What I like about 5X
“5X’s all-in-one stack reduced our tool sprawl: data ingested, transformed, and even dashboards were live in days. Their in-house experts helped us onboard quickly.”
You’re comparing 5X 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 | 5X | dlt (Data Load Tool) | |
|---|---|---|---|
| Connectors | 200+ | 500+ | 60+ |
| Price | $99 / 5M Active Rows | Free Forever tier; Starter ~$500/month for limited usage, then scales with volume | Free (open-source) |
| Free tier | |||
| Location | EU | Singapore (HQ) + USA, UK, India | 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 CSV imports/exports) | ||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.9 | — |
Overview
5X is an end-to-end data platform that combines ETL (500+ connectors), a managed data warehouse, dbt-powered transformations, reverse ETL, and a built-in BI/semantic layer under one roof. It aims to deliver a unified, low-code interface where teams can onboard data, model it, activate it in operational tools, and visualize it—all in a single platform.

500+ connectors for both source and destination (data warehouses, SaaS apps, ad platforms, etc.).
Includes managed Snowflake/BigQuery warehouse, dbt core integration for transformations, and reverse ETL to push data back to apps—all in one platform.
Built-in BI layer and semantic metric definitions so you can build dashboards without a separate BI tool.
Dedicated in-house data experts provide consulting and implementation support, accelerating time-to-value.
Premium pricing (Starter ~$500/mo, scaling with connectors/warehouse usage) can be high for small teams.
Being a newer startup, the community and third-party tutorials are limited; some advanced features are still maturing.
Heavily opinionated stack—if you use an alternate data warehouse or BI tool, integration can require workarounds.
5X Testimonials:
“5X’s all-in-one stack reduced our tool sprawl: data ingested, transformed, and even dashboards were live in days. Their in-house experts helped us onboard quickly.”
“As a relatively new entrant, some advanced features (e.g., AI-driven pipeline suggestions) are still in beta, and pricing can scale up quickly with heavy usage.”
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

5X’s UI guides users through onboard: connect sources, choose warehouse, define dbt models, and build dashboards—all via a low-code interface. Non-technical users can leverage pre-packaged templates, while power users can write custom SQL/dbt.

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
5X’s UI guides users through onboard: connect sources, choose warehouse, define dbt models, and build dashboards—all via a low-code interface. Non-technical users can leverage pre-packaged templates, while power users can write custom SQL/dbt.
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

5X offers a Free Forever tier for small usage (limited connectors/rows). The Starter plan (~$500/mo) covers basic use, but costs increase with data volume and connector count. ROI calculations often justify the cost by consolidating multiple tools.

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
5X offers a Free Forever tier for small usage (limited connectors/rows). The Starter plan (~$500/mo) covers basic use, but costs increase with data volume and connector count. ROI calculations often justify the cost by consolidating multiple tools.
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

End-to-end features: ETL ingestion (500+ connectors), managed warehouse provisioning, dbt transformations, reverse ETL, built-in BI/visualization, and a semantic layer. It also includes lineage tracking and API endpoints for data apps.

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
End-to-end features: ETL ingestion (500+ connectors), managed warehouse provisioning, dbt transformations, reverse ETL, built-in BI/visualization, and a semantic layer. It also includes lineage tracking and API endpoints for data apps.
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

While 5X is tightly integrated, it allows custom dbt models, Python UDFs, and can ingest data from arbitrary APIs. If you don’t need the BI layer, you can skip that component. Custom connectors can be built upon request by their team.

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
While 5X is tightly integrated, it allows custom dbt models, Python UDFs, and can ingest data from arbitrary APIs. If you don’t need the BI layer, you can skip that component. Custom connectors can be built upon request by their team.
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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