Qdrant vs Elasticsearch
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Qdrant takes the lead.
Both Qdrant and Elasticsearch are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Qdrant:
- Qdrant ranks higher overall
- Qdrant has more permissive licensing
- Elasticsearch has 2 more strengths
Qdrant
Qdrant is an open-source vector database available as both a managed cloud service and a self-hosted solution. It offers strong HNSW performance, flexible deployment, and predictable cost structures, making it suitable for both startups and large-scale RAG workloads.
Elasticsearch
Elasticsearch is one of Europe's most widely deployed open-source search engines that includes native vector database capabilities. It combines dense vector search with traditional full-text BM25 keyword search for powerful hybrid retrieval, making it ideal for RAG applications that need both semantic and lexical search capabilities.
Feature Comparison
Infrastructure & Technical Details
| Feature | Qdrant | Elasticsearch |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Self-Hosted, Managed Cloud, Serverless |
| Cost | Starts ~$0.014/hour for smallest node | Serverless: usage-based (ECU); Hosted: starts ~$95/month; Self-hosted: free (infra cost only) |
| License | Apache 2.0 | AGPL v3 / SSPL / Elastic License 2.0 |
| Index Types | HNSW, Sparse (dot similarity) | HNSW, int8_hnsw, int4_hnsw, bbq_hnsw, Flat |
| Cloud Providers | AWS, Azure, GCP | AWS, Azure, GCP, Alibaba Cloud |
| Regional Flexibility | high | high |
| Strengths | 10 | 12 |
| Weaknesses | 5 | 8 |