AI infrastructure costs scale with data volume. Training data, model checkpoints, inference logs — it compounds fast.
RNDA eliminates the storage layer entirely. Queries run on signatures — no decompression, no raw data, no storage bill that grows with your models.
Request an Enterprise AI POC →The Problem
OpenAI projects $129 billion in infrastructure costs over 3 years. The primary driver: every AI system assumes data must be stored at rest and decompressed before compute. Retrieval-augmented generation (RAG) systems are bottlenecked by this decompression step at scale.
How RNDA Solves It
Eliminate the decompression bottleneck
RNDA signatures ARE the queryable form. No decompression step before semantic search. Query latency stays flat as the dataset scales.
Drop-in replacement for vector stores
RNDA signatures are semantically meaningful vectors. Replace your embedding store with a signature store that carries no raw data.
Storage costs proportional to signature count, not data volume
A petabyte of training data becomes a few gigabytes of signatures. Storage costs crater regardless of data volume.
How RNDA Applies
Storage Elimination
Training datasets, model checkpoints, and experiment logs compressed up to 140,835x across data types. A 1 PB AI data lake reduces its storage bill from $276K/year to ~$55/year for the compressed portion — turning petabyte-scale AI infrastructure costs into manageable line items.
Privacy Protection
Personally identifiable training data — user behavior, health records, financial transactions — is encoded at the storage layer. Compliant AI training without stripping signal from sensitive datasets. PII is gone; the statistical patterns that make the data useful remain.
Compliance Management
Auditable lineage of training data versions enables organizations to meet model governance requirements under the EU AI Act and NIST AI RMF. Compressed archives are auditable without being readable — compliance and IP protection simultaneously.
Intelligent Retrieval
Semantic search over compressed training corpora and experiment logs enables data scientists to find relevant prior datasets and evaluation results without full decompression. No decompression bottleneck before semantic search — query latency stays flat as the dataset scales.
Collaborative Intelligence
Distributed ML teams across regions access and version shared training datasets without replicating petabytes to each location. Compressed signatures enable cross-institutional collaboration on AI development without raw data transfer or data residency violations.
Storage Impact
Industry stat: Enterprise AI data lakes range from 100–10,000 TB; storage becomes a primary cost driver at petabyte scale as retraining cycles compound data volume (StoneFly / Akave)
1,000 TB × 20% × $276/TB ÷ 1,000x compression (conservative estimate for AI training data)
1 PB AI data lake saves ~$55,000/year — up to 140,835x compression demonstrated across 30+ data types
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Real data across all domains
What Becomes Possible
"A RAG pipeline that currently decompresses 10GB of documents per query runs the same queries on RNDA signatures. The decompression step is eliminated. Query latency drops. Storage costs drop by the compression ratio."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
Request an Enterprise AI POC →