Best alternatives to HFlow in 2026

Training robots or "Physical AI" models needs huge amounts of sensor data — camera, lidar, motor telemetry — but that data is messy: it comes from different rigs, needs cleaning, and has to be traceable back to exactly how and when it was recorded. HFlow is built to manage that pipeline end to end instead of gluing together generic data tools. HFlow structures data through four stages — collection, ingestion, curation, and delivery — for multimodal data in the MCAP container format (native to ROS 2 robotics stacks). It tracks quality evidence and provenance across every stage, generates and visualizes Apache Airflow DAGs, and stores a Parquet-based, version-pinned metadata catalog queryable via DuckDB without needing to load the raw recordings themselves. It runs via Docker Compose with support for S3, GCS, or Azure cloud storage, is Apache 2.0 licensed, and is backed by Y Combinator (S26), co-founded by Brandon Ong and Kingston Kuan with angel investors from Oracle, Google DeepMind, and OpenAI.