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Managed Vector Database
Easily store and query high-dimensional vectors for semantic search and AI applications.
The Managed Vector Database service provides a simple, scalable way to manage large collections of vector embeddings, enabling fast similarity searches, recommendations, and other AI-driven use cases. This service is ideal for projects needing semantic retrieval, natural language processing, or image recognition.
How it works:
After creating a Vector DB instance, you'll receive an endpoint and API key. Simply POST or PUT your vector data, then query for similar vectors. The service handles indexing, scaling, and low-latency retrieval.
POST /api/v1/vectordb/insert
{
"id": "unique-vector-id",
"embedding": [0.25, 0.54, 0.75, ...]
}
GET /api/v1/vectordb/query?topK=5
{
"query": [0.22, 0.50, 0.80, ...]
}Get Started
Create an instance to start storing your vectors. Once created, the service will provide your unique endpoint and authentication key.
Create Vector Database InstanceUsage Stats
Vectors stored: 0
Queries this month: 0
Billing
Cost per million vectors: $X
Cost per 1K queries: $Y