Overview
fastText is an open-source, industrial-strength library developed by Meta AI (formerly Facebook AI Research) designed for efficient text representation and classification. Built on a C++ core with high-performance Python bindings, it differentiates itself from traditional word2vec models by utilizing subword information (character n-grams). This architectural choice allows it to handle morphologically rich languages and generate vectors for out-of-vocabulary words—a critical advantage in 2026's diverse global digital landscape. While large language models (LLMs) dominate complex reasoning, fastText remains the gold standard for high-throughput, low-latency production tasks such as real-time content moderation and language identification, where millisecond response times and minimal compute overhead are mandatory. It supports hierarchical softmax and quantization, enabling the compression of models to fit on mobile and IoT devices without significant loss in precision. Its ability to train on billions of words in minutes on standard CPUs makes it the most cost-effective solution for massive-scale text processing pipelines where the unit economics of GPU-based inference are unsustainable.