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Guides Qdrant multi-tenant scaling.
Passed 19 of 22 test cases with the skill, and 12 of 22 without it.
and 6 more in the full report
7 fixed12 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/11/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
We run a multi-tenant Qdrant infrastructure where query loads and data sizes are heavily skewed: three major enterprise clients account for 80% of our vector volume, while 5,000 smaller accounts share the rest. Treating every client identically is causing noisy-neighbor slowdowns. Can you outline an optimal resource allocation and sharding blueprint for handling unevenly sized tenants?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends tiered multitenancy to allocate dedicated shards for large tenants. | Passed | Passed |
| Recommends maintaining small tenants on shared shards in a single collection. | Passed | Passed |
| Specifies setting `is_tenant` to true on the tenant payload index. | Missed | Passed |
The instructions your model reads when the skill loads.
Do not create one collection per tenant. Does not scale past a few hundred and wastes resources. One company hit the 1000 collection limit after a year of collection-per-repo and had to migrate to payload partitioning. Use a shared collection with a tenant key.
"Isolated" tenant queries — each tenant only ever sees its own data, with good performance — is the default outcome of payload filtering (is_tenant=true) in a single shared collection. It does not require separate collections. Separate collections only apply to the legal/compliance case, not as a way to get isolation.
Here is a short summary of the patterns:
Use the default multitenancy strategy via payload filtering.
Read about Partition by payload and Calibrate performance for best practices on indexing and query performance.
At this scale, the cluster may consist of several peers. To localize tenant data and improve performance, use custom sharding to assign tenants to specific shards based on tenant ID hash. This will localize tenant requests to specific nodes instead of broadcasting them to all nodes, improving performance and reducing load on each node.
If some tenants are much larger than others, use tiered multitenancy to promote large tenants to dedicated shards while keeping small tenants on shared shards. This optimizes resource allocation and performance for tenants of varying sizes.
Use when: legal/compliance requirements demand per-tenant encryption keys or physical data separation — not merely "tenants shouldn't see each other's data," which payload filtering already guarantees on its own.
is_tenant=true) in a shared collection, not from separate collectionstest cases passed, vs 12 / 22 without
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