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Chinese AGI company building multimodal LLMs, Hailuo video, speech and music models, plus AI apps and open APIs.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
No public pricing
No public pricing
- ✦MiniMax M-series LLMs (M3, 1M context, MSA)
- ✦Hailuo AI video generation
- ✦Speech and music generation models
- ✦MiniMax Code agentic coding tool
- ✦Consumer apps (Hailuo, Xingye)
- ✦Open API and Token Plan for developers
- ✦Unified proxy and protocol conversion across 100+ LLMs
- ✦Model-level fallback and routing
- ✦Semantic and exact-match AI caching
- ✦Token tracking and quota controls
- ✦Content-safety and data-protection filtering
- ✦MCP service hosting and plugin marketplace
- ✦Prompt management as a single source of truth
- ✦Playground for prompt experimentation
- ✦Evaluation to measure changes before production
- ✦Observability and tracing for debugging
- ✦Collaboration across technical and non-technical roles
- ✦Open-source and self-hostable
- ✦Scenario-based agent testing
- ✦LLM evaluation and quality scoring
- ✦Observability for cost and latency
- ✦Prompt management with GitHub sync
- ✦Voice AI simulation
- ✦LLM red-teaming and governance
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- →Coding and agentic tasks
- →AI video generation
- →Text-to-speech and music creation
- →Building on MiniMax model APIs
- →Centralizing access to multiple LLM providers
- →Building and governing AI agent/MCP services
- →Controlling token spend across teams
- →Adding caching and safety to LLM calls
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs