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Vector-database capacity depends on vector dimensions, count, datatype, payload indexes, HNSW, quantization, replication and on-disk settings. Qdrant's production checklist recommends planning sharding from the beginning for horizontal scaling.
Qdrant documentation states CPU/memory needs depend on vector count, dimensions, payloads/indexes, storage, replication and quantization. Memory usage tooling can show RAM, disk and OS page-cache usage by collection.
The application generates embeddings, stores vectors and payloads in Qdrant collections, then uses query embeddings for similarity search.
Beyond vectors, HNSW graphs and payload indexes use memory. Quantization and on-disk options change the footprint.
Qdrant's production checklist recommends enough shards from day one for horizontal scaling. In distributed deployments, snapshots are handled per node.
Add API-key headers and HTTPS as required by your deployment.
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curl -s http://localhost:6333/healthzcurl -s http://localhost:6333/collections | headcurl -s http://localhost:6333/collections/my_collection | headdocker logs --tail=100 qdrant 2>/dev/null || trueCreating snapshots is not enough; restore them to another node or test environment to verify recovery.
It stores embedding/vector data and provides similarity search and filtering for AI/RAG applications.
The vector database layer can generally run on CPU/memory/storage; embedding generation or LLM inference may use GPUs separately.
Qdrant docs allow Docker/Compose in production, but HA, backups, disaster recovery and security remain your responsibility.
Share vector count, dimensions, payload indexes, replication and RAG query volume; we can size RAM/NVMe/nodes.