Following the Postgres philosophy, VeloDB provides flexibility and a rich feature set by supporting a wide range of workloads that typically require multiple databases or systems, ranging from real-time analytics with data freshness at the second level at high concurrency to analytics at scale with lakehouse data, and full-text search for observability to hybrid search for GenAI that combines vector search, BM25, and SQL filters.
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High Performance in real-world conditions at any scale
VeloDB excels in delivering real-time analytics with low latency and high performance at Petabyte scale. It delivers high data freshness at ~1 sec level by providing fast ingest with even faster queries. Ingest is accelerated by a microbatching data load in columnar formats, and query performance is accelerated with over a dozen optimizations and design choices ranging from a pipeline execution engine that minimizes CPU idle time to boost concurrency to data pruning techniques that minimize data processing for fast query response.
Hear from users and customers on how they scaled analytics from Postgres
Hear from one of our customers on how they simplified their data architecture
