Facebook FastText Language Identification vs langid.py
Side-by-side — pricing, strengths, tradeoffs, and fit.
Facebook FastText Language Identification
Fast and accurate language identification model using fastText.
langid.py
Stand-alone language identification system for Python.
At a glance
Open SourceFree Tier
· Not enough data·From $0
Open SourceFree Tier
◌ Declining·From $0
Overview
One-liner
One-liner
Fast and accurate language identification model using fastText.
One-liner
Stand-alone language identification system for Python.
Maturity
Maturity
Not enough dataMaturity
DecliningOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Developers working on multilingual applications who need fast and accurate language identification.
Data scientists processing large volumes of text data from various languages.
✓ Best for
Developers working on projects with multilingual text data who need accurate and fast language identification.
Data scientists preprocessing datasets for machine learning tasks involving multiple languages.
Not ideal for
✕ Not for
Projects requiring real-time streaming analysis where latency is critical.
Applications that require language detection for extremely rare or unsupported languages.
✕ Not for
Projects requiring real-time streaming language detection, as langid.py is designed for batch processing.
Applications that require support for a very specific or niche language not covered by the library's supported list.
Strengths
↑ Strengths
High-speed language identification
Efficient use of fastText library for text classification
Wide range of supported languages
↑ Strengths
High accuracy in language identification
Supports over 90 languages
Easy to integrate into Python projects
Weaknesses
↓ Weaknesses
Limited support for less common languages
Performance issues with very short texts
Complex setup and dependency management
↓ Weaknesses
Limited support for non-European languages
Performance issues with large datasets
Lack of active development and updates
Poor documentation for advanced features