LOVO
generalText-to-speech with 500+ voices in 100 languages
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.
Text-to-speech with 500+ voices in 100 languages
Rewrite AI-generated text to sound human
Rent GPUs for AI, machine learning, and rendering
Turn text prompts into AI-generated videos
Voice-to-text for Mac, Windows, iOS
Voice generation and cloning from text
App translation with full context and human review
Convert AI text to natural, human-like writing
Create animated videos from images, text, or ideas
Voice-to-text transcription for notes and messages
Generate Excel formulas and analyze spreadsheets without coding
Open-source rich text editor framework with extensions
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
Translate subtitles in SRT, VTT, MP3, MP4, and WAV files
Make AI-generated text sound human and undetectable
Make AI text read more naturally
Automate freight forwarding and logistics workflows
Match similar names and addresses in databases
Automate podcast editing and social clips
AI essay writing tool for students and professionals
Turn text ideas into videos with captions and music
Edit videos with automatic subtitles and scene detection
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.