# I Run 5M Vectors on a $6/mo Server. Pinecone Would Charge Me $210. > Source: > Published: 2026-06-14 15:51:30+00:00 Six months ago I moved my RAG pipeline from Pinecone to self-hosted Qdrant. My vector search bill went from $210/month to $6.50/month. Same latency. Same recall. Here's exactly how. The Setup My app does document Q&A for legal contracts. The numbers: 5.2 million vectors (1536-dim, OpenAI embeddings) ~800K queries/month P99 latency requirement: < 50ms On Pinecone Serverless, this cost me roughly $210/month — storage plus read units plus write units for daily ingestion of new documents. What I Moved To A single Hetzner CX32 server: 4 vCPU, 8 GB RAM, 80 GB SSD €8.50/month (about $9.20) Qdrant running in Docker Automated daily backups to S3-compatible storage ($0.50/month) Total: ~$10/month. That's a 95% cost reduction. The Migration Was Easier Than Expected bash# Export from Pinecone (I used their scroll API) python export_pinecone.py --index legal-docs --output vectors.jsonl docker run -d -p 6333:6333 -v ./storage:/qdrant/storage qdrant/qdrant python import_qdrant.py --input vectors.jsonl --collection legal-docs The whole migration took an afternoon. The Qdrant Python client is straightforward, and the API is surprisingly similar to Pinecone's. Performance Comparison I ran the same 10,000 test queries against both setups: MetricPinecone ServerlessQdrant Self-HostedP50 latency23ms4msP99 [latency89ms12msRecall@100.970.97Monthly](mailto:latency89ms12msRecall@100.970.97Monthly) cost$210$10 The self-hosted Qdrant is actually faster because the data sits in memory on the same machine. Pinecone Serverless loads data from object storage on demand, which adds cold-start latency. When Self-Hosting Is a Bad Idea I want to be honest about the trade-offs: Don't self-host if: You have zero DevOps experience and no one on the team does You need 99.99% uptime SLA for enterprise customers Your vector count is growing unpredictably (10M one month, 100M the next) You're a team of 1-2 and every hour on infra is an hour not building product Do self-host if: Your scale is predictable (you know roughly how many vectors you'll have) You're comfortable with Docker and basic server management Cost matters — the difference between $10 and $210 is $2,400/year You want full control over your data and indexing parameters The Cost at Every Scale I built a calculator to compare all four major vector DBs at different scales: ScalePineconeQdrant CloudQdrant Self-HostedSupabase pgvector1M vectors~$22/mo~$14/mo~$7/mo~$27/mo10M vectors~$210/mo~$120/mo~$72/mo~$95/mo100M vectors~$1,900/mo~$950/mo~$480/moN/A 👉 Calculate your exact cost One Thing I Miss About Pinecone The dashboard. Pinecone's web console lets you browse vectors, run test queries, and see index stats visually. With self-hosted Qdrant, I'm using curl and Python scripts. There's a Qdrant Web UI but it's basic. Would I go back? At $200/month savings, absolutely not. But if I were building a quick prototype and didn't want to think about infrastructure, Pinecone's free tier (100K vectors) is genuinely good for getting started. Running self-hosted vector search? I'd love to hear your setup and costs. Also built comparison pages for specific matchups: Pinecone vs Qdrant, Supabase vs Pinecone.