How Physical Intelligence unified its robotics data stack with Postgres managed by ClickHouse

Physical Intelligence develops robotics foundation models intended to generalize across environments, robot embodiments, and tasks, relying on large and diverse datasets including raw training data, operational records, annotations, metadata, and telemetry. As its data grew from tens to hundreds of billions of rows, a single Amazon RDS PostgreSQL instance could no longer efficiently support both strongly consistent transactional workloads and high-cardinality analytical queries, particularly because flexible JSONB-based annotations complicated querying. The company adopted a combined PostgreSQL and ClickHouse Cloud architecture, retaining PostgreSQL for ACID-compliant operational data while replicating data through ClickPipes into ClickHouse for OLAP, search, telemetry, and large-scale annotation analysis. ClickHouse features such as columnar storage, ReplacingMergeTree, materialized views, read-write isolation, and compute-storage separation improved query performance and scalability, enabling researchers to explore data through an internal go/data application that can identify whether specific concepts, tasks, or robot experiences appear across the dataset.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.