What I like about Blendo
“Blendo’s turnkey dashboards and consolidated metrics saved us weeks of manual work—data from all our marketing tools was in BigQuery overnight.”
You’re comparing Blendo 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 | Blendo | dlt (Data Load Tool) | |
|---|---|---|---|
| Connectors | 200+ | 30+ | 60+ |
| Price | $99 / 5M Active Rows | Small plan starts ~$100/month; scales by data volume and connector count | Free (open-source) |
| Free tier | |||
| Location | EU | Sofia, Bulgaria (HQ) + USA offices | 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 | |||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.3 | — |
Overview
Blendo is a cloud-based ETL service that simplifies collecting data from popular SaaS tools (marketing, analytics, CRM) and loading it into a data warehouse or BI tool. It focuses on quick setup, automated schema updates, and easy data enrichment for marketers and analysts.

Quick setup for popular marketing and product analytics sources with pre-built dashboards.
Handles incremental syncs and auto-updates when source schemas change.
Offers a unified metrics layer with common definitions (e.g., MRR, CAC) out-of-the-box.
Niche focus on marketing/product analytics—fewer connectors for general-purpose ETL.
Transformation capabilities are limited to simple mappings; complex needs require additional tools.
Pricing scales quickly as more sources are connected or higher sync frequency is needed.
Blendo Pricing:
“Blendo’s turnkey dashboards and consolidated metrics saved us weeks of manual work—data from all our marketing tools was in BigQuery overnight.”
“Limited customization: can’t define complex transformation logic beyond basic field mappings and date handling.”
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

Blendo’s web UI focuses on connecting predefined sources (select connector, authenticate, choose tables). Once set, data flows automatically on a schedule. Very little configuration needed—ideal for marketers/analysts.

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
Blendo’s web UI focuses on connecting predefined sources (select connector, authenticate, choose tables). Once set, data flows automatically on a schedule. Very little configuration needed—ideal for marketers/analysts.
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

Plans start at $100/month for 1 source, 1 warehouse, and 1 dashboard sync. Higher tiers add connectors and more frequent syncs. Good value for small analytics use cases, but costs increase if you add many sources.

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
Plans start at $100/month for 1 source, 1 warehouse, and 1 dashboard sync. Higher tiers add connectors and more frequent syncs. Good value for small analytics use cases, but costs increase if you add many sources.
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: SaaS connectors (marketing, CRM, product), incremental sync, metrics layer, pre-built BI dashboards (Data Studio, Looker), monitoring & alerts. Limited to analytics-focused data; no general-purpose ETL features.

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: SaaS connectors (marketing, CRM, product), incremental sync, metrics layer, pre-built BI dashboards (Data Studio, Looker), monitoring & alerts. Limited to analytics-focused data; no general-purpose ETL features.
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

Users can define custom SQL transformations on the loaded data via a “SQL runner” feature, but most heavy transformations are done post-load in the warehouse or BI tool. No built-in Python/JS transforms.

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
Users can define custom SQL transformations on the loaded data via a “SQL runner” feature, but most heavy transformations are done post-load in the warehouse or BI tool. No built-in Python/JS transforms.
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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