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Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.
Always-on cloud AI agent that runs multi-step workflows and monitoring on a dedicated 24/7 VM to automate business tasks.
Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
General AI agent that executes multi-step tasks end to end — research, slides, design, browsing — instead of only answering questions.
Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.
No public pricing
- ✦Visual workflow studio for agents
- ✦RAG knowledge pipelines
- ✦Agent runtime with tools and memory
- ✦Marketplace of models and plugins
- ✦Publish as app, API or MCP tool
- ✦Logging, analytics and monitoring
- ✦Always-on agent on a dedicated 24/7 VM
- ✦Multi-step task automation (docs, PPT, video, research)
- ✦Proactive monitoring with alerts and actions
- ✦Shared/self-improving agent knowledge network
- ✦Page deployment and drive storage
- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- ✦Autonomous multi-step task execution
- ✦Website and app building
- ✦AI slides, design and image generation
- ✦Manus browser operator
- ✦Wide Research mode
- ✦Cross-platform web, desktop and mobile apps
- ✦Serverless per-token inference with OpenAI/Anthropic-compatible APIs
- ✦On-demand dedicated and reserved GPU deployments
- ✦Fine-tuning and reinforcement-learning training pipelines
- ✦Large library of open LLM, vision, image and audio models
- ✦Optimized inference engine for throughput and latency
- →Building AI agents and chatbots
- →Creating RAG-based knowledge apps
- →Deploying LLM apps at enterprise scale
- →Automating recurring business workflows overnight
- →Generating reports, documents and presentations
- →Monitoring uptime, pricing or metrics with auto-actions
- →Running research and content tasks hands-off
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads
- →Automate end-to-end digital tasks
- →Produce websites and presentations
- →Conduct broad research
- →Hand off browser tasks to an agent
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale