Qdrant vs Elasticsearch

Compare deployment options, cost efficiency, and features to choose the right vector database for your application. If you want to compare these models on your data, try Agentset.

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

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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.

• Deployment: Self-Hosted, Managed Cloud
• Cost: Starts ~$0.014/hour for smallest node
• License: Apache 2.0

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.

• Deployment: Self-Hosted, Managed Cloud, Serverless
• Cost: Serverless: usage-based (ECU); Hosted: starts ~$95/month; Self-hosted: free (infra cost only)
• License: AGPL v3 / SSPL / Elastic License 2.0

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Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.

Feature Comparison

Infrastructure & Technical Details