Dedalus Labs

Dedalus Labs is making a specific architectural bet: AI agents don't need lighter sandboxes, they need heavier ones. Where most code-execution vendors race toward V8 isolates and micro-containers for density, Dedalus ships full Linux VMs with a real kernel, root access and hardware-level isolation, and then attacks the cost of that weight with a sub-50ms boot and per-second billing on active compute only. That combination is the whole product. The strengths show up in the workflow it enables. A multi-step agent that reads a repo, installs a dependency, runs tests and then waits for the next tool call no longer throws that environment away between steps — filesystem and memory persist across sessions, so step seven doesn't start by re-running pip install. Machines that are asleep cost nothing while their storage keeps living, billed at $0.0001/GiB-hour from provisioned snapshots, so a fleet of mostly-idle agents doesn't bleed money the way an always-on sandbox does. Root inside every machine means you use the same apt, pip, npm and cargo workflow you'd use locally, and GPU/CUDA machines cover short-lived training or inference workloads. Multi-tenant auth through Dedalus Auth plus external URL support makes it a realistic place to host long-lived MCP servers, and live VM migration supports the zero-downtime multi-agent orchestration story. The honest weaknesses: this is not a managed Kubernetes or container orchestration platform, so teams already standardised on containers are fighting the model rather than fitting it. Purely stateless, short-lived workloads pay for persistence they never use — an ephemeral sandbox like E2B is the better-shaped tool there. Storage is POSIX ext4 disk space, not large object storage, so it won't replace an S3-style bucket for big media. At Hobby scale the ceilings bite quickly: 4 vCPU, 16 GiB RAM, 10 GiB storage per machine with no expansion option, and a 50 hr/mo compute ceiling. Machine tables on the seed side mention scale up to 32 vCPU / 128 GB, while the published pricing page caps Pro at 16 vCPU and 64 GiB — treat 16/64 as the tier you can actually buy today. And general access still runs through a waitlist. Where it fits: agent developers whose loops need a persistent Linux environment with real root, startups sandboxing model-generated and therefore untrusted code where kernel-level isolation matters more than density, and ML engineers who want GPU machines that stop billing the moment the job does. Where it doesn't: teams who want a managed container platform, buyers who need general availability this week, and anyone whose workload is a function that runs once and vanishes.