

Feature
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Ease of Use & Interface

Side-by-side

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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.

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Airbyte is best suited for teams with engineering experience. While the cloud version simplifies deployment, building or modifying connectors and managing pipelines typically requires technical skills.

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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.

Pricing & Affordability

Side-by-side

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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.

airbyte logo

Airbyte offers both a generous free tier and paid plans. Standard starts at $10/month with volume-based pricing, while Plus and Pro use capacity-based pricing for more predictable costs. Depending on usage, Airbyte can be affordable, but costs may rise as workloads grow.

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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.

Feature Set

Side-by-side

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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.

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Airbyte has one of the largest connector libraries on the market and supports ELT, Reverse ETL, dbt Core, dbt Cloud, and advanced developer tooling. However, unlike some competitors, it does not offer built-in transformations or native orchestration.

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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.

Flexibility & Customization

Side-by-side

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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.

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Airbyte is highly flexible thanks to its open-source nature, custom connector builder, APIs, and developer tooling. This makes it a strong choice for engineering teams, but less appealing for organizations wanting a low-maintenance, no-code experience.

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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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