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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.
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Free browser tool to place text behind images for social media
Platform for publishing technical expertise to a global audience
Generate manga pages from story descriptions
Chrome extension for text-to-speech, speech-to-text, and dictation
Create AI-generated text adventure games for Discord
AI-assisted investment platform with automated savings and tax features
Essay writer for academic assignments
Rewrite AI text to pass human detection
Blueprint for converting videos and podcasts into books
Convert audio and video to text transcripts
Extract text from images, documents, and screenshots
Free paraphrasing tool for essays and articles
Generate alt text for images automatically
AI text generation in Spanish
Rewrite text and rephrase content
Write research papers and essays with citations
Converts still images into short videos
Generate consistent images and videos from text
AI workspace for filmmaking workflows
Rewrite text for grammar and clarity
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.