AetherWave AI Album Cover Generator vs CoverArt AI

AetherWave AI Album Cover Generator creates artwork from an uploaded song, using its sound, mood, and genre as guidance. It runs on the web and accepts MP3, WAV, OGG, FLAC, and M4A files up to 50 MB. Eight styles are available: Photorealistic, Abstract, Digital Art, Vintage, Minimal, Surreal, Anime, and Street Art. Users can make covers for albums, EPs, singles, and mixtapes, choosing square or portrait output. Square covers are generated at 1024 × 1024 pixels. The tool can animate artwork into a looping MP4 combined with the uploaded audio, described for Spotify Canvas, Instagram, TikTok, and YouTube. Album art, image downloads, and static MP4 creation are free without an account. Animation requires a free account, with a free default five-second animation. Paid access starts at $4.99/mo. Uploaded audio is used to create the shareable MP4 and is not stored permanently, according to the page. The maker says generated art is yours to use for releases and advises reviewing distribution terms.

CoverArt AI is a web service that creates square AI cover art for songs and albums from release details and selected visual styles. Users can add a mood or concept, choose title and artist placement, and upload a logo or reference image. The style library lists more than 50 curated presets, and artwork exports as JPG at 2048×2048 by default. The service says generated covers include commercial licensing for streaming, merchandise, print, and promotion, and that users own their output. Pro Studio members can request custom presets for label or genre needs. The free plan costs nothing and includes 2 credits per month. Paid plans start with Hobbyist at $6.99 a month for 30 monthly credits; Indie is $19.99 a month, Studio is $39.99 a month, and Records is $99.99 a month. The privacy policy says the service stores account details, prompts and release metadata, generated images, usage data, and limited payment transaction records. It says prompts and generated images are not used to train its models without explicit consent. Security measures listed include TLS in transit, encryption at rest, access controls, and regular reviews.