dlt
data load tool (dlt) is an open source Python library that makes data loading easy 🛠️
The Lens
dlt turns "write an API-to-warehouse pipeline" from a weekend project into an afternoon. It's a Python library: declare a source, point it at Postgres, BigQuery, Snowflake, or DuckDB, and it handles schema inference, schema evolution, incremental loading, and retries. Apache 2.0, and the maintainers state plainly the library stays free forever.
There's no server to run. Pipelines are plain Python that execute wherever your code already runs: cron, Airflow, GitHub Actions, a Lambda. That's the real difference from airbytehq/airbyte, which wants a platform deployment with a UI and workers. Your ops burden is whatever orchestration you already have.
Solo data engineers and small teams: this is the sweet spot, especially if you'd rather write Python than click through connector UIs. Teams that want managed runtime, monitoring, and governed collaboration can pay for dltHub Pro. Non-engineers who need hundreds of prebuilt connectors maintained by someone else should use Airbyte or stay on Fivetran.
The catch: dlt gives you a framework, not Fivetran's connector catalog. The long tail of SaaS sources means you write and maintain the extraction code yourself.
Free vs Self-Hosted vs Paid
free self hosted paid cloudFree Tier (The Library)
The full dlt library under Apache 2.0: all destinations (Postgres, BigQuery, Snowflake, Redshift, DuckDB, filesystem, and more), schema inference and evolution, incremental loading, verified sources, and transformations. pip install and go. The maintainers commit to the library staying free.
Self-Hosted Reality
There's nothing to host. Pipelines run inside whatever already executes your Python: a cron job, Airflow, Dagster, GitHub Actions, serverless functions. That makes the marginal infrastructure cost effectively zero for most teams.
Paid Tier: dltHub Pro
A managed platform on top of the library: hosted runtime, deployment, observability, data quality checks, and agentic pipeline generation. Pricing is not published; there's a two-week trial and you talk to them for rates.
The Math
Fivetran's usage-based pricing commonly lands mid-size teams at $1,000-5,000+/mo. dlt costs engineering time instead: a competent Python developer replaces the common connectors in days, but owns them afterward. The break-even depends entirely on whether you have that developer.
Verdict
Free where it counts. Pay for dltHub Pro only when pipeline operations, not pipeline writing, becomes your bottleneck.
The library is free forever. dltHub Pro is a paid managed layer you can ignore until ops becomes the bottleneck.
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Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.
License: Apache License 2.0
Use freely. Patent grant included.
Commercial use: ✓ Yes
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