Trusted in production by teams at
Built for the workloads that
actually break databases.
Performance under real-world conditions
Sub-second latency under concurrency, heavy updates, and complex joins — all at once.
Most databases benchmark on clean, single-table scans. Real workloads apply three pressures at the same time: thousands of concurrent users, data that changes by the hour, and multi-table joins. VeloDB is engineered to hold sub-second latency through all three, so dashboards, data products, and AI apps stay fast as load grows.
Unified engine for analytics, search, and AI retrieval
One engine for every data type, queried in standard SQL.
Structured tables, semi-structured JSON, full-text, and vector embeddings live in one engine and answer to a single SQL statement. You retire the separate search cluster and the standalone vector database, and the pipelines that kept them in sync go away with them. Engineers and AI agents query the same system through the same SQL.
Cost-efficient by design
Lower total cost from the architecture up.
Most analytics spend comes from running and syncing many systems and paying for idle capacity. VeloDB removes both. One engine replaces the search cluster, vector store, and the Redis and ETL layers around them, using built-in caching and materialized views instead. VeloDB Cloud decouples storage from compute, so compute scales elastically, and bursty agent workloads incur costs only while they run. Doris adds columnar ZSTD compression and tiered object storage that cuts storage costs as well.
Open by default
Truly open, with full compatibility with open-source Apache Doris.
VeloDB is built on Apache Doris and stays fully compatible with it. You get the same SQL, the same MySQL wire protocol, and the same connectors as the open-source project, so everything you build on Apache Doris runs on VeloDB unchanged and moves back just as easily. VeloDB also supports integration with open catalogs such as Polaris and Unity, and open table formats like Iceberg, Hudi, Delta, and Paimon.
One engine. Four workloads.
Zero ETL between them.
Customer / Agent-Facing Analytics
Insights in milliseconds with performant search and aggregations under fast-changing data.
Log, Trace & Metric Analytics
Analyze and search PB-scale log, trace, and metric data effectively.
Hybrid Search for RAG
Power GenAI with a cost-effective knowledge store, leveraging hybrid search and progressive filtering.
Lakehouse Analytics
Minimize ETL and scale real-time OLAP with lakehouse architecture.
One engine for analytics,
search, and AI context.
Open source, runs in your cloud, proven in production.