The Serverless Vector Database for Production AI

LanceDB Cloud keeps your vectors and metadata in Lance tables on object storage, then scales compute on demand for low-latency retrieval, hybrid queries, and continuous updates, without running clusters or paying a memory tax.

Unlike most “serverless vector DBs” that still lock you into memory-bound indexing and proprietary storage, LanceDB Cloud stores data in the open Lance format, so storage stays cheap, random access stays fast, and schema/feature evolution doesn’t force rewrites.

True Cloud Vector Scale

Plenty of cloud vector databases can scale compute and separate storage from query nodes. The difference with LanceDB Cloud is how your index scales as your dataset grows: we use disk-based vector + secondary indexes on top of Lance tables in object storage, so you can grow to large collections without being forced into always-on, memory-heavy overprovisioning.

With LanceDB Cloud:

• Scale compute to traffic (burst for peak, scale down when idle).
• Storage + compute are decoupled: data stays in Lance on object storage, query capacity scales independently.
• Disk-based vector + secondary indexes to keep performance predictable as indexes grow.
• Open data format: your vectors + metadata live in Lance tables, portable and interoperable.

The result is a cloud vector deployment where you size for actual traffic, not for worst-case spikes plus a safety margin.

Vector Database Pricing at Scale

For AI and vector databases, the real question isn’t the headline price on a page. It’s what you spend over time to keep retrieval fast and avoid paying twice for storage.

With LanceDB Cloud:

• Storage stays cheap because embeddings and features live in compact columnar files on your data lake or S3-class storage, not in a separate, high-margin storage tier inside the database. You’re not storing the same data twice.
• Compute is usage-based, so you pay primarily for queries and updates, not for 24/7 provisioned clusters waiting for traffic.
• Flexible schema and data evolution (via the Lance format) means less ongoing engineering work to update and modify datasets as schemas and models change.

Cost tracks data and traffic, not a fixed cluster size or a second copy of your dataset. That’s the core of LanceDB Cloud’s vector database pricing model.

From Prototype to Distributed Vector Database

LanceDB Cloud gives you a single path from first prototype to a distributed vector database in production. You can start small and keep the same APIs and data layout as you scale, without switching products or re-platforming.

• Start with the open-source embedded library for local experiments and early services.
• Point the same schema and code at a vector database hosted on LanceDB Cloud when you want managed reliability and massive scalability.
• Increase throughput and let LanceDB Cloud handle the Distributed Vector Database footprint, instead of building and operating your own cluster layer.

Because storage lives in a shared and open data format, you’re not copying large datasets into a proprietary vector store every time you need more capacity. The vector database hosted service simply adds compute around the data you already have.

Cloud Vector Performance and Architecture

Under the hood, LanceDB Cloud runs a distributed architecture tuned for cloud vector search:

• Stateless query services that can be scaled horizontally or to zero as needed
• Data stored in Lance format on object or lake storage, so cloud vector workloads can hit the same datasets you use for analytics and training
• Built-in support for hybrid retrieval patterns (vectors plus filters) without a separate search tier

This gives you a Distributed Vector Database that behaves like a typical cloud service: elastic, fault-tolerant, and straightforward to monitor, without you wiring up your own orchestration and lifecycle scripts.

What Customers See in Practice

“LanceDB powers our retrieval layer for game development. Its vector search performance and flexibility let our team iterate on content and features quickly, without constantly rebuilding indexes or pipelines.”
- Second Dinner

Teams in similar situations see two main benefits: less engineering time tied up maintaining vector infrastructure, and a smaller, more predictable bill for high performance.

Get Started with LanceDB Cloud

If you’re evaluating serverless vector database options or trying to get a handle on vector database pricing at scale, LanceDB Cloud gives you:

• A serverless LanceDB deployment where capacity follows load
• A pricing model where storage lives on your existing lake or object store and compute is usage-based
• A clear path from local open-source use to a managed Distributed Vector Database without rewriting your stack
• Operationally managed by LanceDB

Use the Get a Demo form to walk through your workloads, traffic patterns, and cost targets with the LanceDB team and see what a realistic deployment would look like.