// CASCADED RESIDUAL SCALAR QUANTIZATION //
Vector compression delivering 3.05× compression with only 0.000247% quality loss. Outperforms INT8 baseline by 9× — slash your vector database costs without sacrificing accuracy.
Built for production AI systems at scale
CRSQ 2-cascade preserves 99.9998% cosine similarity. 9× better than INT8 — semantic search stays accurate.
1536 bytes → 504 bytes per vector. For 1 billion vectors, 1TB savings. Pinecone, Weaviate, Qdrant costs drop immediately.
Algorithm never leaves our servers. You get results, not source code.
Works with OpenAI, Cohere, HuggingFace. Any dimension. Drop-in — 2 lines of code.
Benchmarked on modern hardware. Compress entire embedding databases in minutes.
Independent validation confirmed all claims. Zenodo preprint available. 197,742 vectors verified.
Independent test · 10,000 vectors · 384-dim float32
| METHOD | COMPRESSION | COS SIMILARITY | QUALITY LOSS | % > 0.9999 | BYTES/VECTOR |
|---|---|---|---|---|---|
| ⚡ ZERRECODEC CRSQ | 3.05× | 0.999998 | 0.000247% | 100% | 504 B |
| INT8 Scalar (Qdrant/Weaviate) | 4.00× | 0.999978 | 0.002215% | 100% | 392 B |
| Binary Quantization | 32.0× | ~0.950 | ~5.0% | ~60% | 192 B |
| Float32 (No Compression) | 1.00× | 1.000000 | 0% | 100% | 1536 B |
* CRSQ achieves 9× lower quality loss than INT8 at comparable compression ratio.
Test CRSQ in real-time — no signup required
Scale from prototype to production
Perfect for testing and small projects
For production AI applications
For large-scale deployments