SOUNDRAW is an AI Music Generator that allows users to create custom music and beats. It offers an easy way to create music, license music for video, and generate royalty-free music for various projects. Users can choose the mood, genre, and length of the music, and the AI will generate unique songs. The platform also allows users to edit and personalize the songs they create, distribute their music, and earn royalties.
Simply choose the mood, genre, and length of the music you want to create. The AI will generate songs that you can then edit and personalize to fit your specific needs. You can also distribute your music and earn royalties.
Yes, all music generated through SOUNDRAW is royalty-free and safe for commercial use. SOUNDRAW uses original beats created in-house to train its AI model, ensuring that the music is original and free from copyright concerns.
Yes, you can distribute and monetize your songs on platforms like Spotify and Apple Music. SOUNDRAW offers a simple license that allows you to earn 100% of the recording royalties from your songs.
Depending on your plan, you can download music in mp3, wav, and stems formats.
Use these comparison pages to understand the trade-offs between the models most relevant to SOUNDRAW.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.5 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.5 Flash and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.0 Flash Lite and Gemini 2.5 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
