Valkey: Sizing your service
This documentation is part of the Vector search and RAG guide. You can view the complete guide here: Semantic search, recommendations, and retrieval-augmented generation with Valkey.
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Vectors are stored in memory, so it is important to anticipate their footprint. The estimate for float32 vectors is:
numberOfVectors x dimensions x 4 bytes, plus about 30 to 50 percent extra for the HNSW graph.
For example, one million 1536-dimension vectors require about 6 GB for the raw vectors. A 20 GB plan is a comfortable starting point. You can reduce memory usage by choosing a model with fewer dimensions: a 768-dimension model uses only half the memory of a 1536-dimension one.
You can check actual memory usage with FT.INFO chunksIndex and with the used_memory metric in your Prometheus monitoring. If you need more memory, you can upgrade your plan at any time from your dashboard.