What I like about Dell Boomi
“Boomi’s AtomSphere makes deploying integration processes easy—Atommachines can run anywhere (cloud or on-prem), and the visual interface is intuitive for building mappings.”
You’re comparing Dell Boomi vs dlt (Data Load Tool) vs Weld. Explore how they differ on connectors, pricing, and features.


Loved by data teams from around the world
| Weld | Dell Boomi | dlt (Data Load Tool) | |
|---|---|---|---|
| Connectors | 200+ | 200+ | 60+ |
| Price | $99 / 5M Active Rows | Subscription-based (per Atom/connection; starts ~$1000/month) | Free (open-source) |
| Free tier | |||
| Location | EU | Austin, TX, USA | DE |
| 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 (via ODBC/JDBC) | ||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.3 | — |
Overview
Dell Boomi AtomSphere is a cloud-native iPaaS that provides ETL, API management, B2B/EDI integration, and workflow automation via a visual “Atom” runtime architecture. It supports 200+ connectors (SaaS, on-prem, databases) and allows users to build, deploy, and manage integration processes (called Atoms) in a drag-and-drop interface. Boomi’s AtomSphere runs on a lightweight runtime engine that can be deployed in the cloud or on-premise for hybrid scenarios.

200+ connectors for SaaS, on-prem, and big data sources.
Cloud-native or on-prem Atom runtime allows hybrid deployments.
Visual process designer with drag-and-drop mapping, enriched by shape-specific logic (e.g., function, decision, loop).
Built-in error handling, version control, and CI/CD integration.
Costly licensing structure (per-connection, per-Atom), which can escalate for high throughput or many connectors.
Complex transformations sometimes still require scripting (JavaScript/Groovy), reducing low-code benefits for advanced scenarios.
Learning curve: mastering Atoms, Molecules, and hybrid architecture requires time, particularly for non-technical users.
Dell Boomi Documentation:
“Boomi’s AtomSphere makes deploying integration processes easy—Atommachines can run anywhere (cloud or on-prem), and the visual interface is intuitive for building mappings.”
“Licensing can be expensive, especially for high-volume data. Complex integrations can require coding despite the low-code promise.”
Overview
Dlt (data load tool) is an open-source Python library for building modern data pipelines with a code-first approach. It lets developers define ETL or ELT workflows directly in Python, making it highly flexible and easy to embed into orchestration tools like Airflow, Dagster, or Prefect. dlt comes with pre-built connectors for popular data sources, and handles schema inference, incremental loading, normalization, and retry logic automatically. It supports destinations like BigQuery, Snowflake, Redshift, and DuckDB, and is designed to reduce boilerplate while giving teams full control over their data workflows.

Open-source and free to use
High flexibility and control via Python code
60+ pre-built connectors with automatic schema evolution
Built-in incremental loading and state management
Embeddable in any orchestration (Airflow, Prefect, cron, etc.)
No graphical UI—code-first, so not accessible to non-developers
Requires engineering effort to deploy and schedule (no managed SaaS)
Limited built-in transformations compared to dedicated ETL tools
Monitoring and observability must be built around code (no native dashboard)
Smaller community and support compared to more established tools
A reviewer on Medium:
“dlt is lightweight, customizable, and removes a lot of the boilerplate around API ingestion. With just a few lines of Python, we were able to create robust pipelines that handle schema changes and incremental loads seamlessly.”
“High volume, low latency, hard-to-build stuff is complicated. It really depends.”
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

Boomi’s Integration Builder uses a web-based canvas to create process flows. Connectors and maps are configured via dialogs. Error-handling, version control, and deployment controls are integrated. Some users find building very complex workflows cumbersome despite the visual design.

dlt has no graphical interface—pipelines are defined in Python code, making it easy for developers comfortable with code but inaccessible to non-technical users.
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
Boomi’s Integration Builder uses a web-based canvas to create process flows. Connectors and maps are configured via dialogs. Error-handling, version control, and deployment controls are integrated. Some users find building very complex workflows cumbersome despite the visual design.
dlt has no graphical interface—pipelines are defined in Python code, making it easy for developers comfortable with code but inaccessible to non-technical users.
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

Boomi’s pricing is multi-faceted—permanent Atom licenses, per-connection pricing, and usage-based charges for transactions. SMBs may need to request custom quotes to stay within budget.

As an open-source library, dlt is free to use. Users only pay for the infrastructure required to run pipelines, making it highly affordable compared to paid SaaS solutions.
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
Boomi’s pricing is multi-faceted—permanent Atom licenses, per-connection pricing, and usage-based charges for transactions. SMBs may need to request custom quotes to stay within budget.
As an open-source library, dlt is free to use. Users only pay for the infrastructure required to run pipelines, making it highly affordable compared to paid SaaS solutions.
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: ETL/ELT processes, API management, EDI/B2B integration, workflow automation, data quality, and master data management. It also offers training, community forums, and professional services.

dlt provides core pipeline features: connector library, schema inference, incremental loading, and state management. It supports major destinations (Snowflake, BigQuery, Redshift, PostgreSQL, Databricks) and allows in-Python transformations or dbt integration.
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: ETL/ELT processes, API management, EDI/B2B integration, workflow automation, data quality, and master data management. It also offers training, community forums, and professional services.
dlt provides core pipeline features: connector library, schema inference, incremental loading, and state management. It supports major destinations (Snowflake, BigQuery, Redshift, PostgreSQL, Databricks) and allows in-Python transformations or dbt integration.
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

Custom scripting is supported via Groovy or JavaScript for complex transforms. Atoms can be deployed virtually anywhere for hybrid use cases. However, you rely on Boomi for core engine updates; it’s not open-source.

Because pipelines are written in Python, dlt offers unmatched customization—developers can fetch from any API, implement custom logic, and integrate with any orchestration or monitoring framework. This flexibility requires engineering investment but allows tailor-made solutions.
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
Custom scripting is supported via Groovy or JavaScript for complex transforms. Atoms can be deployed virtually anywhere for hybrid use cases. However, you rely on Boomi for core engine updates; it’s not open-source.
Because pipelines are written in Python, dlt offers unmatched customization—developers can fetch from any API, implement custom logic, and integrate with any orchestration or monitoring framework. This flexibility requires engineering investment but allows tailor-made solutions.
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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