Knowledge Base Size Calculator

A knowledge base size calculator is a tool that estimates the storage requirements, vector index size, and embedding ingestion costs for AI knowledge bases used in Retrieval-Augmented Generation (RAG) and semantic search pipelines. In a RAG system, documents are split into chunks, each chunk is converted into a high-dimensional vector by an embedding model, and those vectors are stored in a vector database. The knowledge base size calculator online models these steps — chunk count, vector storage in bytes, metadata overhead, monthly growth — and calculates both the initial setup cost and the ongoing storage and re-indexing expenses over your projection horizon.

1. Document Corpus & Chunking: Enter the number of documents and average word count. The calculator converts words to tokens (0.75 words/token), then applies your chunk size and overlap settings to compute the total chunk count. Smaller chunks produce more vectors but enable more granular retrieval.
2. Vector Storage Sizing: Each chunk produces one embedding vector. Vector storage = dimensions × 4 bytes (float32) + ~60 bytes overhead for IDs and pointers. Metadata per chunk (source URL, timestamps, document ID) adds additional storage. The calculator shows the exact per-chunk byte breakdown and total index size.
3. Growth Projection: Set a monthly growth rate (new documents added as a percentage of current corpus). The calculator projects month-by-month how document count, chunk count, index size, storage cost, and new embedding costs evolve over your selected time horizon (1–60 months).
4. Total Cost Summary: The final output combines initial ingestion cost, incremental growth ingestion, scheduled re-index costs, and accumulated vector database storage billing into a single period total — giving you the full knowledge base TCO for your planning horizon.

• Embedding Dimensions: Higher-dimension models (3072d vs 512d) produce 6× larger vectors per chunk. A 10,000-chunk index using text-embedding-3-large requires ~120 MB of raw vector storage vs ~20 MB for voyage-3-lite. Always check whether quality improvements justify the storage increase.
• Chunk Size and Overlap: Halving the chunk size roughly doubles the number of vectors (and storage). Overlap adds token redundancy between adjacent chunks — a 10% overlap on 512-token chunks adds ~5% more vectors. Larger chunks mean fewer vectors but lower retrieval precision.
• HNSW Index Overhead: Most vector databases use HNSW (Hierarchical Navigable Small World) graph indexes for fast ANN search. HNSW adds 25–60% storage overhead on top of raw vector bytes. The calculator shows raw storage — plan for 1.3–1.6× headroom in your infrastructure.
• Metadata Storage: Storing chunk metadata (source URL, document title, creation date, custom tags) adds 100–500 bytes per chunk. For a 1M-chunk knowledge base, 300 bytes of metadata per chunk adds ~300 MB of additional payload storage.

To minimize knowledge base storage costs: first, benchmark your retrieval quality using a smaller-dimension embedding model (768d or 1024d) before defaulting to 1536d or 3072d — many production RAG systems perform equally well with Google's text-embedding-004 at $0.025/1M tokens. Second, avoid aggressive overlap unless you have documented recall problems at chunk boundaries — most knowledge bases perform well with 10–15% overlap. Third, compress or truncate metadata to the minimum required fields. Finally, use a self-hosted vector database (pgvector or Milvus) for large, stable corpora where storage exceeds 10 GB — managed vector database costs grow linearly with size while compute costs remain more predictable with self-hosting.