Sold by: VeloDB Inc
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VeloDB Cloud: The Real-Time Analytical Database Built for Analytics and AI.
VeloDB Cloud is the fully managed, cloud-native real-time analytical database powered by Apache Doris - the fastest open-source OLAP engine trusted by 5,000+ enterprises worldwide. Deliver sub-second analytics, hybrid search, and AI-ready data infrastructure from a single unified platform.
Overview
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 15,900+ 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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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Cost/unit |
|---|---|
VeloDB Cloud Usage | $0.002 |
Dimensions summary
This listing uses a single usage-based dimension: VeloDB Cloud Usage, billed in Units. You pay for what you consume rather than a fixed subscription or seat count. Usage reflects the compute and storage your workload draws, since storage and compute scale independently. You can run multiple compute clusters that share data but isolate their workloads, and each scales on demand. Because billing tracks actual consumption, your cost rises and falls with cluster activity. There are no separate tiers or instance-size options to select on Marketplace; all consumption rolls into this one metered dimension.
Top-of-mind questions for buyers
Usage reflects the compute and storage your workload consumes. Compute is metered by vCPU running time, and storage and cache are metered by the data volume held over time. These resources scale separately, so each contributes to your metered consumption based on what your clusters actually use.
Charges track active consumption. Compute meters running vCPU time, so a suspended cluster stops accruing compute charges. Stored data still counts toward usage while it exists, since storage is metered by volume held over time. Scheduled auto-suspend can pause compute to reduce cost during idle periods.
You can run multiple compute clusters that share the same stored data but isolate their workloads. Each cluster scales independently on demand, and its running compute adds to your metered usage. Storage is billed once for the shared dataset, so added clusters raise compute consumption without duplicating storage.
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Customer support services e-mail : support@velodb.io
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Real-Time Query Performance
Sub-second query latency on petabyte-scale data with cost-based optimizer and materialized views enabling up to 42x faster queries and 98% less data scanned
Data Ingestion and Freshness
Real-time ingestion with second-level freshness via streaming, CDC (Change Data Capture), and micro-batch loading supporting high concurrency for thousands of simultaneous queries
Hybrid Search Capabilities
Native unified vector search, BM25 full-text search, and SQL analytics in a single storage layer with support for combining vector similarity, full-text, and SQL filters in one query
Multi-Source Query Federation
Federated query capability across MySQL, PostgreSQL, Oracle, SQL Server, and object storage (S3, GCS) with direct querying of Apache Iceberg, Hudi, Delta Lake, and Paimon
Storage Architecture and Optimization
Columnar storage with MPP (Massively Parallel Processing) architecture, vectorized execution engine, tiered storage (NVMe, EBS, S3), 10:1 compression, and AWS Graviton/ARM support
Distributed SQL Database Architecture
Fully managed, distributed SQL database with lock-free cloud-native architecture designed for transactional (OLTP) and analytical (OLAP) workloads in a single engine
High-Throughput Data Ingestion
Parallel, distributed lock-free ingestion capable of processing millions of events per second with real-time query processing on billions of rows
Vector Search Capabilities
Indexed vector search with full-text search capabilities optimized for generative AI applications and semantic search operations
Concurrent User Scalability
Cloud-native elastic scale-out architecture supporting tens or hundreds of thousands of concurrent users with super-low latency query performance
Unified Workload Processing
Single engine capability to power high-performance transactional, analytical, and vector workloads simultaneously without requiring data movement between systems
In-Database Analytics Capabilities
Advanced in-database analytics including machine learning, geospatial, time series, graph, text, and path analytics built into the database engine
Multi-Format Data Support
Native support for structured, semi-structured, and unstructured data with integrated analytics and AI capabilities
Vector Embedding Management
Vector Store with SQL Interface for storing, managing, and querying vector embeddings directly in the platform to support RAG, semantic search, and generative AI use cases
Open Table Format Interoperability
Native support for Apache Iceberg and Delta Lake Open Table Formats enabling seamless interoperability with lakehouse architectures on shared object storage
Federated Query Engine
QueryGrid technology providing federated query access across Teradata and third-party systems with optimizer-driven pushdown across cloud and on-premises environments without data movement
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