redu.cloud

Product Introduction

1. Definition: redu.cloud is an agent-native Infrastructure-as-a-Service (IaaS) cloud platform. It provides on-demand, stateful cloud resources (virtual machines, managed databases, block storage, private networks) specifically designed to be provisioned, managed, and operated by AI agents via the Model Context Protocol (MCP).
2. Core Value Proposition: It exists to enable AI agents to execute cloud operations—such as application deployment, DevOps, infrastructure scaling, and live incident remediation—at near-agent speed. Its primary value is shifting cloud management from human-paced workflows (CLI, web console) to AI-agent-paced automation, offering granular control over real infrastructure with the convenience of agent-driven orchestration.

Main Features

1. Agent-First Cloud Orchestration (redu MCP): The core interface is an MCP server (mcp.redu.cloud/mcp) that allows AI agents (Claude Code, Cursor, GitHub Copilot, etc.) to directly interact with the cloud's API. Agents can authenticate, analyze project code, generate costed deployment plans, and execute provisioning commands after user approval. This bypasses traditional manual steps and enables programmatic, conversational infrastructure management.
2. High-Speed, Real Infrastructure Provisioning: The platform operates its own physical infrastructure in Germany (not a reseller), optimized for rapid provisioning cycles. This allows agents to spin up resources like compute instances (from £0.0764/hr), managed PostgreSQL/Redis, and block storage (from £0.0001/GB/hr) in seconds, minimizing agent "waiting" time and token consumption during complex operations.
3. Full-Stack, Hourly-Billed IaaS Components: Offers a complete cloud stack billed to the minute with no minimums. Key components include: Compute Instances (VMs), Autoscaling Clusters (policy-based horizontal scaling), Private Networks (isolated VLAN per project), Managed Databases (PostgreSQL with pgvector, Redis), Load Balancers, and Block Storage. This provides the raw building blocks for any architecture, controllable by an agent.
4. Deploy Library & Agent-Safe Workflow: Features a library of open-source application deployment recipes (e.g., n8n, Open WebUI, Gitea). An agent can use these as templates, propose a customized plan with cost, and await explicit user approval before provisioning. This creates a safe, reviewable gate for all agent-driven changes, combining automation with human oversight.

Problems Solved

1. Pain Point: The slow speed of human-operated cloud management (via consoles, CLIs, or IaC tools) creates a bottleneck for AI agents, wasting tokens and time on waiting for resource provisioning.
2. Pain Point: Platform-as-a-Service (PaaS) solutions (e.g., Vercel, Railway) abstract away infrastructure control for convenience, leading to vendor lock-in, higher costs at scale, and an inability to debug or fix issues at the machine or network level.
3. Target Audience: AI Agent Developers & Engineers building stateful, agentic applications; DevOps teams seeking to automate infrastructure via AI; Startups and developers wanting the cost control and flexibility of real IaaS without the operational overhead.
4. Use Cases: AI Agent Deployment: An agent deploying and scaling a full-stack application with a database and background workers. Live Incident Response: An agent diagnosing a production issue by SSH-ing into the VM, reading logs, applying a hotfix, and committing the change. Stateful AI Model Training: Provisioning GPU or high-memory instances on-demand for training runs. DevOps Automation: Agents managing CI/CD pipelines, autoscaling policies, and network configuration changes.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros: Unprecedented speed for agent-driven ops; full infrastructure control without manual sysadmin work; transparent, granular, hourly pricing with a generous free tier (always-on VM + Postgres); built-in safety via plan-review-approve workflow; operates its own EU-based infrastructure for data locality.
   • Cons: Requires integration and trust in an AI agent workflow; less suitable for teams not using MCP-compatible agents; as a newer platform, it lacks the vast service catalog (e.g., managed Kubernetes, serverless functions) of AWS or Google Cloud; primary region is currently in Germany.

2. Key Alternatives & Differentiation:

   • vs. Traditional IaaS (AWS EC2, Google Compute Engine): redu.cloud is not competing on breadth of services. Its differentiation is the agent-native MCP layer and provisioning speed optimized for AI, which AWS does not offer. It simplifies the raw IaaS model specifically for agent interaction.
   • vs. PaaS (Vercel, Railway, Render): Unlike PaaS which provides a managed runtime, redu.cloud provides the actual VMs and networks. This gives full root access and deeper control, allowing agents to fix low-level issues and build custom architectures (e.g., a self-hosted Kubernetes cluster), which is impossible on a locked-down PaaS. It typically offers lower cost at scale.
   • vs. Other "AI-Infra" Platforms: Many AI platforms focus on GPU instances for model training. redu.cloud's focus is on general-purpose infrastructure orchestration by AI agents, making it unique for deploying and operating complete applications, not just training models.

Frequently Asked Questions (FAQ)

1. How is redu.cloud different from a Platform-as-a-Service (PaaS)? A PaaS provides a managed slot for your code, abstracting the underlying machine. redu.cloud provides the actual virtual machines, networks, and databases (IaaS). This gives you full control and root access, which your AI agent can use to deploy, debug, and fix anything, avoiding PaaS limitations, lock-in, and premium pricing at scale.
2. Is it safe to let an AI agent control my cloud infrastructure? redu.cloud implements a safety-first workflow. The agent must present a detailed plan with costs for user approval before any provisioning occurs. API keys are scoped and short-lived, and all actions are fully logged. The agent acts as a driver on a guarded road, with you as the approving supervisor.
3. What happens if my deployment fails or breaks on redu.cloud? Because your application runs on a real virtual machine you control, your AI agent can securely SSH into the instance, diagnose the problem by examining system logs, apply a fix directly, and push the correction to your code repository. This level of in-place remediation is not possible in container-only PaaS environments.
4. What is the redu MCP and which AI agents support it? The redu Model Context Protocol (MCP) server is a standardized interface that allows AI agents to communicate with and control the redu.cloud platform. It is compatible with agents like Claude Code, Cursor, GitHub Copilot, Gemini CLI, and any other agent that supports the open MCP standard.
5. What does the redu.cloud free tier include? The free tier includes one always-on (non-expiring) virtual server and one managed PostgreSQL database, providing a permanent base for developers and agents to build and test projects without time limits or credit card requirements.