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Qdrant vs Elasticsearch
Trust Score comparison · March 2026
VS
Trust Score Δ
10
🏆 Elasticsearch wins
Signal Comparison
180k / wknpm downloads3.8M / wk
200 commitsCommits (90d)891 commits
21k ★GitHub stars70k ★
2k q'sStack Overflow82k q's
Growing fastCommunityHigh
QdrantElasticsearch
Key Differences
| Factor | Qdrant | Elasticsearch |
|---|---|---|
| License | Apache-2.0 | SSPL / Elastic License |
| Language | TypeScript / Python / Rust | Java |
| Hosted | Self-hosted | Yes |
| Free tier | — | — |
| Open Source | — | ✓ Yes |
| 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 Elasticsearch if…
- You need full-text search with complex filtering and faceting
- You're building a log aggregation pipeline (ELK stack)
- You need near-real-time analytics over large datasets
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);Elasticsearch
from elasticsearch import Elasticsearch
es = Elasticsearch("https://localhost:9200")
# Index a document
es.index(index="tools", id=1, document={
"name": "Elasticsearch",
"category": "search",
"trust_score": 80,
})
# Full-text search
results = es.search(index="tools", body={
"query": {
"multi_match": {
"query": "search analytics",
"fields": ["name", "summary"]
}
}
})Community Verdict
Based on upvoted notes🏆
Elasticsearch wins this comparison
Trust Score 98 vs 88 · 10-point difference
Elasticsearch 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.