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Official agent skill

Qdrant Scaling Qps

by qdrant in qdrant/skills

Guides Qdrant query throughput (QPS) scaling. An agent skill from qdrant/skills.

OfficialApache-2.0Auto-check passedDatabases

Install Qdrant Scaling Qps

skills CLI
$ npx skills add qdrant/skills --skill qdrant-scaling-qps -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install qdrant/skills qdrant-scaling-qps --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/qdrant/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/qdrant-scaling/scaling-qps .claude/skills/qdrant-scaling-qps && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qdrant-scaling-qps
GitHub stars
253
Used in
2 other repos
Token cost
~933 tokens
SKILL.md length
367 words
Files
1
Skills in repo
33
Repo updated
First seen
Licence
Apache-2.0

At a glance

Guides Qdrant query throughput (QPS) scaling. An agent skill from qdrant/skills.

  • Someone asks how to increase QPS
  • SKILL.md covers Performance Tuning for Higher…, Minimize impact of Update…, Horizontal Scaling for… and Disk I/O Bottlenecks, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Need more throughput

What it does

Qdrant Scaling Qps is an agent skill from qdrant/skills, published by the product's own GitHub organization. Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.

Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering Vector databases. It works with Qdrant. The repository describes itself as: Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python…. The licence is Apache-2.0.

When your agent uses it

  • Someone asks how to increase QPS
  • Need more throughput
  • Queries per second too low
  • How to handle more concurrent queries

Example prompts

  • “how to increase QPS”
  • “need more throughput”
  • “queries per second too low”
  • “/qdrant-scaling-qps”

What it can do on your machine

Read from SKILL.md and the folder at commit 476a18d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • skills.qdrant.tech

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qdrant Scaling Qps loads about 933 tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 367 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~933

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from qdrant/skills at commit 476a18d, republished under its Apache-2.0 licence (© qdrant). 367 words, ~933 tokens.

Download SKILL.mdSave it as .claude/skills/qdrant-scaling-qps/SKILL.md (or your agent's skills folder).
name
qdrant-scaling-qps
description
Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.

Scaling for Query Throughput (QPS)

Throughput scaling means handling more parallel queries per second. This is different from latency. Segment count pulls throughput and latency in opposite directions, so pick a priority per collection.

High throughput favors fewer, larger segments so each query touches less overhead.

Performance Tuning for Higher RPS

  • Use fewer, larger segments (default_segment_number: 2) Maximizing throughput
  • Enable quantization pinned in RAM to reduce disk IO: memory: pinned on Qdrant 1.19 or newer, always_ram: true on 1.18 or older Quantization
  • Use batch search API to amortize overhead Batch search

Minimize impact of Update Workloads

  • Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads Low latency search
  • Set optimizer_cpu_budget to limit indexing CPUs (e.g. 2 on an 8-CPU node reserves 6 for queries)
  • Configure delayed read fan-out (v1.17+) for tail latency Delayed fan-outs

Horizontal Scaling for Throughput

If a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.

  • Shard replicas serve queries from replicated shards, distributing read load across nodes
  • Each replica adds independent query capacity without re-sharding
  • Use replication_factor: 2+ and route reads to replicas Distributed deployment

See also Horizontal Scaling for general horizontal scaling guidance.

Show full SKILL.md (170 more words)Show less

Disk I/O Bottlenecks

If it is not possible to keep all vectors in RAM, disk I/O can become the bottleneck for throughput. In this case:

  • Upgrade to provisioned IOPS or local NVMe first. See impact of disk performance to vector search in Disk performance article
  • Use io_uring on Linux (kernel 5.11+) io_uring article
  • In case of quantized vectors, prefer global rescoring over per-segment rescoring to reduce disk reads. Example in the tutorial
  • Configure higher number of search threads to parallelize disk reads. Default is cpu_count - 1, which is optimal for RAM-based search but may be too low for disk-based search. See configuration reference
  • If still saturated, scale out horizontally (each node adds independent IOPS)

What NOT to Do

  • Do not expect one segment configuration to maximize both throughput and latency: pick a priority per collection
  • Do not use many small segments for throughput workloads (increases per-query overhead)
  • Do not scale horizontally when IOPS-bound without also upgrading disk tier
  • Do not run at >90% RAM (OS cache eviction = severe performance degradation)

© qdrant, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/qdrant-scaling/scaling-qps of qdrant/skills.

