Qdrant Vector Search
The Qdrant Vector Search API provides developers with a free open-source solution for storing, filtering, and searching vector embeddings for AI applications.
Developed by Qdrant
Reference for available routes, request structures, and live examples.
Inserts or updates vector embeddings
http://localhost:6333/collections/{name}/pointscurl -X PUT 'http://localhost:6333/collections/{name}/points' \
-H 'Authorization: Bearer YOUR_API_KEY'{
"points": [
{
"id": "point1",
"vector": [
0.1,
0.2,
0.3
],
"payload": {
"city": "Berlin"
}
}
]
}{
"status": "acknowledged",
"operation_id": 123
}- Semantic search over document corporaOptimized Capability
- Real-time AI recommendationsOptimized Capability
- Image/vector similarity searchOptimized Capability
- Hybrid search using metadata + embeddingsOptimized Capability
- LLM context retrieval and RAG workflowsOptimized Capability
- ✓ Open-source with an active, growing community
- ✓ Extremely low latency suitable for real-time applications
- ✓ Supports advanced filtering and hybrid vector/scalar search
- ✓ Seamless integration with popular ML and data engineering workflows
- ✓ Comprehensive language SDKs and detailed documentation
- ✗ Initial vector data preparation is necessary
- ✗ Scaling for large datasets may require managed cloud hosting
- ✗ Niche focus on vector search, not general-purpose data storage
- ✗ Hybrid query syntax may require learning curve for newcomers
- ✗ Limited support for relational or transactional operations
FAQs
API Specifications
v1Under 10 minutes for cloud or Docker setup.
1,000 requests/minute (free tier)
Up to 1 million vectors, 100 MB storage, 1,000 requests/minute
Use Case: Best For
AI/ML developers needing scalable vector search for embeddings, semantic search applications, LLM-powered tools, and real-time recommendations.
Not Recommended For
Relational or transactional workloads, projects needing only traditional scalar search, or applications that don’t use vector embeddings.
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