I Run 5M Vectors on a $6/mo Server. Pinecone Would Charge Me $210.

A developer migrated a 5.2-million-vector RAG pipeline from Pinecone Serverless to self-hosted Qdrant on a Hetzner CX32 server, reducing monthly costs from $210 to $10 while achieving lower latency (P99 12ms vs 89ms) and identical recall. The migration took an afternoon, and the developer provides a cost comparison across scales, noting that self-hosting is suitable for predictable workloads and teams comfortable with Docker.

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.