Open the folder on GitHubat commit 476a18d

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in qdrant/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Qdrant Scaling Qps next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qdrant Scaling Qps compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qdrant Scaling Qps this skillqdrant/skills2532 repos~933Automated safety check: PassApache-2.0
Qdrant Performance Optimizationgithub/awesome-copilot40k1 repos~461Automated safety check: PassMIT
Qdrant Scalinggithub/awesome-copilot40k1 repos~467Automated safety check: PassMIT
Codebase Explorationgiancarloerra/SocratiCode3.3k1 repos~1.5kAutomated safety check: PassAGPL-3.0
Qdrant Vector SearchOrchestra-Research/AI-Research-SKILLs13k5 repos~3.4kAutomated safety check: PassMIT
Using Vector Databasesancoleman/ai-design-components5261 repos~3.5kAutomated safety check: PassMIT

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Works with

Categories

Questions about Qdrant Scaling Qps

What does Qdrant Scaling Qps do?

Guides Qdrant query throughput (QPS) scaling. An agent skill from qdrant/skills. Qdrant Scaling Qps is an agent skill from qdrant/skills, published by the product's own GitHub organization. Guides Qdrant query throughput (QPS) scaling.

When should I use Qdrant Scaling Qps?

Qdrant Scaling Qps fits situations like: someone asks how to increase QPS; need more throughput; queries per second too low; how to handle more concurrent queries.

How do I install Qdrant Scaling Qps in Claude Code?

Run `npx skills add qdrant/skills --skill qdrant-scaling-qps -a claude-code`. Or copy the skill folder (skills/qdrant-scaling/scaling-qps in qdrant/skills) into .claude/skills/qdrant-scaling-qps in your project. Claude Code loads it when a task matches its description.

How do I install Qdrant Scaling Qps in Codex?

Run `npx skills add qdrant/skills --skill qdrant-scaling-qps -a codex`. Or copy the skill folder (skills/qdrant-scaling/scaling-qps in qdrant/skills) into .agents/skills/qdrant-scaling-qps in your project. Codex loads it when a task matches its description.

Can I use Qdrant Scaling Qps in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qdrant/skills --skill qdrant-scaling-qps -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qdrant-scaling-qps, .gemini/skills/qdrant-scaling-qps, .github/skills/qdrant-scaling-qps and .opencode/skills/qdrant-scaling-qps in your project.

What does Qdrant Scaling Qps need to run?

SKILL.md names no scripts, command-line tools or credentials: Qdrant Scaling Qps is instructions for the agent only.

Does Qdrant Scaling Qps access the network?

SKILL.md names 1 domain. As links in the text: skills.qdrant.tech. This is read from the text; nothing was executed.

Is Qdrant Scaling Qps safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qdrant Scaling Qps use?

Qdrant Scaling Qps is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qdrant Scaling Qps use?

About 933 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qdrant Scaling Qps?

Skills that share tags, products or a category with Qdrant Scaling Qps: Qdrant Performance Optimization (github/awesome-copilot, 40k stars), Qdrant Scaling (github/awesome-copilot, 40k stars), Codebase Exploration (giancarloerra/SocratiCode, 3.3k stars) and Qdrant Vector Search (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qdrant Scaling Qps?

qdrant (a GitHub organization, an official publisher) maintains it in qdrant/skills, which has 253 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 6, 2026.

Source: qdrant/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.