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Qdrant vs Pinecone
Trust Score comparison · March 2026
Signal Comparison
180k / wknpm downloads680k / wk
200 commitsCommits (90d)38 commits
21k ★GitHub stars2.1k ★
2k q'sStack Overflow1.2k q's
Growing fastCommunityMedium
QdrantPinecone
Key Differences
| Factor | Qdrant | Pinecone |
|---|---|---|
| License | Apache-2.0 | Proprietary |
| Language | TypeScript / Python / Rust | Python / TypeScript |
| Hosted | Self-hosted | Yes |
| Free tier | — | — |
| Open Source | — | — |
| TypeScript | ✓ | ✓ |
Pick Qdrant if…
- Performance-critical vector search at scale
- You want the fastest self-hostable vector DB
- Memory-efficient workloads with millions of vectors
Pick Pinecone if…
- You want a managed vector DB with zero ops overhead
- You're building RAG and need fast semantic search
- You need serverless scaling with pay-per-use pricing
Side-by-side Quick Start
Qdrant
import { QdrantClient } from '@qdrant/js-client-rest';
const client = new QdrantClient({ url: process.env.QDRANT_URL, apiKey: process.env.QDRANT_API_KEY });
const results = await client.search('my_collection', {
vector: [0.1, 0.2, 0.3, ...],
limit: 5,
});
console.log(results);Pinecone
from pinecone import Pinecone
pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
index = pc.Index('my-index')
index.upsert(vectors=[{
'id': 'doc-1',
'values': embedding,
'metadata': { 'text': 'Source document text' },
}])
results = index.query(
vector=query_embedding, top_k=5, include_metadata=True
)Community Verdict
Based on upvoted notes🏆
Qdrant wins this comparison
Trust Score 88 vs 64 · 24-point difference
Qdrant leads on Trust Score with stronger signal data across downloads and community health. That said, the other tool is worth considering if your use case matches its specific strengths above.