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Official agent skill

Qdrant Vertical Scaling

by qdrant in qdrant/skills

Guides Qdrant vertical scaling decisions. An agent skill from qdrant/skills.

OfficialApache-2.0Auto-check passedDatabases

Install Qdrant Vertical Scaling

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

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

GitHub CLI
$ gh skill install qdrant/skills qdrant-vertical-scaling --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-data-volume/vertical-scaling .claude/skills/qdrant-vertical-scaling && 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-vertical-scaling
GitHub stars
253
Used in
2 other repos
Token cost
~1.2k tokens
SKILL.md length
611 words
Files
1
Skills in repo
33
Repo updated
First seen
Licence
Apache-2.0

At a glance

Guides Qdrant vertical scaling decisions. An agent skill from qdrant/skills.

  • Someone asks how to scale up a node
  • SKILL.md covers When to Scale Vertically, How to Scale Vertically in…, RAM Sizing Guidelines and When Vertical Scaling Is No…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Upgrade node size

What it does

Qdrant Vertical Scaling is an agent skill from qdrant/skills, published by the product's own GitHub organization. Guides Qdrant vertical scaling decisions. Use when someone asks 'how to scale up a node', 'need more RAM', 'upgrade node size', 'vertical scaling', 'resize cluster', 'scale up vs scale out', or when memory/CPU is insufficient on current nodes. Also use when someone wants to avoid the complexity of horizontal scaling.

Its SKILL.md is about 1.2k 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 scale up a node
  • Upgrade node size
  • Vertical scaling
  • Scale up vs scale out

Example prompts

  • “how to scale up a node”
  • “need more RAM”
  • “upgrade node size”
  • “/qdrant-vertical-scaling”

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
    • cloud.qdrant.io
    • github.com

    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 Vertical Scaling loads about 1.2k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 611 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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). 611 words, ~1,237 tokens.

Download SKILL.mdSave it as .claude/skills/qdrant-vertical-scaling/SKILL.md (or your agent's skills folder).
name
qdrant-vertical-scaling
description
Guides Qdrant vertical scaling decisions. Use when someone asks 'how to scale up a node', 'need more RAM', 'upgrade node size', 'vertical scaling', 'resize cluster', 'scale up vs scale out', or when memory/CPU is insufficient on current nodes. Also use when someone wants to avoid the complexity of horizontal scaling.

What to Do When Qdrant Needs to Scale Vertically

Vertical scaling means increasing CPU, RAM, or disk on existing nodes rather than adding more nodes. This is the recommended first step before considering horizontal scaling. Vertical scaling is simpler and avoids distributed system complexity.

  • Vertical scaling for Qdrant Cloud is done through the Qdrant Cloud Console
  • For self-hosted deployments, resize the underlying VM or container resources

When to Scale Vertically

Use when: current node resources (RAM, CPU, disk) are insufficient, but the workload doesn't yet require distribution.

  • RAM usage approaching 80% of available memory (OS page cache eviction starts, severe performance degradation)
  • CPU saturation during query serving or indexing
  • Disk space running low for on-disk vectors and payloads
  • A single node can handle up to ~100M vectors depending on dimensions and quantization
  • For non-production workloads, which are tolerant to single-point-of-failure and don't require high availability

How to Scale Vertically in Qdrant Cloud

Vertical scaling is managed through the Qdrant Cloud Console.

  • Log into Qdrant Cloud Console or use CLI tool
  • Select the cluster to resize
  • Choose a larger node configuration (more RAM, CPU, or both)
  • The upgrade process involves a rolling restart with no downtime if replication is configured
  • Ensure replication_factor: 2 or higher before resizing to maintain availability during the rolling restart

Important: Scaling up is straightforward. Scaling down requires care -- if the working set no longer fits in RAM after downsizing, performance will degrade severely due to cache eviction. Always load test before scaling down.

RAM Sizing Guidelines

RAM is the most critical resource for Qdrant performance. Use these guidelines to right-size.

  • Exact estimation of RAM usage is difficult; use this simple approximate formula: num_vectors * dimensions * 4 bytes * 1.5 for full-precision vectors in RAM
  • Quantization adds a compressed copy alongside the original vectors. RAM for vectors drops only when the originals move to disk (memory: cold) and the quantized copy stays in RAM. With scalar quantization, the copy is 1/4 the size (INT8 reduces each float32 to 1 byte) Quantization
  • With binary quantization, the copy is 1/32 the size Binary quantization
  • On Qdrant 1.19 or newer, the turbo4 datatype (dense vectors only) divides by ~8 on its own, without needing separate quantization Vector datatypes
  • Add overhead for the HNSW index (about 150 bytes per point at the default m: 16) and payload indexes; the WAL counts against disk, not RAM Capacity planning
  • Reserve 20% headroom for optimizer operations and OS cache
  • Monitor actual usage via Grafana/Prometheus before and after resizing Monitoring
Show full SKILL.md (198 more words)Show less

When Vertical Scaling Is No Longer Enough

Recognize these signals that it's time to go horizontal:

  • Data volume exceeds what a single node can hold even with quantization and mmap
  • IOPS are saturated (more nodes = more independent disk I/O)
  • Need fault tolerance (requires replication across nodes)
  • Need tenant isolation via dedicated shards
  • Single-node CPU is maxed and query latency is unacceptable
  • Next vertical scaling step is the largest available node size. You might need to be able to temporarily scale up to the larger node size to do batch operations or recovery. If you are already at the largest node size, you won't be able to do that.

When you hit these limits, see Horizontal Scaling for guidance on sharding and node planning.

What NOT to Do

  • Do not scale down RAM without load testing first (cache eviction = severe latency degradation that can last days)
  • Do not ignore the 80% RAM threshold (performance cliff, not gradual degradation)
  • Do not skip replication before resizing in Cloud (rolling restart without replicas = downtime)
  • Do not jump to horizontal scaling before exhausting vertical options (adds permanent operational complexity)
  • Do not assume more CPU always helps (IOPS-bound workloads won't improve with more cores)

© 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-data-volume/vertical-scaling 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 Vertical Scaling 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 Vertical Scaling compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qdrant Vertical Scaling this skillqdrant/skills2532 repos~1.2kAutomated 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 Vertical Scaling

What does Qdrant Vertical Scaling do?

Guides Qdrant vertical scaling decisions. An agent skill from qdrant/skills. Qdrant Vertical Scaling is an agent skill from qdrant/skills, published by the product's own GitHub organization. Guides Qdrant vertical scaling decisions.

When should I use Qdrant Vertical Scaling?

Qdrant Vertical Scaling fits situations like: someone asks how to scale up a node; upgrade node size; vertical scaling; scale up vs scale out.

How do I install Qdrant Vertical Scaling in Claude Code?

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

How do I install Qdrant Vertical Scaling in Codex?

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

Can I use Qdrant Vertical Scaling 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-vertical-scaling -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-vertical-scaling, .gemini/skills/qdrant-vertical-scaling, .github/skills/qdrant-vertical-scaling and .opencode/skills/qdrant-vertical-scaling in your project.

What does Qdrant Vertical Scaling need to run?

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

Does Qdrant Vertical Scaling access the network?

SKILL.md names 3 domains. As links in the text: skills.qdrant.tech, cloud.qdrant.io and github.com. This is read from the text; nothing was executed.

Is Qdrant Vertical Scaling 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 Vertical Scaling use?

Qdrant Vertical Scaling 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 Vertical Scaling use?

About 1.2k tokens (SKILL.md is roughly 4.9k 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 Vertical Scaling?

Skills that share tags, products or a category with Qdrant Vertical Scaling: 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 Vertical Scaling?

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.