Embedding Dimension Storage Calculator
Compare vector storage at float32, float16 and int8.
Results update automatically as you type.
Result
Storage by vector precision
1,000,000 vectors x 1,536 dims
| Precision | Bytes/dim | Size (GB) | Monthly |
|---|---|---|---|
| float32 | 4 | 8.01 GB | $2.00 |
| float16 | 2 | 4.01 GB | $1.00 |
| int8 | 1 | 2.00 GB | $0.5007 |
Quantizing vectors can slash storage. Compare the size and monthly cost of your vector set at float32, float16 and int8 precision.
How this is calculated
This calculator uses the rag formulas described in our methodology. All figures are estimates for planning; verify current pricing with each provider before relying on them.
Worked example (defaults)
With the default inputs above, here is the result:
Result
Storage by vector precision
1,000,000 vectors x 1,536 dims
| Precision | Bytes/dim | Size (GB) | Monthly |
|---|---|---|---|
| float32 | 4 | 8.01 GB | $2.00 |
| float16 | 2 | 4.01 GB | $1.00 |
| int8 | 1 | 2.00 GB | $0.5007 |
Frequently asked questions
Does quantization hurt accuracy?+
Float16 is usually lossless for retrieval; int8 trades a little accuracy for big storage savings. Test on your data.
What's the overhead factor?+
Indexes (like HNSW) add roughly 40% on top of raw vector bytes, which this includes.