Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/secondlifes/code-intel/vertical-scalingnpx skills add SecondLifes/code-intel --skill vertical-scalinggit clone --depth 1 https://github.com/SecondLifes/code-intelWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00078 | $0.00994 |
| Opus 5 | $0.00039 | $0.00497 |
| Sonnet 5 | $0.00016 | $0.00199 |
| Haiku 4.5 | $0.00008 | $0.00099 |
Grade A, and why
qdrant-vertical-scaling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to qdrant-vertical-scaling — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, avoids distributed system complexity, and is reversible.
- 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: 2or 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 * 1.5for full-precision vectors in RAM - With scalar quantization: divide by 4 (INT8 reduces each float32 to 1 byte) Quantization
- With binary quantization: divide by 32 Binary quantization
- On Qdrant 1.19 or newer, the
turbo4datatype (dense vectors only) divides by ~8 on its own, without needing separate quantization Vector datatypes - Add overhead for HNSW index (~20-30% of vector data), payload indexes, and WAL
- Reserve 20% headroom for optimizer operations and OS cache
- Monitor actual usage via Grafana/Prometheus before and after resizing Monitoring
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 70 lines · 78 tokens per session scan A 2c4c91144c27
qdrant-vertical-scaling is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 21d ago), licensed Apache-2.0. It adds 78 tokens to every session and 994 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to qdrant-vertical-scaling, differing in 0 lines, and is treated as a copy.
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