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
Rent GPUs for AI, machine learning, and rendering
Turn text prompts into AI-generated videos
AI alt text generator for images
Voice generation and cloning from text
App translation with full context and human review
Convert AI text to natural, human-like writing
Portfolio platform with galleries and zero-commission sales
Generate Excel formulas and analyze spreadsheets without coding
Long-form writing editor for storytellers
Turn text into memes with AI
Open-source rich text editor framework with extensions
Convert video to 3D animation instantly
Search and analyze video with AI
Free AI sound effect generator from text
Generate unique text for blogs, marketing, and writing projects
AI-powered ad creation with optimization for conversions
AI regex generator and tester
Generate short videos from text prompts
Make AI-generated text sound natural and human-like
Publish 50+ SEO-optimized articles monthly with full automation
Extract text from images, PDFs, and handwritten notes
Transform AI-generated content to read more naturally
Build a blog or website in minutes
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.