Memory sized for your workload.
Vector search and agent state, tuned to what you are actually storing instead of the nearest pricing tier.
What you get
- Managed vector database with capacity planned from your real corpus
- Embedding pipeline: ingest, chunk, update, and re-index without downtime
- Conversation and task state that survives restarts
- Hybrid retrieval when pure vector search returns the wrong thing
- Straightforward migration path if you outgrow the setup
A note on sizing
Vector memory is predictable arithmetic, not a mystery. We size it from your document count, dimensions, and query pattern, and we tell you what it costs before you commit to it.
Common questions
Capacity is calculated from your document count, embedding dimensions, and query pattern. Vector memory is predictable arithmetic, so the number comes before the commitment rather than after the first invoice.
Yes. The embedding pipeline handles ingest, chunking, updates, and re-indexing without downtime.
Hybrid retrieval combines vector search with other signals for the queries where pure semantic similarity picks the wrong document.
Yes. Conversation and task state persist across restarts rather than living only in process memory.
Tell us what you are shipping.
Send the shape of the problem — what the agent does, who uses it, and what breaks today. We will tell you what we would run and what it would take.