Dedalus Labs
Dedalus Machines are the fastest persistent sandboxes for AI agents. Spin up long-running, stateful agents that never sleep in <50ms.
Overview
What is Dedalus Labs?
Dedalus Labs provides cloud infrastructure purpose-built for AI agents that need to run for hours, days, or continuously in production. Unlike ephemeral sandboxes that destroy state after each session, Dedalus Machines give every agent its own persistent Linux microVM. Files, installed packages, environment variables, caches, and running processes all survive across sleep and wake cycles. Sleep is free — you only pay for compute when the agent is actually running. Key capabilities: - 50ms cold start with scale-to-zero (no idle charges) - VM-level hardware isolation for running untrusted LLM-generated code - Persistent POSIX-compliant filesystem across sessions - Per-second billing starting at $0.0504/vCPU-hr - GPU-capable VMs for local model inference - Built-in MCP (Model Context Protocol) support with a hosted marketplace of production-ready MCP servers - Multi-model SDK supporting Claude, GPT, Gemini, DeepSeek, Mistral, and 100+ models - Dedalus Auth (DAuth): zero-trust, multi-tenant authentication for MCP servers Dedalus is designed for developers building autonomous coding agents, research agents, CRM agents, monitoring agents, and any stateful AI workflow. Free tier includes $20 sign-up credit and 50 compute hours per month with no credit card required. Backed by Y Combinator (S25) with $11M in seed funding from Kindred Ventures and Saga Ventures.
What is Dedalus Labs used for?
Tags

Top Features
- Persistent VM infrastructure for AI agents
- Automatic sleep and millisecond wake-up
- MCP server marketplace
- REST API and SDK access
- Real-time agent monitoring dashboard
- Pay-per-use billing with free idle time
How to use Dedalus Labs?
Sign up and create a free account at dedaluslabs.ai Spin up a persistent VM for your AI agent in seconds Deploy your agent — VMs sleep automatically when idle and wake in milliseconds Monitor and manage running agents via the dashboard or API Scale as needed — pay only for active compute time
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Pros & Cons
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Use Cases
- Running long-running AI agents in production
- Hosting MCP servers at scale
- Building AI-powered automation workflows
- Deploying multi-agent systems
- Reducing cloud costs for AI workloads
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