Estimate storage requirements for vector databases like Pinecone, Weaviate, Qdrant, and Milvus. Plan capacity for embeddings with index overhead and metadata calculations.
Quick Presets
HNSW (Hierarchical Navigable Small World)
Hierarchical Navigable Small World graph. Fast approximate search, higher memory.
O(log n)Quality: approximate| Model | Dimensions | Provider |
|---|---|---|
| OpenAI text-embedding-3-small | 1536 | OpenAI |
| OpenAI text-embedding-3-large | 3072 | OpenAI |
| OpenAI text-embedding-ada-002 | 1536 | OpenAI |
| Cohere embed-english-v3.0 | 1024 | Cohere |
| Cohere embed-multilingual-v3.0 | 1024 | Cohere |
| Voyage voyage-large-2 | 1536 | Voyage |
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Vector databases power modern AI applications from semantic search to RAG systems. But estimating storage requirements isn't straightforward—you need to account for raw vector data, index overhead, and metadata. This calculator helps you plan capacity across popular vector databases.
Vector databases store high-dimensional embeddings and enable similarity search. Storage requirements depend on vector count, dimensions, index type, and precision. Unlike traditional databases, vector DBs often need significant memory for fast retrieval.
Storage Formula
Storage = Vectors × Dimensions × Bytes per Value × Index Overhead + MetadataVector database pricing often scales with storage. Knowing your requirements helps budget accurately for cloud services.
Different index types have different memory/speed tradeoffs. HNSW uses 2-4x more memory than flat but offers faster search.
Most vector DBs need indexes in RAM for fast queries. Underestimating causes performance problems.
Compare costs across Pinecone, Weaviate, Qdrant, Milvus, and others based on your actual storage needs.
Plan for growth. Know when you'll need to upgrade tiers or add nodes.
Retrieval-Augmented Generation stores document chunks as vectors. A 100K document corpus might have 1M+ chunks.
Product catalogs, knowledge bases, and FAQ systems. Storage scales with catalog size.
Visual search and recommendations. Image embeddings are typically 512-2048 dimensions.
User and item embeddings for personalization. Often millions of vectors.
Store normal patterns and detect outliers. Industrial and security applications.
Combined text, image, and audio embeddings. CLIP models enable cross-modal retrieval.
HNSW offers the best speed/accuracy tradeoff for most use cases. Use Flat for small datasets (<100K) or when you need exact results. IVF works well for very large datasets. PQ sacrifices accuracy for massive compression.