You’re comparing dlt (Data Load Tool) vs Fivetran vs Weld. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Weld | dlt (Data Load Tool) | Fivetran | |
|---|---|---|---|
| Connectors | 200+ | 60+ | 700+ |
| Price | $99 / 5M Active Rows | Free (open-source) | Usage-based, starting $500 for 1 million MARs (no fixed base) |
| Free tier | |||
| Location | EU | DE | US |
| Extract data (ETL) | |||
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | |||
| Transformations | |||
| AI Assistant | |||
| On-Premise | |||
| Orchestration | |||
| Lineage | |||
| Version control | |||
| Load to/from Excel | |||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | — | 4.2 |
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
Fivetran is a robust cloud-based Extract-Load-Transform (ELT) platform that automates the process of data integration from various sources into popular data warehouses such as Google BigQuery, Snowflake, and Amazon Redshift. Known for its reliability and ease of setup, Fivetran provides pre-built connectors that allow businesses to extract data from a wide range of applications, databases, and services. Fivetran’s key strength lies in its ability to handle the extraction and loading of data efficiently, with minimal configuration and maintenance. This makes it an attractive choice for organizations looking to quickly onboard a data integration solution without the need for extensive engineering resources.
Wide variety of connectors
Easy setup, low maintenance, and scalability with pre-built connectors
Robust security protocols
Detailed and helpful documentation
Near real-time replication capabilities
Complex and expensive pricing model
Depends on external tools for data transformations (e.g., DBT)
Doesn't support data transformations pre-load
No AI assistant or advanced automation features
Steep learning curve for DBT beginners
From a review on G2:
“The pre-built connectors makes data integration super easy, without the need of an expensive data engineering team. If you are using DBT, there is a DBT package for most of the pre-built connectors that will provide configurable data marts/models.”
“New connectors are released infrequently, and pricing is somewhat opaque if you are not familiar. It is somewhat opinionated, so if you are not already using a modern data stack w. their preferred partners it's a bit harder to integrate.”
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
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.
While Fivetran offers a comprehensive set of connectors, it requires more technical knowledge, especially for setting up and managing advanced data transformations, as it may rely on external tools like DBT. In other words, Fivetran is easy to use for data ingestion, but transformations demand proficiency with SQL or DBT.
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
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.
While Fivetran offers a comprehensive set of connectors, it requires more technical knowledge, especially for setting up and managing advanced data transformations, as it may rely on external tools like DBT. In other words, Fivetran is easy to use for data ingestion, but transformations demand proficiency with SQL or DBT.
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
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.
Fivetran’s pricing can be quite complex and increases significantly with the volume of data, making it potentially expensive for growing companies or those with large datasets. This can be a disadvantage for teams looking for a cost-effective solution.
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
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.
Fivetran’s pricing can be quite complex and increases significantly with the volume of data, making it potentially expensive for growing companies or those with large datasets. This can be a disadvantage for teams looking for a cost-effective solution.
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
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.
Although Fivetran excels in ELT capabilities and offers near real-time data replication, it lacks advanced transformation features. Users must rely on DBT for advanced transformations, which introduces complexity but does not require a third-party platform if DBT Core is used.
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
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.
Although Fivetran excels in ELT capabilities and offers near real-time data replication, it lacks advanced transformation features. Users must rely on DBT for advanced transformations, which introduces complexity but does not require a third-party platform if DBT Core is used.
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
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.
Fivetran relies on SQL-based transformations via DBT Core, which gives users power and flexibility but may not suit those needing quick, low-code manipulation.
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
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.
Fivetran relies on SQL-based transformations via DBT Core, which gives users power and flexibility but may not suit those needing quick, low-code manipulation.
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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