Everyone's building RAG systems now, which means everyone needs a vector database. The problem? Nine different vendors all claim to be the fastest, and their benchmarks are about as reliable as weather predictions.
I spent a month testing six databases with 10M embeddings from real customer support conversations. Here's what actually happened, not what the marketing says.
What Nobody Tells You About "Enterprise Ready"
Qdrant talks a big game about performance. They're right - it's fast as hell. But their Kubernetes operator crashed twice during my testing, and their documentation assumes you already know HNSW inside and out. Good luck if you're just trying to get something working by Friday.
Pinecone markets itself as "just works" enterprise solution. It does work, until you get the bill. My test dataset cost $847 for one month. For a fucking proof of concept. Their index creation took 45 minutes for what Milvus handled in 8.
Milvus is the fastest at building indexes - I'll give them that. But version 2.3.1 has a memory leak that'll eat your entire cluster. Learned that one at 3am when our staging environment ran out of RAM. Their architecture docs explain why it uses so much memory, but good luck tuning it properly without breaking something. Use 2.3.0 or wait for 2.4.0.
The Performance Reality Check
Forget the benchmarks. Here's what matters:
Your data isn't their test data. Most benchmarks use clean, perfectly distributed vectors. My customer support embeddings? Clustered as hell, with massive outliers that break HNSW graphs. Only Qdrant handled this gracefully.
Concurrent users kill everything. All those "50,000 QPS" claims? That's single-threaded. Add 100 concurrent users and watch latency go to shit. MongoDB Vector Search went from 25ms to 3.2 seconds under load.
Filtering is where dreams go to die. You want to filter by user_id or date? Kiss your performance goodbye. Most systems either post-filter (losing results) or pre-filter (scanning everything). Chroma straight up crashed when I filtered 80% of vectors due to SQLite corruption issues. Only Qdrant's query planner handles this properly.
The Hidden Costs That'll Wreck Your Budget
Pinecone: Started at $70/month, ended at $847. Data transfer fees are insane if you're not on AWS us-east-1.
Weaviate: "Free" until you need backup. Then it's $500/month for decent disaster recovery.
Milvus on EKS: The database is free. The 8 EC2 instances it needs for high availability? $1,200/month.
Qdrant Cloud: Actually reasonable pricing, but their free tier is a joke. 1GB storage? That's like 50,000 vectors.
What Actually Works in Production
After four weekends of pain:
- Qdrant if you can handle the operational complexity and have engineers who know what they're doing
- Chroma for prototyping, but don't even think about production scale
- MongoDB if you already run MongoDB and don't need blazing speed
- Pinecone if your company doesn't care about burning money for convenience
The rest? Wait six months and see if they fix their shit.
The One Thing Every Benchmark Misses
None of them test with real failure scenarios. What happens when your index gets corrupted? How long does disaster recovery take? Can you actually migrate your data out if you want to switch?
I found out the hard way when Chroma's storage got corrupted during our load test. Seven hours to rebuild the index from scratch. Meanwhile, Qdrant's snapshot restore took 12 minutes.
Choose accordingly.
