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docs: remove cloud handle shard rebalancing from FAQ

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@@ -91,8 +91,4 @@ Memory usage in fastEmbed depends on several factors:
2. **Data Parallelism**: fastEmbed utilizes Python's multiprocessing to split large lists of strings into smaller ones and process them in parallel. While this enhances processing speed, it can also lead to higher RAM consumption.
3. **Model Used**: Memory usage varies depending on the model used to embed your data.
For optimal performance and memory management, consider these factors when using fastEmbed.
### How does your cloud handle shard rebalancing when increasing the number of nodes?
Our [cloud platform](https://cloud.qdrant.io) handles shard rebalancing when scaling out. However, it's essential to create enough shards beforehand to facilitate the scaling process effectively. For instance, if you have 3 nodes, it's advisable to choose 6 or 9 shards to allow for rebalancing upon extending your cluster, which wouldn't be possible with just 3 shards.
For optimal performance and memory management, consider these factors when using fastEmbed.