About Vector DB Ops
Vector DB Ops covers one narrow thing: keeping a vector database healthy after it is already in production.
Not what a vector database is. Not which one to pick. Not how the search algorithm works. The operational layer — memory, index builds, replication, backups, upgrades, dashboards, and the slow Tuesday afternoon where p99 latency has quietly tripled.
Why this is a whole site
Because the explainer content is everywhere and the operational content mostly isn’t.
There are thousands of pages telling you what an embedding is and a good number comparing products. There are far fewer telling you that your index rebuild will need roughly twice the steady-state memory while both copies exist, or that a backup of a vector store is only a backup if you have restored it into a scratch environment and run a recall check against a known query set.
That gap is what this site is for. The reader here is on call. They chose the database months ago, they are not revisiting that decision, and they need the thing to stay up.
What we think
Most vector-database incidents are capacity incidents wearing a costume. Slow queries, failed builds, OOM-killed pods, mysterious latency cliffs — trace them back and you usually find memory, or an index that outgrew the shape it was planned for.
A backup you have never restored is not a backup. This is true of every database and gets skipped more often with vector stores, because the restore is awkward to verify. Verifying it means querying it.
Rebuilds are the main event. Embedding models change, dimensionality changes, corpora get re-chunked. Any of those means rebuilding the index, and how gracefully your setup handles a rebuild determines how much of your year is spent on maintenance windows.
“It depends on your workload” is a real answer, but only when followed by what it depends on. Vector count, dimensionality, update rate, and filter selectivity change the answer to almost every question here. We name which one is driving it.
On numbers
We don’t publish benchmarks or product comparisons.
Published vector-database benchmarks are workload-specific, frequently vendor-run, and out of date within a couple of releases. A number measured on someone else’s corpus, at someone else’s dimensionality, with someone else’s filter patterns, tells you very little about yours.
Instead you get the arithmetic: which terms go into a sizing estimate, what each one scales with, and how to measure the ones only you can know. Worked examples use clearly labelled hypothetical inputs. Substitute yours and the structure holds.
Same discipline for products. Engines are named where their behaviour genuinely differs — that’s useful and checkable. They are never ranked, scored, or recommended against.
Not operational advice for your system
Everything here is general. Behaviour varies significantly between engines and versions, and the right answer depends on your data, your traffic, and your availability requirements. Read your engine’s documentation, test procedures in a non-production environment first, and never run a rebuild you haven’t rehearsed.