{
    "name": "Vector Database Cost Calculator",
    "url": "https://calculators.geekmidst.com/calculators/vector-database-cost-calculator",
    "markdown": "https://calculators.geekmidst.com/calculators/vector-database-cost-calculator.md",
    "category": "Software, cloud & AI",
    "description": "Estimate vector database size and monthly cost from vector count, dimensions, precision, metadata, index overhead, replicas, storage and query prices.",
    "summary": "Each vector takes dimensions × bytes per dimension, plus metadata. Add index overhead and replicas to get stored size, then price storage, queries and writes.",
    "also_called": [
        "Embedding storage calculator",
        "Vector index size calculator"
    ],
    "applies_to": "Worldwide",
    "last_updated": "2026-10-29",
    "computation": "Results are computed in the visitor’s browser on the HTML page. This API describes the calculator; it does not compute results.",
    "inputs": [
        {
            "name": "vectors",
            "label": "Number of vectors",
            "type": "number",
            "default": "10000000"
        },
        {
            "name": "dimensions",
            "label": "Dimensions",
            "type": "number",
            "default": "768"
        },
        {
            "name": "precision",
            "label": "Precision",
            "type": "select",
            "default": "float32",
            "options": {
                "float32": "float32 (4 bytes)",
                "float16": "float16 (2 bytes)",
                "int8": "int8 (1 byte)",
                "binary": "binary (1 bit)"
            }
        },
        {
            "name": "metadata_bytes",
            "label": "Metadata per vector",
            "type": "number",
            "default": "200",
            "unit": "bytes",
            "optional": true
        },
        {
            "name": "index_overhead",
            "label": "Index overhead",
            "type": "number",
            "default": "50",
            "unit": "%",
            "optional": true
        },
        {
            "name": "replicas",
            "label": "Copies stored",
            "type": "number",
            "default": "1"
        },
        {
            "name": "storage_price",
            "label": "Price per GB-month",
            "type": "number",
            "default": "0.25"
        },
        {
            "name": "queries_m",
            "label": "Queries per month",
            "type": "number",
            "default": "5",
            "unit": "million",
            "optional": true
        },
        {
            "name": "query_price",
            "label": "Price per million queries",
            "type": "number",
            "default": "2.00",
            "optional": true
        },
        {
            "name": "writes_m",
            "label": "Writes per month",
            "type": "number",
            "default": "1",
            "unit": "million",
            "optional": true
        },
        {
            "name": "write_price",
            "label": "Price per million writes",
            "type": "number",
            "default": "1.00",
            "optional": true
        },
        {
            "name": "currency",
            "label": "Currency",
            "type": "currency",
            "default": "USD"
        }
    ],
    "outputs": [
        {
            "key": "estimated_cost",
            "label": "Monthly cost",
            "format": "currency"
        },
        {
            "key": "raw_gb",
            "label": "Raw vectors and metadata",
            "format": "number"
        },
        {
            "key": "stored_gb",
            "label": "Stored size",
            "format": "number"
        },
        {
            "key": "stored_gib",
            "label": "Stored size in binary units",
            "format": "number"
        },
        {
            "key": "storage_cost",
            "label": "Storage",
            "format": "currency"
        },
        {
            "key": "query_cost",
            "label": "Queries",
            "format": "currency"
        },
        {
            "key": "write_cost",
            "label": "Writes",
            "format": "currency"
        }
    ],
    "formula": {
        "text": "Raw bytes = Vectors × (Dimensions × Bytes per dimension + Metadata);  Stored = Raw × (1 + Overhead%) × Copies;  Cost = Stored GB × Price + Queries × Query price + Writes × Write price",
        "variables": {
            "Bytes per dimension": "4 (float32), 2 (float16), 1 (int8), 0.125 (binary)",
            "Copies": "primary plus replicas"
        }
    },
    "worked_example": {
        "inputs": {
            "vectors": "10000000",
            "dimensions": "768",
            "precision": "float32",
            "metadata_bytes": "200",
            "index_overhead": "50",
            "replicas": "1",
            "storage_price": "0.25",
            "queries_m": "5",
            "query_price": "2.00",
            "writes_m": "1",
            "write_price": "1.00",
            "currency": "USD"
        },
        "steps": [
            "Per vector: 768 × 4 + 200 = 3,272 bytes; × 10 million = 32.72 GB.",
            "With 50% index overhead: 49.08 GB × 0.25 = 12.27.",
            "Queries 5 × 2.00 = 10; writes 1 × 1.00 = 1; total 23.27."
        ],
        "result": "Ten million 768-dimension float32 vectors need about 49 GB with index overhead, about 23.27 a month at these example prices."
    },
    "interpretation": "Dimensions and precision drive size. Quantising float32 to int8 cuts raw vector storage by 75%, usually at a small cost in recall; check the trade-off on your own queries.",
    "assumptions": [
        "All vectors have the same dimensions and average metadata size.",
        "Index overhead applies to the whole stored record."
    ],
    "limitations": [
        "Many managed services price by pods, nodes or read and write units instead of GB; convert your plan to these terms or enter an equivalent rate.",
        "Memory-resident indexes may need RAM sized to the index, which is priced differently from disk."
    ],
    "rules": null,
    "faqs": [
        {
            "question": "How big is one embedding?",
            "answer": "Dimensions × bytes per value. A 768-dimension float32 vector is 3,072 bytes; a 1,536-dimension one is 6,144 bytes."
        },
        {
            "question": "Does embedding generation cost go here?",
            "answer": "Not directly. Creating embeddings is a one-off token cost per document; use the LLM Token Cost Calculator with your embedding model’s price."
        }
    ],
    "sources": [],
    "related": [
        {
            "name": "LLM Token Cost Calculator",
            "url": "https://calculators.geekmidst.com/calculators/llm-token-cost-calculator"
        },
        {
            "name": "AI Chatbot Cost Forecast Calculator",
            "url": "https://calculators.geekmidst.com/calculators/ai-chatbot-cost-forecast-calculator"
        },
        {
            "name": "Cloud Storage Cost Calculator",
            "url": "https://calculators.geekmidst.com/calculators/cloud-storage-monthly-cost-calculator"
        },
        {
            "name": "Database Storage Growth Calculator",
            "url": "https://calculators.geekmidst.com/calculators/database-storage-growth-calculator"
        }
    ],
    "disclaimer": null,
    "publisher": "GM¹X CalcVerse"
}