// 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 Quantization | 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 scalar quantization 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
// WORKS WITH EVERY EMBEDDING PROVIDER
Drop-in replacement. Works with any float32 vector.
# pip install pinecone-client import pinecone vectors = embed(documents) # 1536 bytes each index.upsert(vectors) # expensive!
# pip install zerrecodec import zerrecodec as zc vectors = embed(documents) compressed = zc.compress( # 504 bytes each vectors, api_key="zc_your_key" ) index.upsert(compressed) # 3.05x cheaper!
Based on Pinecone/Weaviate storage rates
Join developers already testing ZerreCodec
"Finally a compression that doesn't destroy semantic accuracy. Our RAG pipeline quality stayed identical at 3x lower storage cost."
"We tested against INT8 on 10M OpenAI embeddings. ZerreCodec was 9x more accurate at comparable compression. Impressive."
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