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
Voice generation and cloning from text
Create animated videos from images, text, or ideas
Get instant feedback on your writing to sound more natural
Make AI-generated text sound natural and human-like
Publish 50+ SEO-optimized articles monthly with full automation
Match similar names and addresses in databases
Adjust lighting in photos and videos with AI
4K AI video creation with lip sync
Rephrase text quickly and clearly
Auto-generate podcast summaries
Rewrite AI text to bypass detection
Convert typed text to realistic handwritten notes
Platform for publishing technical expertise to a global audience
AI motion generation for characters
Create custom sound effects from text descriptions
Voice-to-text that understands technical terms and jargon
Convert AI-written text to natural, human-sounding writing
Web search and semantic rerank API for LLM applications
Bypass AI detection with one click
Turn text descriptions into full-stack web apps
Convert text into short videos for YouTube and social media
AI code generation for.NET teams
Text and video chat with strangers worldwide, no signup required
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.