Napkin
generalTurn text into visual charts and infographics
llms 18
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.
Meilisearch
generalFast, open-source search and AI retrieval engine
Shorts Generator
generalTurn text into short-form video
AiVOOV
generalGenerate realistic voices in multiple languages
Koast
generalLaunch, automate, and scale Meta ads from one dashboard
CopyCopter
generalConvert long-form content into short social videos
texttovideo.bot
generalConvert text into short videos
Note This Down
generalTranscribe and summarize meetings and lectures
AI Sound Effect Generator
generalGenerate sound effects with AI
I Have a Dream
generalScript and video editor for social media
Pipio
generalGenerate videos with customizable templates
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Text To Speech Online ❤️ FREE UNLIMITED
generalConvert text to audio with natural-sounding voices
Tiny Wow
generalFree tools for PDF, images, YouTube, and online utilities
Aardvark
generalSend SMS reminders before meetings to reduce no-shows
Text Wallpaper Generator
generalFree custom text wallpapers for devices and servers
F5-TTS
generalFree text-to-speech synthesis with natural speech
AIMusixer
generalGenerate and download AI-created songs instantly
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.