We use DLT in every data pipeline we build. It's the ingestion layer in our platform, chosen after trying several alternatives. This post covers how DLT works, what you give up compared to tools like Fivetran, and why we think it's the right call for most teams.
The cost of getting data from A to B
Getting data from one place to another doesn't create business value on its own. The insights you extract from that data do.
Yet many companies spend €1,500+ per month on tools like Fivetran just to move data around. That's €18,000 per year on the non-value-adding part of your data stack.
Why traditional tools are expensive
Tools like Fivetran charge based on:
- Number of connectors - Each data source adds cost
- Rows processed - Your data growth = higher bills
- Premium features - Advanced transformations cost extra
A typical mid-sized company with 10-15 data sources easily reaches €1,500/month. Scale up, and costs balloon to €3,000-5,000/month.
Enter dlt: the open-source alternative
DLT (Data Load Tool) is a Python library for building data pipelines. It's free, open-source, and backed by a thriving community (~5,000 GitHub stars).
What dlt offers
- Free forever - No per-connector or per-row fees
- 100+ verified sources - APIs, databases, files, cloud services
- Automatic schema evolution - Adapts to source changes
- Built-in data quality - Validation and monitoring included
- Python-native - Customize anything, integrate with your code
- Version controlled - All pipelines are code in Git
The real cost comparison
Fivetran
€1,500/month
€18,000 per year
- Limited to pre-built connectors
- Customization requires paid support
- Vendor lock-in
- Costs scale with data volume
Dlt + our platform
~€100/month
€1,200 per year
- 100+ connectors + custom sources
- Fully customizable (it's Python)
- No vendor lock-in
- Fixed compute costs
Save €16,800/year
But is it production-ready?
Yes. DLT is used by companies running billions of rows daily. It's built by data engineers who understand production requirements:
- Incremental loading for efficiency
- Automatic retries and error handling
- Schema evolution without breaking pipelines
- Built-in observability and logging
- Active community support
Our approach: dlt + modern infrastructure
We run DLT pipelines in Docker containers on Google Cloud Run:
- Cloud Run Jobs - Serverless compute, pay only when running
- Cloud Scheduler - Trigger workflows on your schedule
- Cloud Workflows - Trigger DBT once DLT is done
- Git version control - Track every change
This architecture is lightweight, scalable, and costs a fraction of traditional platforms.
What about dbt?
DLT gets data into your warehouse. DBT transforms that raw data into insights. Together, they form a complete, cost-efficient stack.
Learn more about how DBT turns data into business value.
The bottom line
Data ingestion is a commodity. Don't overpay for it.
With DLT and modern cloud infrastructure, you get enterprise-grade capabilities at a fraction of the cost. Save €16,800+ per year and invest that budget where it creates real value: in analytics, ML models, and insights.