Merge pull request #2536 from qdrant/sizing-calculator-reference

Update Sizing Calculator References

qdrant-landing/content/articles/bulk-uploads-in-qdrant.md

@@ -251,4 +251,4 @@ The safest approach is to choose the right strategy for the workload instead of
 
 > **Tip:** After a large upload, confirm the collection status is green and the optimizers have finished before serving production traffic.
 
-By designing the collection and upload process before ingestion starts, you can make bulk uploads more efficient, more stable, and easier to scale as your dataset grows. To size your deployment, use the [Qdrant sizing calculator](https://sizing.qdrant.tech).
+By designing the collection and upload process before ingestion starts, you can make bulk uploads more efficient, more stable, and easier to scale as your dataset grows. To size your deployment, use the [Qdrant sizing calculator](https://sizing.qdrant.tech/estimate).

qdrant-landing/content/documentation/capacity-planning.md

@@ -20,6 +20,8 @@ When setting up your cluster, you'll need to figure out the right balance of **R
 - Your cluster's replication settings.
 - Whether you're using quantization and how you’ve set it up.
 
+<aside role="status">To estimate your collection footprint interactively, try the <a href="https://sizing.qdrant.tech/estimate">Qdrant Sizing Calculator</a>.</aside>
+
 ## Calculating RAM size 
 
 You should store frequently accessed data in RAM for faster retrieval. If you want to keep all vectors in memory for optimal performance, you can use this rough formula for estimation:

qdrant-landing/content/documentation/scaling/vertical-scaling.md

@@ -46,12 +46,12 @@ 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>
 
 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/)).
-
 ## When Vertical Scaling Is No Longer Enough
 
 These signals mean it's time to scale horizontally instead of resizing further: