How Similarity API Matches Records at Scale
Real-world text data is inconsistent — characters are dropped or reordered, formatting varies, and exact matches often fail.
Similarity API uses optimized, NLP-based similarity scoring to compare strings at the character level, allowing records that refer to the same entity to be matched even when written differently.
The API supports both matching within a dataset and matching one dataset against another, and processes data in large batches — handling thousands or millions of records per request with configurable preprocessing and no ongoing maintenance.
Designed for Speed, Control & Trust
Why data teams choose us...
Lightning Fast
A matching engine that scales exponentially better, making real-time deduplication of millions of records practical.
Zero Setup Required
Simple REST API—no clusters, projects, or pipelines to spin up. Copy → paste → fuzzy-match in minutes.
Highly Configurable
Fine-tune thresholds and behavior without the math. Defaults work; knobs are there if you need them.
Flexible, Transparent Pricing
Pay-as-you-go or custom plans—per-row pricing with no hidden fees.
Batteries Included
From data cleaning to match confidence, the heavy lifting is done. You focus on decisions, not wiring.
Security & Privacy by Design
Data stays in memory, encrypted in transit, and is never stored or shared.
1,000× Faster at 1M Rows
We benchmarked Similarity API against common Python fuzzy matching libraries — RapidFuzz, FuzzyWuzzy, and python-Levenshtein — across 10k, 100k, and 1M rows.
Similarity API is:
- 12× faster at 10k rows
- 20× faster at 100k rows
- Up to 1,000× faster at 1M rows
Where local libraries take hours or days, Similarity API completes million-row deduplication in about 7 minutes — with zero infrastructure and no blocking or preprocessing code.
Benchmarks are public and reproducible.