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

Start Qdrant

docker run -d -p 6333:6333 -v ./storage:/qdrant/storage qdrant/qdrant

Import

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 [email protected] 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.