Comparing dlt (Data Load Tool) with Estuary and Weld



What is dlt (Data Load Tool)
Pros
- 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.)
Cons
- 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:
What I like about dlt (Data Load Tool)
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.
What I dislike about dlt (Data Load Tool)
What is Estuary
Pros
- Purpose-built for real-time CDC and streaming ETL with sub-100ms latency.
- Automatic schema evolution with exactly-once delivery guarantees.
- 200+ no-code connectors for databases, SaaS apps, and message queues.
- Flexible deployment: public cloud, private cloud, or self-hosted (BYOC).
Cons
- Premium pricing model ($0.50/GB consumed + connector fees) can be expensive for small teams.
- Still growing connector catalog; niche or very new APIs may require custom work.
- Smaller community compared to older open-source tools, meaning fewer community-built resources.
Estuary Pricing Page:
What I like about Estuary
Estuary’s real-time, no-code model is magical—getting data instantly with minimal effort and near-zero pipeline maintenance. Plus, their support is fantastic.
What I dislike about Estuary
Pricing can be high for lower-volume teams, and some less-common connectors are still in development, which limits immediate use cases for niche sources.
What is Weld
Pros
- Premium quality connectors and reliability
- User-friendly and easy to set up
- AI assistant
- Very competitive and easy-to-understand pricing model
- Reverse ETL option
- Lineage, orchestration, and workflow features
- Advanced transformation and SQL modeling capabilities
- Ability to handle large datasets and near real-time data sync
- Combines data from a wide range of sources for a single source of truth
Cons
- Requires some technical knowledge around data warehousing and SQL
- Limited features for advanced data teams
A reviewer on G2 said:
What I like about Weld
First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.
What I dislike about Weld
Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.
dlt (Data Load Tool) vs Estuary: Ease of Use and User Interface
dlt (Data Load Tool)
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.
Estuary
Estuary’s UI is intuitive: users can add connectors, configure CDC streams, and specify destinations in a few clicks. Complex transformations can be written in SQL or TypeScript directly in the Flow editor, but most tasks are handled via no-code connectors.
dlt (Data Load Tool) vs Estuary: Pricing Transparency and Affordability
dlt (Data Load Tool)
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.
Estuary
While Estuary provides a 10 GB/month free tier and a 30-day trial, its consumption-based pricing ($0.50/GB + connector fees) can become costly at scale. Teams processing hundreds of GBs per month should budget accordingly.
dlt (Data Load Tool) vs Estuary: Comprehensive Feature Set
dlt (Data Load Tool)
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.
Estuary
Key features include real-time CDC (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via SQL/TypeScript or dbt. It also supports Kafka-compatibility and private storage for data replay.
dlt (Data Load Tool) vs Estuary: Flexibility and Customization
dlt (Data Load Tool)
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.
Estuary
Estuary allows custom TypeScript transforms in-stream or SQL in-destination. Pipelines can be managed via CLI (flowctl) and integrated into CI/CD. While most connectors are no-code, custom connectors can be built using the open-source Flow SDK.
Summary of dlt (Data Load Tool) vs Estuary vs Weld
Weld | dlt (Data Load Tool) | Estuary | |
---|---|---|---|
Connectors | 200++ | 60+ | 200+ |
Price | €99 / Unlimited usage | Free (open-source) | $0.50/GB consumed + per-connector fee |
Free tier | No | No | Yes |
Location | EU | DE | New York, NY, USA |
Extract data (ETL) | Yes | Yes | Yes |
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | No |
Transformations | Yes | Yes | Yes |
AI Assistant | Yes | No | No |
On-Premise | No | Yes | Yes |
Orchestration | Yes | No | Yes |
Lineage | Yes | No | Yes |
Version control | Yes | Yes | Yes |
Load data to and from Excel | Yes | No | No |
Load data to and from Google Sheets | Yes | No | Yes |
Two-Way Sync | Yes | No | No |
dbt Core Integration | Yes | No | Yes |
dbt Cloud Integration | Yes | No | No |
OpenAPI / Developer API | Yes | Yes | Yes |
G2 Rating | 4.8 | 4.8 |
Conclusion
You’re comparing dlt (Data Load Tool), Estuary, Weld. Each of these tools has its own strengths:
- dlt (Data Load Tool): 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.. 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..
- Estuary: key features include real-time cdc (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via sql/typescript or dbt. it also supports kafka-compatibility and private storage for data replay. . while estuary provides a 10 gb/month free tier and a 30-day trial, its consumption-based pricing ($0.50/gb + connector fees) can become costly at scale. teams processing hundreds of gbs per month should budget accordingly. .
- Weld: 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.. weld offers a straightforward and competitive pricing model, starting at €99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..