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Applied-AI studio and R&D group building enterprise products like the FeatureOS feedback platform for product teams.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.
MiniMax's general-purpose autonomous AI agent that plans and completes complex multi-step tasks from a single prompt.
Free trial available
No public pricing
No public pricing
- ✦In-house product line including FeatureOS and SupportWire
- ✦Feedback board, roadmap, changelog, and knowledge base modules (FeatureOS)
- ✦AI-powered feedback analysis and duplicate detection (FeatureOS)
- ✦API and webhook access for custom integrations
- ✦Applied AI R&D consulting engagements
- ✦Product engineering services for client teams
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- ✦Serverless GPU compute defined in Python
- ✦Sub-second container cold starts
- ✦Autoscale 0 to 1000+ GPUs
- ✦Inference, training and batch workloads
- ✦Secure sandboxes for untrusted code
- ✦Built-in logging and observability
- ✦ChatLLM access to multiple top AI models
- ✦AI agents and automation
- ✦No-code full-stack app creation
- ✦Enterprise generative AI platform
- ✦Structured ML model building
- ✦Optimization and forecasting
- ✦Autonomous multi-step task execution
- ✦Natural-language task delegation
- ✦Powered by MiniMax frontier models
- ✦Handles research, building and content tasks
- →Product teams collecting and prioritizing customer feedback (via FeatureOS)
- →Companies needing custom applied-AI research or engineering
- →Startups outsourcing product engineering to a specialist studio
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes
- →Chat with many AI models in one place
- →Build and deploy ML models
- →Automate tasks with AI agents
- →Delegating complex tasks to an AI agent
- →Automating research and analysis
- →Producing reports and deliverables