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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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Create short videos from text with AI
Free video chat with random people
Free AI blog writing tool
AI motion generation for characters
Generate talking head videos from prompts
AI tool that generates text descriptions, captions, and prompts from images
Create custom sound effects from text descriptions
Convert AI-generated text to read as human-written
Generate 3D character animations from text descriptions
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Convert text to audio with natural-sounding voices
Study complex topics through your favorite characters and memes
Rewrite text in Spanish and remove plagiarism
Dictate and auto-format text 9x faster across any app
Voice-to-text that understands technical terms and jargon
Convert AI-written text to natural, human-sounding writing
Rewrite AI text to bypass detection tools
Web search and semantic rerank API for LLM applications
Free online notepad for quick note-taking and organization
Convert images to detailed text prompts instantly
Image generation with Flux.1 models and LoRA support
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SEO-optimized blog posts generated in minutes
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.