A serverless vector database is one where the infrastructure scales automatically and you pay for what you actually use — storage, writes, and queries — rather than provisioning and paying for fixed capacity in advance. There are no servers or clusters for you to size, manage, or keep running; the service handles all of that behind a simple API.
The model’s appeal is that it removes both capacity planning and idle cost. Traditional deployments require you to provision enough resources for peak load and pay for them continuously, even when traffic is low. A serverless database expands and contracts with demand, so you neither run out of capacity during spikes nor pay for unused headroom during quiet periods, which is especially attractive for variable or unpredictable workloads.
Serverless architectures often decouple storage from compute, which also helps with multi-tenancy by reducing noisy-neighbour effects and making per-tenant scaling cleaner. The trade-offs are the usual ones for managed services — some loss of control and potential cost unpredictability at very high, steady volumes — but for many teams the operational simplicity and pay-as-you-go pricing make serverless the easiest way to run vector search in production.