Notes for the problems that show up after launch: bad plans, awkward migrations, index debt, vacuum pressure, replica lag, and the small decisions that make PostgreSQL easier to operate.
TimescaleDB helps when the pain is truly time-series pain. Chunk interval, compression windows, retention, late data, and aggregate refresh policies decide whether it stays boring.
The best vector database depends on ownership boundaries, filter semantics, recall targets, migration paths, cost, and operational maturity - not benchmark screenshots.
Chunk size and overlap decide retrieval quality, citation accuracy, freshness, permissions, and cost. Treat chunks as serving data with ownership.
Vector search observability needs recall, result count, filter selectivity, freshness lag, tenant skew, index health, reranker cost, and answer grounding - not just latency.
Multi-tenant vector search is a correctness and isolation problem. Namespaces, filters, and partitions each fail differently under tenant skew and ACL rules.
Slow PostGIS systems usually do not fail because geography is hard. They fail because the query shape stops the spatial index from pruning work early.
Vector search looks easy until tenant filters, permissions, freshness, and deleted content arrive. The hard part is not nearest neighbors; it is filtered recall under production rules.
Hybrid search helps when exact terms and semantic meaning both matter. It fails when teams blend rankings without query intent, calibration, or evaluation.
Vector cost is not just storage. Dimensions, top_k, filters, rerankers, embedding refreshes, payload size, index rebuilds, and tenant skew all compound.
The hard vector database decision is not pgvector versus Pinecone on a checklist. It is whether your filters, recall target, update rate, and incident budget still fit inside Postgres.
EXPLAIN ANALYZE is readable once you know the order: find the slow node, compare estimates to actuals, check loops, then confirm buffers and waits.
A nested loop join is fast or slow depending on the row counts. Here is when it is the right plan, when it is not, and how to read the EXPLAIN output to tell.