The demo was easy. You embedded a few thousand documents, queries came back in single-digit milliseconds, and everyone agreed it was impressive.
Production is a different system. The corpus is fifty times larger, it changes daily, someone expects a restore procedure, and the index that fit comfortably in memory no longer does.
Vector DB Ops is about that second system. Sizing it, rebuilding it without taking the service down, backing it up in a way that actually restores, upgrading it, and knowing which of its metrics will tell you something is wrong before your users do.
No product rankings, no benchmarks, no “top 10” lists. Just the operational work that starts once the choice is already made.
Latest posts
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Sizing a vector index in memory
Why your index needs several times the RAM of the raw vectors, which terms drive the total, and how to estimate before you provision.
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Reindexing without downtime
Embedding model changed, so the whole index has to be rebuilt. Four strategies, what each costs in memory and complexity, and how to roll back.
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Backing up a vector database
Snapshots, what they miss, and why a vector-store backup is only real once you've restored it and run a recall check against a known query set.
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What to monitor on a vector database
The handful of signals that predict an incident, the ones that only confirm one, and why recall needs a synthetic probe rather than a metric.
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When your vector database gets slow
A diagnosis order for rising query latency: read the percentile shape first, then work through memory, filters, segments, tombstones, and search parameters.