Vast.ai
generalRent GPUs for AI, machine learning, and rendering
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.
Rent GPUs for AI, machine learning, and rendering
AI alt text generator for images
Voice generation and cloning from text
Turn text into memes with AI
Search and analyze video with AI
Make AI-generated text sound natural and human-like
Publish 50+ SEO-optimized articles monthly with full automation
Transform AI-generated content to read more naturally
Build a blog or website in minutes
News monitoring and trend analytics for business teams
Voice and audio to structured notes
Generate quizzes from text, video, or audio content
Free AI image and audio tools without signup
Convert PDFs into interactive courses with adaptive quizzes
Organize and search technical documentation across teams
Software deals for entrepreneurs
Makes AI-generated text read like human writing
Make AI-generated text read naturally
Text humanization for AI-generated content
Turn articles into professionally edited video
Convert YouTube videos to blog posts
Rephrase text quickly and clearly
Auto-generate podcast summaries
Write product descriptions, ad copy, and blog outlines
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.