DeepSeek
generalOpen-weight LLMs including a 671B MoE model that matches GPT-4o at far lower API cost
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.
Open-weight LLMs including a 671B MoE model that matches GPT-4o at far lower API cost
AI music creation and audio production
Create slides from text instantly
Scalable cloud compute
Long-form writing editor for storytellers
Turn text into memes with AI
Convert video to 3D animation instantly
Extract text from images with OCR
Write SEO-optimized blog content with ease
Summarize articles, PDFs, and videos instantly
Generate Anki flashcards from study materials
Generate quizzes from text, video, or audio content
Transcribe audio and video files to text
Create and schedule AI photos, videos, and voiceovers
Translate text in images across 130+ languages
Software deals for entrepreneurs
AI content creation and SEO assistant
Text-to-podcast tool with 120+ AI voices and natural conversations in multiple languages
Rewrite AI text to bypass detection
Turn documents into auto-graded quizzes
Humanize AI text to bypass detection
AI video generation
Directory of AI video creation tools with workflows
Canned responses and text shortcuts for support teams
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.