Overview

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Founded by the original creators of Apache Doris, VeloDB is the leading commercial distribution and managed service for the Apache Doris ecosystem. Apache Doris is a top-level Apache Software Foundation project with 14,000+ GitHub stars and 700+ contributors. VeloDB Cloud is 100% compatible with Apache Doris, so migration is seamless with zero vendor lock-in. Pay only for what you use, billed hourly across SaaS, BYOC, and Enterprise plans. Free trial available. See full pricing and the cost estimator at https://www.velodb.io/pricing .
One engine for real-time analytics at any scale VeloDB Cloud combines columnar storage, MPP (Massively Parallel Processing) architecture, and a vectorized execution engine to deliver:
- Sub-second queries on petabyte-scale data, with a cost-based optimizer and materialized views (up to 42x faster queries, 98% less data scanned)
- Real-time ingestion with second-level freshness via streaming, CDC (Change Data Capture), and micro-batch loading
- High concurrency for thousands of simultaneous queries powering customer-facing analytics and embedded BI
Replace multiple systems with one
- OLAP analytics: a faster, simpler alternative to ClickHouse, Druid, and Kylin
- Log and search analytics: replace Elasticsearch for observability at up to 10x lower cost, 5x ingestion throughput, and 80% storage savings
- Lakehouse: query Apache Iceberg, Hudi, Delta Lake, and Paimon directly (3x faster than Trino/Presto), or accelerate with internal tables
- Federated queries across MySQL, PostgreSQL, Oracle, SQL Server, and object storage (S3, GCS)
AI-ready: hybrid search, vector search, and MCP Apache Doris natively unifies vector search, full-text search, and SQL analytics in a single storage layer for Generative AI and RAG workloads:
- Hybrid Search: combine vector similarity, BM25 full-text, and SQL filters in one query for higher retrieval accuracy
- Native vector indexes for embedding-based semantic search at scale
- AI SQL functions (summarize, classify, extract, translate, sentiment) built directly into SQL
- MCP (Model Context Protocol) support to connect AI agents to live analytical data
- AI observability via OpenTelemetry, Langfuse, and OpenLLMetry
Flexible deployment
- Fully Managed SaaS: zero-ops with auto-scaling compute and storage
- BYOC (Bring Your Own Cloud): data stays in your own AWS VPC for full sovereignty; reuse your Reserved Instances and Savings Plans
- Enterprise (Self-Managed): run on EC2, EKS, or bare metal with commercial support and SLAs
Tiered storage (NVMe, EBS, S3), 10:1 compression, and AWS Graviton/ARM support deliver up to 40% better price-performance. Production-proven across financial services, e-commerce, Web3/fintech, observability, IoT, and Generative AI.
Highlights
- 1) Real-time analytical database powered by Apache Doris with sub-second query latency on petabyte-scale data; 2) AI-ready with native hybrid search (vector + full-text + SQL), MCP protocol support, and built-in AI SQL functions for RAG and agentic AI.
- 1) Unified OLAP engine replacing ClickHouse, Elasticsearch, and Trino - 10x cost reduction for log analytics, 3x faster lakehouse queries; 2) Cloud-native with storage-compute separation, tiered storage, auto-scaling, and AWS Graviton support.
- 1) Fully compatible with Apache Doris - zero vendor lock-in with bidirectional migration between managed and self-managed deployments; 2) Flexible deployment: Fully Managed SaaS, BYOC (data in your VPC), or Enterprise Self-Managed on AWS.
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