For millions of embeddings and low latency, the best choice depends mostly on your update pattern, filtering needs, and ops tolerance—but here’s the practical shortlist:

Best general-purpose picks

1) Qdrant

• Great balance of latency, scale, and ease of use
• Strong support for metadata filtering
• Good for millions to tens/hundreds of millions of vectors
• Easy to run self-hosted or use managed

Pick Qdrant if: you want a modern, reliable default and expect to use filters often.

2) Pinecone

• Very strong managed option
• Low-latency at scale, minimal ops
• Good for production workloads where you want to avoid infra management

Pick Pinecone if: you want the easiest managed path and are okay with vendor pricing.

3) Weaviate

• Good developer experience
• Strong schema/filtering and hybrid search options
• Solid for production, though performance/ops profile varies by setup

Pick Weaviate if: you want richer search features and a flexible platform.

If latency is the top priority

4) Milvus

• Designed for very large-scale vector search
• Good when you need serious throughput and scale
• More moving parts than Qdrant, but powerful

Pick Milvus if: you anticipate very large scale and can handle more operational complexity.

If you’re already in a search stack

5) Elasticsearch / OpenSearch

• Useful if you need keyword + vector + filters together
• Not usually the best pure vector DB for lowest latency, but convenient

Pick these if: hybrid text + vector retrieval is more important than best-in-class vector latency.

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My practical recommendation

If you want a safe default for millions of embeddings with low latency:

• Qdrant if self-hosted or flexible deployment
• Pinecone if fully managed
• Milvus if you expect to grow very large and need scale first

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What matters most for low latency

To keep latency low at million-scale, focus on:

• ANN index type (HNSW, IVF, DiskANN depending on engine)
• Metadata filtering efficiency
• Memory usage / quantization
• Top-K size
• Replication and sharding
• Embedding dimension and normalization

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Quick rule of thumb

• 1M–50M vectors: Qdrant or Pinecone
• 50M+ vectors / heavy throughput: Milvus or Pinecone
• Need keyword + vector + filters: Weaviate or OpenSearch
• Want simplest ops: Pinecone

If you want, I can give you a recommendation based on your exact constraints:

• vector dimension
• read QPS
• write/update rate
• metadata filters
• cloud vs self-hosted
• budget