Embedding Storage Cost Calculator
Calculate vector storage costs for RAG systems from document count and embedding dimensions. Enter values for instant results with step-by-step formulas.
Reviewed by Daniel Agrici, Founder & Lead Developer
Formula
Storage = Vectors * (Dimensions * 4 + Metadata) * Index_Overhead * Replicas
Where Vectors = Documents * Chunks_per_doc, each dimension uses 4 bytes (float32), Metadata averages ~500 bytes, and HNSW index overhead is approximately 1.5x. Embedding cost = (Total_tokens / 1M) * model_price_per_1M_tokens.
Worked Examples
Example 1: Small Knowledge Base RAG System
Problem:A startup has 5,000 documents averaging 8 chunks each, using OpenAI text-embedding-3-small (1536 dims) with Pinecone serverless.
Solution:Total vectors = 5,000 * 8 = 40,000\nEmbedding tokens = 40,000 * 250 = 10,000,000\nEmbedding cost = (10/1) * $0.02 = $0.20\nStorage per vector = 1536 * 4 = 6,144 bytes\nTotal storage = 40,000 * (6,144 + 500) * 1.5 = 398 MB\nPinecone serverless = 40,000 * $0.000002 + 0.39 * $0.33 = $0.21/month
Result:Embedding: $0.20 (one-time) | Storage: ~398 MB | Monthly: ~$0.21
Example 2: Enterprise Document Search Platform
Problem:100,000 documents, 15 chunks each, OpenAI large embeddings (3072 dims), Weaviate dedicated, 2 replicas.
Solution:Total vectors = 100,000 * 15 = 1,500,000\nEmbedding tokens = 1,500,000 * 250 = 375,000,000\nEmbedding cost = 375 * $0.13 = $48.75\nStorage per vector = 3072 * 4 = 12,288 bytes\nTotal storage = 1,500,000 * (12,288 + 500) * 1.5 * 2 = 57.5 GB\nWeaviate dedicated = ~$500/month
Result:Embedding: $48.75 (one-time) | Storage: ~57.5 GB | Monthly: ~$500
Frequently Asked Questions
What are vector embeddings and why do they need storage?
Vector embeddings are numerical representations of text, images, or other data that capture semantic meaning as arrays of floating-point numbers. When you embed a text chunk, a model like OpenAI text-embedding-3-small converts it into a 1536-dimensional vector where each dimension represents some learned feature of the content. Semantically similar texts produce vectors that are close together in this high-dimensional space, enabling similarity search. These vectors need specialized storage because traditional databases are not optimized for nearest-neighbor search across hundreds or thousands of dimensions. Vector databases like Pinecone, Weaviate, and Qdrant use specialized indexing algorithms like HNSW (Hierarchical Navigable Small World) graphs to enable fast approximate nearest-neighbor search. The storage cost depends on the number of vectors, their dimensionality, associated metadata, and the index overhead required for efficient retrieval.
How does embedding dimension affect storage costs and performance?
Embedding dimension directly impacts storage costs because each dimension requires 4 bytes (float32) of storage. A 1536-dimension vector occupies 6,144 bytes (6 KB), while a 3072-dimension vector takes 12,288 bytes (12 KB). For one million vectors, this difference translates to approximately 6 GB versus 12 GB of raw storage before index overhead. Higher dimensions generally capture more semantic nuance and produce better retrieval quality, but they also increase compute time for similarity calculations and require more RAM for in-memory indexes. Many modern embedding models offer dimension reduction options where you can use fewer dimensions with only marginal quality loss. For example, OpenAI text-embedding-3-small supports outputting lower dimensions. The optimal choice balances retrieval quality against cost and latency requirements for your specific use case.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy