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
Turn text prompts into AI-generated videos
Voice generation and cloning from text
App translation with full context and human review
Convert AI text to natural, human-like writing
Generate Excel formulas and analyze spreadsheets without coding
Open-source rich text editor framework with extensions
Make AI-generated text sound natural and human-like
Publish 50+ SEO-optimized articles monthly with full automation
Make AI text read more naturally
Automate podcast editing and social clips
AI essay writing tool for students and professionals
AI voice covers for songs with 10,000+ voices
SMS automation for local businesses
Generate story outlines and full narratives from prompts
Rewrite AI text to read human-written
Rephrase text quickly and clearly
Auto-generate podcast summaries
AI video maker and editor
AI-powered writing and productivity tools
Convert typed text to realistic handwritten notes
Create AI voice covers and text-to-speech audio
Platform for publishing technical expertise to a global audience
Create AI-generated text adventure games for Discord
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.