Vector Database Migration and Rollout (Delivery)

Vector Database Migration and Rollout (Delivery)

1. 01

   Inventory the client's current search and retrieval stack before touching any data

   Map existing keyword search, database, and content pipelines to identify what will be replaced or augmented. Confirm the volume of records and the expected query rate so the chosen vector database can be sized correctly.

2. 02

   Select the vector database deployment model that matches the client's risk profile

   Compare fully managed options like Zilliz's Vector Lakebase or MongoDB Atlas against open-source self-hosting with Weaviate. Managed services reduce operational overhead but introduce lock-in; self-hosting gives control but requires ongoing maintenance.

3. 03

   Define the embedding strategy and choose a model that fits the data types

   Decide whether to use built-in embedding generation, as Weaviate offers, or a separate embedding API. Test at least two models on a sample of the client's content to measure retrieval quality before committing.

4. 04

   Build a proof of concept with a representative subset of the client's data

   Load 10,000 to 50,000 records into the candidate database and run the client's top search queries. Measure latency and relevance against the existing keyword system to quantify the improvement.

5. 05

   Design the hybrid search fallback for queries that vector search handles poorly

   Combine vector search with full-text or keyword search, as platforms like Meilisearch support, to catch exact matches and proper nouns. This prevents the classic failure where semantic search misses precise terms.

6. 06

   Plan the data synchronization pipeline to keep embeddings fresh

   Set up incremental updates so new or changed content is re-embedded and indexed without a full rebuild. Document the sync frequency and the process for handling deletions to avoid stale results.

7. 07

   Run a load test at 2x the client's expected peak traffic

   Simulate concurrent queries to verify the database handles the projected volume without latency spikes. Use the results to tune index parameters or scale the deployment before go-live.

8. 08

   Execute the cutover with a rollback plan and a parallel run period

   Run the new vector search alongside the old system for at least one week, comparing results and monitoring for regressions. Keep the old infrastructure available for instant rollback if critical issues surface.