Embedding Store Selection and Exit Review (Onboarding)
A checklist with 7 steps: Map each client AI feature to a retrieval pattern before any database is named.
By InnovaAI ResearchPublished
What are the steps?
Embedding Store Selection and Exit Review (Onboarding)
- 01
Map each client AI feature to a retrieval pattern before any database is named
Separate semantic search over documents, recommendation ranking, and multi-turn assistant memory into distinct workloads. A single client account often needs two of the three, and each carries different latency and filtering requirements.
- 02
Score candidate stores against the client's deployment constraint, not the demo
On-premises or data-residency requirements eliminate fully managed options early. Qdrant supports cloud, hybrid, edge, and on-premises deployment, while Pinecone runs only as a managed service, so the constraint decides the shortlist before benchmarks do.
- 03
Confirm the store handles the client's filter complexity at query time
Metadata filtering on tenant ID, date range, and permission scope is where prototypes break. Qdrant's advanced metadata filtering and Meilisearch's hybrid full-text plus semantic approach solve different halves of this problem, so test with the client's real filter set.
- 04
Decide whether vector search lives inside the operational database or beside it
MongoDB Atlas unifies operational data, vector search, and stream processing in one system, which removes a sync job but couples retrieval scaling to the primary database. Document that tradeoff in the client-facing architecture note.
- 05
Quantify the exit cost in writing before signing
Record embedding dimensions, index configuration, and any proprietary reranking or memory layer the client would lose on migration. Weaviate's built-in embedding generation and Engram memory module are convenient, but a client leaving that stack must re-embed the entire corpus.
- 06
Set a re-embedding budget line in the statement of work
Model upgrades force full corpus re-embedding, and that cost scales with document count, not query volume. Estimate it at onboarding so a mid-retainer model swap does not become an unpriced change order.
- 07
Assign a named owner for index schema changes
One person approves dimension changes, chunking strategy shifts, and metadata field additions. Uncontrolled schema drift is the most common cause of silent retrieval quality loss in agency-managed deployments.