The Vector Database Storage Calculator estimates the storage footprint and monthly storage charge for an embedding collection. It counts raw vector values, an index-overhead percentage, and metadata bytes per vector, so the result can be used to size a production RAG or semantic-search store.
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Enter a collection’s expected shape and its storage rate. The output uses decimal GB, dividing bytes by 1,000,000,000, and rounds printed figures to two decimal places.
How to use it
Start with Number of vectors and Embedding dimensions. The initial values are 1,000,000 vectors and 1,536 dimensions. A vector count should reflect chunks or records stored, rather than source documents, when one document produces several embeddings.
Select Precision. The choices are float32 at 4 bytes per dimension, float16 at 2 bytes, and int8 at 1 byte. The default is float32. This field determines the raw bytes used for each dimension.
Check dimensions and precision against the actual embedding model and database configuration before treating the result as capacity.
Enter Index overhead as a percentage of raw-vector bytes. The default is 50%. Then add Metadata per vector in bytes, which begins at 500 bytes, and the storage price in dollars per GB per month, which begins at $0.25.
Read total storage with its raw-vector and index-plus-metadata components. Adjust one field at a time when comparing an embedding-model migration or an indexing setting.
Fields explained
Number of vectors – count of stored embedding records. Default: 1,000,000; step: 1; negative values become 0.
Embedding dimensions – number of numeric coordinates in each vector. Default: 1,536; step: 1.
Precision – storage per dimension. float32 means 4 bytes, float16 means 2 bytes, and int8 means 1 byte. The selected value is used directly in the formula.
Index overhead (%) – index bytes expressed as a percentage of raw-vector bytes. Default: 50%; step: 1 percentage point.
Metadata per vector (bytes) – stored non-vector bytes for each record. Default: 500 bytes; step: 1 byte.
Storage price per GB / month – your monthly dollar rate for one decimal GB. Default: $0.25; step: $0.01.
Reading the results
| Result | Meaning | Action |
|---|---|---|
| Total storage | Raw vectors, index, and metadata combined | Reserve at least this decimal GB amount. |
| Monthly cost | Total storage multiplied by your rate | Compare storage providers or tiers. |
| Raw vectors | Vector count × dimensions × bytes | Compare precision and dimensions. |
| Index + metadata | Graph overhead plus per-record metadata | Inspect schema and index settings. |
Raw vectors are only one portion of the result. The source note says HNSW-style indexes add about 50% to 100% for the graph, while metadata and replication can add more. This UI includes the entered index percentage and metadata, but has no replication field.
One million float32 vectors at 1,536 dimensions occupy 6.14 GB raw.
Warning: the price is applied only to the calculated storage GB. Database request, compute, replication, and transfer charges are outside the formula.
Index plus metadata is printed as a combined value. It is the sum of index bytes and metadata bytes, so it cannot identify which component needs reduction without checking the entered inputs.
The formula
The source calculates rawBytes = vectors × dimensions × bytes, indexBytes = rawBytes × indexOverhead / 100, and metadataBytes = vectors × metadataPerVector. Total bytes are the sum of those three values. It divides each by 1e9 for GB and calculates monthlyCost = totalGB × pricePerGB.
| Built-in default | Code value |
|---|---|
| Vectors | 1,000,000 |
| Dimensions | 1,536 |
| Precision | float32, 4 bytes |
| Index overhead | 50% |
| Metadata | 500 bytes per vector |
| Storage rate | $0.25 per GB/month |
At those defaults, raw bytes are 1,000,000 × 1,536 × 4 = 6,144,000,000, or 6.144 GB. Index bytes are 3.072 GB and metadata is 0.5 GB. Total storage is 9.716 GB and monthly cost is 9.716 × 0.25 = $2.429, displayed as 9.72 GB and $2.43.
Info: selecting int8 changes bytes per dimension from 4 to 1, so raw-vector storage is one quarter of the float32 calculation for the same collection.
All numeric inputs are clamped to zero. An invalid or empty numeric field follows the source’s Number(value) || 0 behavior and contributes zero.
Worked examples
Small document set. Use 100,000 vectors, 768 dimensions, float32, 50% index overhead, 300 metadata bytes, and $0.25/GB. Raw is 0.3072 GB, index is 0.1536 GB, metadata is 0.03 GB, total is 0.4908 GB, and cost is $0.12. Record this baseline before adding replicas.
Default collection. The defaults produce 6.144 GB raw, 3.072 GB index, 0.5 GB metadata, 9.716 GB total, and $2.43 per month. Test the actual index overhead after the collection is built.
Float16 migration. For 5,000,000 vectors at 1,536 dimensions, float16, 75% overhead, 500 metadata bytes, and $0.25/GB, raw is 15.36 GB, index is 11.52 GB, metadata is 2.5 GB, total is 29.38 GB, and cost is $7.35. Validate retrieval quality before committing to the precision change.
Large int8 collection. At 100,000,000 vectors, 1,536 dimensions, int8, 100% overhead, 1,000 metadata bytes, and $0.25/GB, raw is 153.6 GB, index is 153.6 GB, metadata is 100 GB, total is 407.2 GB, and cost is $101.80. Metadata alone accounts for 100 GB in this case. Review payload schema before adding more fields.
Common mistakes
Counting documents rather than vectors understates storage when documents are chunked. Count every embedding record that is written.
Using 1,536 as bytes instead of dimensions inflates a raw-size estimate. Precision supplies the bytes-per-dimension term.
Assuming the total includes replicas or request charges leaves an incomplete budget. Add those provider charges separately because the UI has no such inputs.
