What to Do When Qdrant Needs to Scale Vertically

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 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

When Vertical Scaling Is No Longer Enough