Cyanite is commonly used for automated music tagging and characteristic-based classification, with search and similarity built around descriptive labels. MusicAtlas can surface characteristics too, but is designed as search infrastructure for commercially released music at scale — combining multiple models and representations across audio, lyrics, and context. This page explains how those approaches differ, and why teams evaluating music search often arrive at different conclusions.
Automated extraction of musical characteristics such as mood, instrumentation, genre, and energy.
Tagging and classification workflows that enrich catalogs with structured descriptors.
Filtering and faceting libraries based on assigned characteristics (browse, filter, refine).
Labels and publishers working in commercially released music — where the broader recorded-music landscape acts like the “internet” for discovery, reference matching, and relevance.
Music search infrastructure at scale built on multi-model analysis and multi-representation indexing across audio, lyrics, and context.
Intent-driven discovery using multiple entry points (reference tracks, lyric themes, moods, use-cases, and natural-language prompts).
A web-scale index of recorded music, designed to operate as an intelligence layer across products and workflows.
Core approach: Cyanite approaches music understanding through characteristics and tags.
MusicAtlas approaches music understanding through search and ranking, where characteristics are one signal among many rather than the primary interface.
Role of tags: In Cyanite, tags are the main output and the primary way teams explore music.
In MusicAtlas, tags can help inform relevance, but discovery is driven by queries, reference tracks, lyric meaning, and ranked results across multiple representations.
Similarity: Cyanite supports similarity, often alongside or through characteristic spaces.
MusicAtlas treats similarity as one capability among many, combining multiple models and representations rather than relying on a single similarity space.
Search behavior: Cyanite workflows often center on filtering and browsing by descriptors.
MusicAtlas is designed for direct search, where users ask for music and receive ranked answers without needing to navigate label hierarchies.
Index design: Cyanite is commonly evaluated as an enrichment layer that produces characteristic metadata.
MusicAtlas is built as a multi-representation index across audio, lyrics, and context — designed to improve relevance as it scales and as usage signals accumulate.
Catalog context: Cyanite is often used to organize and describe a library.
MusicAtlas is built around commercially released music — the broader recorded-music landscape that labels and publishers reference every day. That wider corpus acts as the foundation for relevance, context, and discovery, not just the boundaries of a single catalog.
Developer access: Cyanite is typically used as a tagging and enrichment service.
MusicAtlas provides an open developer API focused on track-level intelligence, enabling search, similarity, and ranking workflows that integrate into existing systems.
Both systems analyze audio and can surface musically related tracks. The difference lies in how that analysis is used: Cyanite emphasizes characteristic extraction and classification, while MusicAtlas emphasizes search, ranking, and discovery directly from user intent across audio, lyrics, and context.
Cyanite is a strong system for automated music tagging and characteristic-based classification. MusicAtlas is search infrastructure for commercially released music at scale, built on multi-model analysis and a multi-representation index across audio, lyrics, and context. In practice, teams evaluating Cyanite are often looking to label and organize catalogs, while teams evaluating MusicAtlas are looking for a discovery and search layer that returns ranked answers and improves relevance over time.