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Reduce storage costs while maintaining search quality. Remove low-scoring vectors, optimize index size, and keep your Pinecone database performant.

The Problem

Why Vector Compression Matters

Rising Storage Costs

Vector indexes grow unbounded. Low-scoring vectors consume storage and slow down queries without adding value.

Query Performance Degradation

As indexes grow, search latency increases. Larger datasets mean slower nearest-neighbor lookups and higher API costs.

Selective Removal Risk

Deleting vectors is risky. You need a smart approach that removes noise while preserving search quality.

How It Works

The Compression Pipeline

Simple three-step vector compression process

Sample Vectors

Connect to your Pinecone index and sample vectors to analyze compression impact.

client = PineconeCompressionClient( api_key="your-key", index_name="your-index" ) # Estimate compression savings savings = client.estimate_savings() # Sampled 100 vectors from index # Analysis complete

Score & Analyze

Calculate L2 norm for each vector. Low-scoring vectors are identified as candidates for deletion.

# Calculate vector norms (L2) score: 0.945 ████████████████ ← high quality score: 0.672 ███████████ ← medium quality score: 0.156 ██ ← low quality (delete) score: 0.089 █ ← low quality (delete) Deletion threshold: 0.34 Vectors to delete: 261 / 10,000

Delete Low-Scoring

Apply compression with your choice of strategy. Dry-run mode previews impact before committing.

# Preview compression (dry-run) result = client.compress( strategy="balanced", dry_run=True ) ────────────────────────────────── Compression Results ────────────────────────────────── Original vectors: 10,000 Vectors to delete: 261 Final vectors: 9,739 Storage savings: 2.62% Strategy: balanced ──────────────────────────────────
Features

Built for Developers

Pinecone Integration

Native support for Pinecone indexes with simple API client and REST endpoints.

Dry-Run Mode

Preview compression impact before applying changes. See exact storage savings and vector reduction estimates.

L2 Norm Scoring

Score vectors using L2 norm (Euclidean magnitude). Simple, deterministic, and fast to calculate.

Multiple Strategies

Choose between Balanced (default) and Aggressive compression strategies to match your needs.

Simple API

Clean Python client and REST endpoints. Estimate savings, compress, and monitor all from one interface.

Open Source

Self-hosted or use our managed service. Full transparency, no vendor lock-in.