Vector Database Selection and Proof-of-Concept (Onboarding)

Vector Database Selection and Proof-of-Concept (Onboarding)

1. 01

   Map client query patterns to embedding and search requirements

   Identify whether the use case needs semantic search, hybrid search, or RAG, and estimate data volume and latency targets. This determines whether a managed platform or self-hosted option fits.

2. 02

   Compare managed versus self-hosted deployment trade-offs

   Evaluate lock-in risks of proprietary services against the operational overhead of open-source alternatives. For example, Zilliz offers a fully managed Vector Lakebase, while Weaviate can be self-hosted.

3. 03

   Run a proof-of-concept with representative client data

   Use a sample of at least 1,000 documents to test retrieval accuracy and query latency. Measure recall@10 and p95 latency to validate the chosen database meets client expectations.

4. 04

   Assess integration effort with existing client stack

   Check whether the vector database plugs into the client's current data pipeline, embedding models, and application framework. MongoDB Atlas, for instance, unifies operational and vector data, which may reduce integration complexity.

5. 05

   Document cost projections for the client's expected scale

   Estimate monthly infrastructure costs at 10x and 100x current data volume, including egress and compute. Present a clear comparison between managed and self-hosted options.

6. 06

   Define rollback criteria and exit strategy

   Specify what triggers a switch back to the previous search solution, and outline how embeddings and indexes would be migrated if the client changes vendors.