We're making enterprise data pipelines 10x cheaper (and actually maintainable)

After watching companies burn through $50k+/month on Big Warehouse Platforms for relatively simple data workflows, we built a cost-effective alternative that doesn't sacrifice reliability.

The problem: Most data teams are stuck between expensive managed services and the operational nightmare of rolling their own infrastructure. We've seen startups with <100GB of data paying enterprise prices, and mid-size companies afraid to experiment because each query costs real money.

Our approach: We've packaged battle-tested open source tools (Apache Airflow, dbt, ClickHouse/DuckDB) into a managed platform that:

Costs 80-90% less than traditional cloud data warehouses

Scales from gigabytes to terabytes without architectural rewrites

Gives you actual ownership of your data and transformations

Takes <30 minutes to get your first pipeline running

What makes this different:

No vendor lock-in - everything runs on standard tools you can migrate

Transparent, predictable pricing (no surprise bills)

Built for teams who want data platform benefits without platform engineering overhead

We're currently in beta with a few customers processing everything from e-commerce analytics to IoT sensor data. The cost savings have been dramatic - one customer went from $8k/month to $500/month for the same workload.

We know many of you have built internal data platforms. We're curious - what were the biggest pain points? What would make you choose a managed service over building in-house?

We're also happy to share more technical details about our architecture choices and how we achieve the cost savings.

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