Hybrid search (BM25 + vector): Worth noting that ParadeDB's pg_search isn't an AWS-native feature either, you'd need to self-host it on EC2. On Azure PostgreSQL, we built pg_textsearch which provides the same BM25 ranking model (term frequency saturation, document-length normalization, IDF) natively. Fun fact, the main contributor of pg_textsearch is now on the Azure Postgres team :)
Docs: https://learn.microsoft.com/en-us/azure/horizondb/ai/full-te...
High-dimensional vectors: This is actually an area where we're ahead. pgvector with HNSW caps at 2,000 dimensions. We support pgvector for vector storage and search, and for high-dimensional / large-scale workloads we ship pg_diskann — Microsoft's graph-based vector index that supports up to 16,000 dimensions and also does advanced in-index filtering (your WHERE clauses get evaluated during graph traversal, so you don't lose recall on selective predicates).
pgvector: https://learn.microsoft.com/en-us/azure/horizondb/ai/vector-...
DiskANN high-dimension support: https://learn.microsoft.com/en-us/azure/horizondb/ai/vector-...
These are available today on Azure PostgreSQL, specifically Azure HorizonDB (Preview). Happy to dig into specifics if you have a particular workload in mind.