Riffusion
GeneralGenerative AI for creating and remixing music
This category covers tools built around large language models: infrastructure for deploying, fine-tuning, evaluating, and monitoring LLMs in production. With 369 tools listed, it is one of the more technical categories on the site, aimed primarily at developers and ML engineers rather than end users.
Generative AI for creating and remixing music
Search and analyze video with AI
News monitoring and trend analytics for business teams
Turn text into short-form video
Create videos from text prompts
Writing assistant for authors and novelists
Upscale photos and create images from descriptions
Convert PDFs into interactive courses with adaptive quizzes
Launch, automate, and scale Meta ads from one dashboard
Convert long-form content into short social videos
Convert YouTube videos to blog posts
Generate sound effects with AI
Convert articles and text to video
Extract text from images and scanned docs
Online gaming platform
Generate videos with customizable templates
Rewrite AI text to pass human detection
AI text generation in Spanish
Chat with custom AI characters and roleplay scenarios
Verify claims against reliable sources
Free tools for PDF, images, YouTube, and online utilities
Send SMS reminders before meetings to reduce no-shows
Free custom text wallpapers for devices and servers
Convert audio and video to text or subtitles, with summaries
LLM tooling has exploded alongside the models themselves, and the category now spans several distinct problem areas. Deployment and serving tools like PeriFlow and Dstack help teams run models efficiently at scale. Evaluation and observability tools like UpTrain and AIWatch track model quality, drift, and cost over time. Memory and retrieval tools like Cognee add persistent context or RAG capabilities to LLM applications. When choosing, the key questions are infrastructure fit (cloud, on-prem, or hybrid), model compatibility (OpenAI-only vs. open-weight models), and whether the tool addresses your actual bottleneck, whether that is latency, cost, accuracy, or developer velocity. Pricing structures vary: some tools charge per token processed, others per seat or per API call. Open-source options exist across most sub-categories, which is worth considering for teams with engineering capacity to self-host.