What it sells
Managed GPU-accelerated Spark + SQL at 2x speed and half the cost
Business model breakdown
Buyer: who owns the budget?
UnverifiedHead of data engineering / data platform lead who owns the warehouse bill.
Trigger: what makes them buy now?
Compute costs balloon and pipelines run for hours; in the case study GCP credits ran out and BigQuery ETL cost over $4,000 per run.
Workflow: what inputs become what output?
Point ParaQuery at existing files in cloud storage (no ingestion), run the same Spark/SQL ETL on GPU-accelerated open-source Spark, with white-glove setup replacing self-managed infra.
Economics: what it saves and how it charges
Claims 2x speed at half the cost; case study cut a monthly ETL cycle from $4,000 to $1,000 (-75%), under $2 per TiB scanned vs BigQuery on-demand $6.25/TiB.
Distribution: how it reaches customers
Early-access waitlist, risk-free pilots, custom ROI analysis and white-glove delivery; runs on GCP, AWS, Azure and GPU clouds.
Funding history and coverage
| Round date | Stage | Amount | Investors | Source |
|---|---|---|---|---|
| — | Y Combinator Spring 2025 | — | Y Combinator | ycombinator.com |
Profiles summarize public reporting and company websites. Business-model notes are MaxBC's reading of that material, not confirmed facts; unknowns are left blank rather than guessed. Updated 2026-10-12.