Danger: an index overhead of zero removes index bytes from the estimate, even if the deployed database still creates an index.
Use a measured percentage or a conservative HNSW planning value before ordering capacity.
FAQ
Which GB convention does the calculator use?
It divides total bytes by 1e9.
That is decimal GB, rather than binary GiB divided by 1,073,741,824.
Does float16 cut every part of storage in half?
No. It halves raw-vector bytes and the percentage-based index bytes.
Metadata bytes stay unchanged because they are entered separately per vector.
Can I model int8?
Yes. Select int8, which the source maps to one byte per dimension.
For the same count and dimensions, raw bytes are one quarter of float32.
Is replication included?
No. The source note says replication can add more storage, but it is not an input.
Multiply or add a separate allowance for the provider’s replica policy.
Why does metadata matter at scale?
Metadata is multiplied by every vector.
At 100 million vectors and 1,000 bytes each, it is 100 GB before index overhead.
Disclaimer
This is an educational estimate for planning. Results depend on the model, provider, hardware, configuration, and current pricing, all of which change.
The live calculator on the page is the source of truth for its exact fields. Verify against current prices and a small real test before scaling spend.
The metadata calculation here is interesting but I’m concerned about the practical implications when you’re actually running this on consumer hardware. Default 500 bytes per vector seems reasonable for basic stuff, but once you start adding things like doc IDs, chunk positions, source URLs, and timestamps, you’re easily looking at 1.5-2KB per vector. That changes the math significantly. Also, the calculator assumes you can just swap precision levels, but going from float32 to int8 requires quantization during embedding generation, and most APIs (OpenAI, Cohere) only give you float vectors. If you’re using a local model with LLamaIndex to generate embeddings, sure, you can quantize, but then you’re trading accuracy for storage. I tested this with Ollama using Mistral embeddings at int8 versus float16 on my RTX 4090, and the retrieval quality degraded noticeably around 50k vectors. The calculator doesn’t account for that tradeoff. Also missing: replication overhead. If you’re running HNSW indexes on something like Weaviate or Milvus with replication factor of 3, your actual storage footprint triples. The 50% index overhead default feels low for HNSW at scale too—I’ve seen closer to 80-100% in production.
You’ve identified several real constraints that definitely deserve clarification. You’re right that the calculator sits at the intersection of theoretical capacity planning and practical deployment reality, and those don’t always align.
On the metadata point: you’re correct that real-world metadata bloat is significant. The 500-byte default works for minimal scenarios (maybe just an ID and a timestamp), but production RAG systems typically need chunk indices, source references, and sometimes similarity scores or retrieval metadata. A more realistic floor for non-trivial systems is probably 1-2KB as you mentioned. Users should view that field less as ‘average’ and more as ‘your specific schema size’—it’s worth running `SELECT AVG(OCTET_LENGTH(metadata_column)) FROM vectors LIMIT 10000` on an existing collection to ground the number.
On quantization and retrieval quality: that’s a critical practical detail. The calculator treats float32, float16, and int8 as fungible storage choices, but they’re not. Post-training quantization of embeddings does degrade recall, especially for semantic search where the tail of the similarity distribution matters. The degradation you’re seeing around 50k vectors aligns with what we’ve heard from other practitioners—quantization works better when you have a large corpus where approximate neighbors are abundant, and breaks down on smaller, high-precision use cases. Worth noting: some vector databases (Pinecone, Weaviate with AQ) apply lossy compression dynamically rather than at ingest, which is a different tradeoff.
Index overhead is the biggest wildcard. HNSW papers cite 50-100% depending on M and ef_construction parameters. Your observation of 80-100% in production is valuable—that’s likely M=12 or M=16 with high ef_construction. The calculator doesn’t expose those tuning knobs, so users relying on defaults should probably add 20-30% safety margin on top of that 50% baseline.
Have you tested whether HNSW parameter tuning made a measurable difference in your index overhead numbers, or was that fairly consistent across different M values?
Thanks for the detailed response. Yeah, I ran some quick benchmarks on M values—M=4 gave me around 60-70% overhead on my test set, M=12 pushed it to 85-90%, M=16 hit that 100% mark I mentioned. The ef_construction was the real culprit though; pushing it from 200 to 400 nearly doubled the index size. Makes sense theoretically because you’re building a denser graph, but it’s not obvious when you’re first tuning a new collection. Your point about dynamic lossy compression is interesting—I hadn’t looked into Weaviate’s AQ closely. Do you know if that preserves recall better than pre-quantization, or is it roughly equivalent?
Great follow-up data. M=4 to M=16 being 60-70% up to 100% is a clean illustration of why that default 50% can be misleading for real configurations.
On Weaviate’s AQ (Approximate Quantization): it’s a different beast. Standard post-training quantization of embeddings happens offline—you quantize the full-precision vectors down to int8 or similar, and that’s final. AQ works by storing both the quantized version and residual vectors, then performing search in the lower-precision space but re-ranking with the residuals. The recall preservation is noticeably better than naive quantization, though there’s still a small hit (typically 2-5% recall loss vs. 10-15% for pure int8 quantization). The tradeoff is that you don’t save as much storage as you’d think—you’re keeping multiple representations. In practice, if storage is the constraint, pre-quantization is cheaper; if recall is the constraint, AQ is worth the extra bytes.
For your use case with the RTX 4090, you probably have enough VRAM that storage optimization isn’t your bottleneck anyway. The real win is getting ef_construction dialed in so you’re not paying 100% graph overhead for marginal recall gains. Most practitioners find the sweet spot somewhere around ef_construction=300-400 for production, which matches what you’re seeing.