docs: Add pgvector and Vector Buckets differences (supabase#40893)

Add pgvector differences

Author: ChrisChinchilla

apps/docs/content/guides/storage/vector/introduction.mdx

@@ -40,6 +40,22 @@ Vector buckets excel at:
 - **RAG (Retrieval-Augmented Generation)** - Find relevant context for LLM queries
 - **Personalization** - Recommend tailored content based on user embeddings
 
+## Comparison to pgvector
+
+Vector buckets share similarities to pgvector and matches the developer experience of using pgvector as much as possible, but Vector buckets and any [Foreign Data Wrappers (FDW)](/docs/guides/database/extensions/wrappers/overview) they use only support one similarity search algorithm, the `<===>` distance operator.
+
+This makes Vector buckets ideal for:
+
+- Large-scale data storage
+- Backend processing workflows
+- Applications where speed is less critical
+
+And pgvector is ideal for:
+
+- Fast prototyping and small data volumes
+- Applications requiring quick response times
+- User-facing features closer to the front end
+
 ## How Vector buckets work
 
 1. **Create a bucket** to organize your vector data

apps/docs/content/guides/storage/vector/querying-vectors.mdx

@@ -11,6 +11,12 @@ Expect rapid changes, limited features, and possible breaking updates. [Share fe
 
 Vector similarity search finds vectors most similar to a query vector using distance metrics. You can query vectors using the JavaScript SDK or directly from Postgres using SQL.
 
+<Admonition type="tip" title="Comparison to pgvector">
+
+Vector buckets and any [Foreign Data Wrappers (FDW)](/docs/guides/database/extensions/wrappers/overview) they use only support one similarity search algorithm, the `<===>` distance operator.
+
+</Admonition>
+
 ## Basic similarity search
 
 <Tabs