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Cyanite
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Music Generation (130)

Cyanite Verified Tool

Cyanite is an AI music-intelligence platform for automatic tagging, descriptions, similarity search and catalog discovery across genre, mood, instruments, lyrical themes and tempo. Music businesses should ingest authorized catalogs, verify tags and similarity results, review cultural and genre bias, protect unreleased tracks and metadata, test API workflows, document editorial corrections and retain qualified human curation.

Last Update: August 20, 2026

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Starting price Free + paid plans

Tool Information

Cyanite is an AI music-intelligence platform for automatic tagging, descriptions, similarity search and catalog discovery across genre, mood, instruments, lyrical themes and tempo. Music businesses should ingest authorized catalogs, verify tags and similarity results, review cultural and genre bias, protect unreleased tracks and metadata, test API workflows, document editorial corrections and retain qualified human curation.

Use authorized, minimal inputs and grant integrations the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, governance, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, code, candidate data, customer messages and visual details, preserve originals and version history, and retain accountable human approval before publication, hiring, outreach, deployment or operational action.

A free start route is available with optional paid plans and enterprise integrations. Tracks, analyses, searches, API calls, catalogs, users, storage, support, renewal and taxes vary.

AI output can be inaccurate, biased, derivative, insecure, incomplete or misleading. Review consent, copyright, likeness, commercial rights, training and retention terms, renewals, refunds, platform rules and applicable law. Hiring, education, adult-content, customer-service, marketing, financial and software workflows require qualified human review.

F.A.Q (3)

Cyanite is an AI music-intelligence platform for automatic tagging, descriptions, similarity search and catalog discovery across genre, mood, instruments, lyrical themes and tempo. Music businesses should ingest authorized catalogs, verify tags and similarity results, review cultural and genre bias, protect unreleased tracks and metadata, test API workflows, document editorial corrections and retain qualified human curation.

Use authorized, minimal inputs and grant integrations the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, governance, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, code, candidate data, customer messages and visual details, preserve originals and version history, and retain accountable human approval before publication, hiring, outreach, deployment or operational action.

Verified pricing: Free + paid plans. A free start route is available with optional paid plans and enterprise integrations. Tracks, analyses, searches, API calls, catalogs, users, storage, support, renewal and taxes vary.

Pros and Cons

Pros

  • Automatically tags music catalogs with AI metadata
  • Provides natural-language free-text music search
  • Supports similarity search across a catalog
  • Offers both a web app and an API
  • Integrates with catalog-management platforms
  • Can export tagging results as CSV or Google Sheets
  • Exports are compatible with DISCO; Songspace; and SourceAudio
  • Analyzes characteristics such as mood; genre; BPM; key; and vocals
  • Identifies a representative 30-second segment
  • Lets new users analyze five songs free each month
  • Supports custom enterprise integrations
  • Offers pilot and testing options for startups
  • Provides volume discounts for large catalogs
  • Can reduce repetitive manual catalog tagging
  • Supports selected non-commercial projects through an innovator program
  • Can enrich catalog searchability without manual tagging of every track

Cons

  • The free allowance is limited to five songs per month
  • Self-service subscription prices are not displayed in the official FAQ
  • Business pricing requires contacting sales
  • Enterprise charges can combine API subscription; analysis usage; and search fees
  • Large catalogs may create significant analysis costs
  • Automated tags can be subjective or incorrect
  • Genre and mood taxonomies may not match a team's internal vocabulary
  • Similarity search quality depends on the catalog and audio quality
  • Human review remains necessary for licensing and high-value placements
  • Uploading unreleased music creates confidentiality concerns
  • Rights holders must authorize catalog processing
  • API integration requires development effort
  • Free versions provide limited feature access
  • Reanalysis may be needed after taxonomy or model changes
  • AI metadata should not replace music-supervisor judgment
  • Vendor documentation describes capabilities but not independent accuracy benchmarks

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