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qdrant-landing/content/documentation/scaling/vertical-scaling.md
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@@ -46,15 +46,11 @@ Exact RAM usage is difficult to predict precisely, but this formula gives a reas
num_vectors * dimensions * 4 bytes * 1.5
```
Quantization can reduce this estimate by a factor of 4 to 32, depending on the quantization method.
Quantization can reduce this estimate by a factor of 4 to 32, depending on the quantization method. See [Quantization](/documentation/manage-data/quantization/) for the tradeoffs between quantization methods, and monitor actual memory usage before and after resizing (see [Monitor Collection Memory Usage](/documentation/ops-monitoring/memory-usage/)).
<aside role="status">
To get a more detailed sizing estimate, try the <a href="https://sizing.qdrant.tech/estimate">Qdrant Sizing Calculator</a>.
</aside>
<aside role="status">To get a more detailed sizing estimate, try the <a href="https://sizing.qdrant.tech/estimate">Qdrant Sizing Calculator</a>.</aside>
On top of the vector data itself, budget for the HNSW index, which typically adds 20% to 30% overhead, along with payload indexes and the write-ahead log. Reserve about 20% headroom for optimizer operations and operating system cache.
See [Quantization](/documentation/manage-data/quantization/) for the tradeoffs between quantization methods, and monitor actual memory usage before and after resizing (see [Monitor Collection Memory Usage](/documentation/ops-monitoring/memory-usage/)).
<aside role="status"> On top of the vector data itself, budget for the HNSW index, which typically adds 20% to 30% overhead, along with payload indexes and the write-ahead log. Reserve about 20% headroom for optimizer operations and operating system cache.</aside>
## When Vertical Scaling Is No Longer Enough