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Qdrant Vertical Scaling

QdrantOfficialPublished by Qdrant

  • Skill
  • Guidelines
  • Cloud and DevOps
  • Apache-2.0
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From the author

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.
— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to vertical scaling for Qdrant, helping users determine when to increase CPU, RAM, or disk resources on existing nodes. It emphasizes vertical scaling as the primary strategy to avoid the operational complexity of distributed systems, while offering specific thresholds for memory usage, CPU saturation, and disk capacity. The guide covers cloud-specific resizing procedures, RAM sizing formulas, and critical warnings regarding performance cliffs and downtime prevention.

What it does

  • Defines the criteria for when to scale Qdrant nodes vertically versus horizontally.
  • Provides guidelines for monitoring RAM, CPU, and disk usage to identify scaling needs.
  • Outlines the process for resizing Qdrant Cloud clusters safely.
  • Explains the impact of quantization and memory tiers on RAM requirements.
  • Lists common pitfalls to avoid during the scaling process, such as improper downscaling.
  • Identifies performance indicators that signal the need to transition to horizontal scaling.

When to use it

  • When current node resources are insufficient for the workload.
  • When RAM usage approaches 80% capacity.
  • When CPU saturation occurs during query serving or indexing.
  • When disk space is running low for vectors and payloads.
  • When you want to avoid the complexity of managing a distributed cluster.
  • Before performing a resize operation to ensure best practices are followed.

Advantages

  • Simplifies infrastructure management by avoiding distributed system complexity.
  • Provides clear, actionable thresholds to prevent performance degradation.
  • Includes specific guidance on memory optimization techniques like quantization.
  • Helps prevent downtime during cloud resizing through replication recommendations.
  • Offers a clear path for when to transition from vertical to horizontal scaling.

Usage examples

“How do I know if I should scale my Qdrant node up?”
The agent explains that vertical scaling is appropriate when CPU, RAM, or disk are nearing capacity, provided the workload does not yet require distribution, and warns about the 80% RAM threshold.
“What should I check before resizing my Qdrant Cloud cluster?”
The agent advises ensuring a replication factor of at least 2 to maintain availability during the rolling restart and suggests monitoring current usage via metrics.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/qdrant/qdrant-vertical-scaling/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip qdrant-vertical-scaling.zip -d .agents/skills

Documentation by Qdrant

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

  • Resize in the Qdrant Cloud Console or with the CLI tool. Every node in the cluster is scaled to the same size Vertical scaling

  • 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

  • Estimate RAM per component (vectors, HNSW index, ID tracker, payload indexes) for the structures you keep in RAM, using the formulas in Capacity planning. For a full sizing walkthrough, see qdrant-sizing

  • 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 TurboQuant datatypes shrink vectors on their own, without separate quantization: turbo4 by ~8x (1.19+), turbo8 by ~4x and turbo16 by ~2x (both 1.20+, higher recall). They apply to dense vectors and multivectors Vector datatypes

  • On Qdrant 1.20 or newer, the ID tracker can move to cached or cold memory tiers (previously always resident), which frees RAM at very high point counts Memory tiers

  • 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

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)

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

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Attribution and licence

Published by
Qdrant
Publisher
Qdrant (Official)
Source
qdrant/skills · skills/qdrant-scaling/scaling-data-volume/vertical-scaling · commit 9f55f6a
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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