Embedding Storage Cost Calculator
Vector storage and embedding cost. Storage is vectors times dimensions times bytes, and quantising from float32 to int8 cuts it by four with a small accuracy cost that is usually worth taking at scale.
Also called: vector database cost, embedding cost calculator.
22.89 GB for 4,000,000 vectors of 1,536 dimensions at float32. Generating them costs $16,000.00 once, and storage runs $4,577.64 a month. Quantising to int8 would use 5.72 GB. This is raw vector storage. An index structure typically adds twenty to fifty percent on top.
How this is calculated
Each vector is dimensions times bytes per component, so a 1,536 dimension float32 vector is about 6 KB. At millions of chunks that becomes substantial, and the index structure typically adds twenty to fifty percent on top of the raw vectors. Quantisation is the main lever: int8 uses a quarter of float32 with a small retrieval accuracy cost that most applications find acceptable. Embedding generation is a one-off cost per chunk and is usually small relative to ongoing storage.
vectors times dimensions times bytes per component, before index overhead- n
- Vector count
- d
- Dimensions
- b
- Bytes per component
Method and limits
What it assumes
- Raw vector storage before index overhead, which is shown as a note.
What it deliberately does not model
- Index structures add twenty to fifty percent depending on the algorithm and parameters.
- Re-embedding after a model change repeats the full generation cost.
- Quantisation accuracy loss depends on the data and should be measured, not assumed.
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Frequently asked questions
- How much does a vector cost to store?
- A 1,536 dimension float32 vector is about 6 KB, so a million of them is roughly 6 GB before index overhead.
- Is quantisation worth it?
- At scale usually. int8 uses a quarter of float32 storage with a small retrieval accuracy cost, which is worth measuring on your own data before committing.