The Storage Problem

Enterprise data volumes are growing 25-30% per year. Cloud storage costs, while declining per GB, are rising in total spend. The average enterprise spends $3-8 million annually on storage infrastructure.

The standard response? Buy more storage, tier cold data, delete what you can. But there's a simpler option: make the data smaller.

Current Compression Falls Short

Most enterprise data is compressed with gzip or LZMA. These are excellent general-purpose algorithms — but they're general-purpose. They don't know that your CSV has timestamp columns, or that your logs follow repeating templates, or that your JSON has a discoverable schema.

That's like using a Swiss Army knife when you need a surgeon's scalpel.

The PZIP Approach

PZIP detects what type of data you have, then applies specialized compression strategies for that specific format. The results are significant:

CSV/Parquet: 20-69% smaller than LZMA — column types, dictionary encoding, delta compression
Log files: 30-48% smaller — template extraction, slot encoding
JSON/JSONL: 25-89% smaller — schema detection, key dictionaries
Office documents: 15-85% smaller — OOXML optimization

Impact at Scale

For a 10 PB data lake:

15% median savings = 1.5 PB fewer stored
~250 fewer 8TB drives
~3,750 kWh/year saved in power
~1.6 metric tons CO2/year reduced

Better compression isn't just efficient — it's a climate action.

No Risk

PZIP's never-worse guarantee means you can deploy with zero risk. If PZIP can't beat your current compression, it outputs your current compression. You literally cannot lose.