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How to handle scaling of Qdrant, including horizontal and vertical scaling strategies, sharding, replication, and load balancing. Use when you want to scale your Qdrant deployment to handle increased load or larger datasets.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Search and store knowledge locally using Qdrant Edge. No server needed.
Guides developers through building community Qdrant skills that embed domain knowledge into Claude. Use when someone wants to "create a community qdrant skill", "build an external skill for qdrant", "make a qdrant skill for the community", "build a public qdrant skill", or asks how to "create a skill for qdrant memory issues for developers". Also trigger when someone says "I want to build an open source qdrant skill" or "help me create a qdrant skill for public use". Do NOT use for internal SA skills, general non-Qdrant skill creation, or Python project skills unrelated to Qdrant.
Diagnoses when and why to recommend specific cost optimization techniques for Qdrant customers. Use when a customer or SA says "too expensive", "need to reduce cost", "pricing too high", "how to lower price", "sticker shock", or asks about scaling costs. Also trigger when a deal is blocked by pricing, a customer compares competitor pricing, a startup says the quote is too high, or someone needs a formula to predict costs at scale. This skill answers "when should I recommend scalar vs binary quantization?" not "how to configure quantization." Do NOT use for general Qdrant setup, feature requests, or implementation how-tos.
Handles technical objections that block Qdrant deals and provides proven response playbooks. Use when a deal is stalled due to pricing concerns, missing features, performance issues, or competitive pressure. Triggers on "deal blocked", "customer concerned about cost", "proposal too expensive", "missing feature blocking production", "latency issues on call", "why not use pinecone", "customer built workaround", or "champion lost budget battle". Do NOT use for general product questions, documentation lookups, or technical implementation.
Guides Qdrant SAs through building internal skills that embed domain knowledge into Claude. Use when someone wants to "create a qdrant skill", "build a skill for qdrant diagnostics", "make an internal skill for qdrant customers", "create a qdrant troubleshooting skill", or asks how to "turn SA knowledge into a skill". Also trigger when someone says "help me build a skill for qdrant deal objections" or "I want to create a skill for qdrant cluster sizing". Do NOT use for general skill creation unrelated to Qdrant, external community skills, or Python/JS project skills.
Diagnoses and fixes Qdrant memory problems. Use when a developer reports OOM crashes, high RAM usage, HNSW consuming too much memory, latency spiking after switching storage, or needing to fit more vectors on existing hardware. Also trigger when someone asks which quantization to use, how to choose between mmap and RAM, how to estimate memory for scaling, or why their Qdrant node keeps dying. Do NOT use for initial Qdrant setup, API scripting, embedding model selection, search accuracy tuning, backups, or vector DB comparisons.
| name | qdrant-scaling |
| description | How to handle scaling of Qdrant, including horizontal and vertical scaling strategies, sharding, replication, and load balancing. Use when you want to scale your Qdrant deployment to handle increased load or larger datasets. |
| allowed-tools | ["Read","Grep","Glob"] |
Qdrant is designed to handle both large datasets and high query loads, and provides different scaling options to meet the needs of different use cases. This document navigates through different scenarios of scaling Qdrant, required in different use cases.
