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Guides Qdrant scaling decisions.
Passed 15 of 21 test cases with the skill, and 4 of 21 without it.
and 8 more in the full report
12 fixed4 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
We need to determine if our current single-node Qdrant instance can be scaled vertically (adding RAM/CPU) before we commit to cluster sharding overhead. Which guide should we consult for vertical scaling?
| Checked | Without skill | With skill |
|---|---|---|
| References scaling-data-volume/vertical-scaling/SKILL.md. | Missed | Passed |
The instructions your model reads when the skill loads.
Route first, then answer. Match the user's symptom in the table, Read that file, and answer from it.
Do not answer from this page alone: it contains routing only, not the guidance. If two rows match, read both.
| The user says | Read |
|---|---|
| Data does not fit on a single node, running out of disk or memory as the dataset grows | scaling-data-volume/SKILL.md |
| Need to shard the collection across more nodes, data outgrew one node | scaling-data-volume/SKILL.md |
| Cannot handle enough parallel queries, need higher QPS or throughput | scaling-qps/SKILL.md |
| Can't hold the request rate, CPU is pegged | scaling-qps/SKILL.md |
| A single query is too slow, need to cut the tail latency of individual requests | minimize-latency/SKILL.md |
| p99 or tail latency too high, but traffic/QPS is fine | minimize-latency/SKILL.md |
| Queries return very large result sets and slow down | scaling-query-volume/SKILL.md |
Large limit, top-1000 queries, pagination, scroll across shards | scaling-query-volume/SKILL.md |
| Many tenants or customers, one collection each, tenant isolation | scaling-data-volume/tenant-scaling/SKILL.md |
| Only recent data matters, retention, expiring old vectors, time-based rotation | scaling-data-volume/sliding-time-window/SKILL.md |
| Single node no longer fits the workload, before deciding to shard | scaling-data-volume/vertical-scaling/SKILL.md |
| Already vertically maxed out, need more nodes, resharding | scaling-data-volume/horizontal-scaling/SKILL.md |
Latency and throughput pull opposite ways on segment count.
For latency, increase segments toward the CPU core count (default_segment_number: 16).
For throughput, use fewer and larger segments (default_segment_number: 2).
Applying the wrong direction makes the reported problem worse.
test cases passed, vs 4 / 21 without
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