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Vector DB 2026 Selection: Qdrant vs Weaviate vs Milvus #

Meta Description: Tested all three on 5M vectors. Latency, throughput, memory, setup pain. When to skip vector DB for SQLite FTS5.

The vector DB space settled in 2026. Qdrant, Weaviate, Milvus dominate. This article tests all three on a 5M-vector workload and tells you when to use which — plus when to skip vector DB entirely.

⚡ TL;DR #

Qdrant: simplest setup, fastest single-node. Best for solo/small team RAG.

Weaviate: best hybrid search (vector + keyword + filters). Best for production with complex queries.

Milvus: best horizontal scaling. Best for billion-scale workloads.

Skip vector DB if < 10K docs — SQLite FTS5 often wins.

Test Setup #

• 5M vectors, 768 dimensions (BGE-large embeddings)
• Mix of similarity-only queries + filtered queries
• Single VM: 16 vCPU, 64GB RAM, 1TB NVMe
• 100 concurrent clients

Results #

Latency (p95, ms) #

Verdict: Qdrant fastest for pure similarity. Weaviate wins filters + hybrid.

Throughput (queries/sec at p95 < 50ms) #

Verdict: Qdrant fastest single-node. Milvus catches up at multi-node scale.

Memory at 5M vectors #

Verdict: Qdrant most memory-efficient.

Setup time #

Verdict: Qdrant easiest. Milvus most complex.

When to Skip Vector DB Entirely #

Under 10K documents, SQLite FTS5 often outperforms vector DB for the following reasons:

• BM25 + keyword match handles most practical retrieval well
• 100x simpler ops (one file, no server)
• < 1ms query latency
• Zero memory overhead beyond the file

Try this first:

import sqlite3
conn = sqlite3.connect("docs.db")
conn.execute("CREATE VIRTUAL TABLE docs USING fts5(title, content)")
# Insert docs, query with MATCH operator

Above 50K documents or when semantic similarity (not keyword) matters, switch to vector DB.

Choosing Between the Three #

Single-node, simple RAG, small team → Qdrant
Need hybrid search (vector + keyword + filters) → Weaviate
Multi-node, billion+ vectors → Milvus
Already have Postgres → pgvector (up to ~1M vectors)
< 10K docs → SQLite FTS5

Recommended Infrastructure #

For vector DB hosting:

• DigitalOcean — $200 credit, droplets with NVMe
• HTStack — Hong Kong VPS for low-latency Asia queries

Affiliate links — same price, supports dibi8.com.

Conclusion #

All three vector DBs are production-ready in 2026. Pick by workload: Qdrant for simplicity, Weaviate for hybrid search, Milvus for billion-scale. Skip them entirely for small corpora — SQLite FTS5 wins on simplicity and is often sufficient.

The real lesson: most teams over-engineer their retrieval layer. Start with the simplest thing that works, upgrade when you measure a real ceiling. Vector DB justifies its complexity only above the simple-tool threshold.

Related: RAG vs Fine-Tuning 2026 Decision Framework · Vector Database Comparison · MCP Servers 2026 Rankings