Dedalus Labs
FreemiumFull Linux VMs in <250ms for AI agents — persistent, idle-free, pay-per-active-second
By Tanmay Verma, Founder · Last verified 06 Jul 2026
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Added 5d ago
77/100Safe BetIn short
Dedalus Labs — Full Linux VMs in <250ms for AI agents — persistent, idle-free, pay-per-active-second. Best for AI agent developers needing fast, stateful compute, Teams building multi-step agentic workflows, ML engineers running training/inference on ephemeral VMs. Free to start; paid plans from $20/mo.
Is Dedalus Labs actually worth it?
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Editorial Verdict
Best for
AI agent developers needing fast, stateful computeTeams building multi-step agentic workflowsML engineers running training/inference on ephemeral VMsStartups building sandboxed code execution environmentsDevelopers automating CI/CD pipelines with persistent environments
Not ideal for
Users seeking simple serverless function execution (e.g., AWS Lambda style)Teams that need a fully managed Kubernetes platformProjects requiring short-lived, stateless compute with no persistence needsApplications requiring massive object storage (limited to 20 GB standard tier)
Dedalus is the most cost-effective option today for stateful agentic compute — no idle billing, sub-250ms cold starts, and real VM isolation. It's still in waitlist but the transparent pricing and $10k startup credit make it a no-brainer for teams building persistent AI agents.
Skip Dedalus Labs if Skip Dedalus Labs if you need a fully managed serverless platform or static hosting — Dedalus is raw VMs with root access, designed for stateful agent workloads, not for quick function deploys.
Compare with: Dedalus Labs vs LangSmith, Dedalus Labs vs Spider Cloud, Dedalus Labs vs Truleo
Last verified: July 2026
What's new in Dedalus Labs
Checked 2 days agoAcross the latest 5 updates: 2 feature updates and 3 news mentions.
NewsBlog·10 days agoNewest
Why we are building virtual machines for AI agents
Dedalus Labs explains rationale for building VMs for AI agents, emphasizing speed and persistence.
NewsBlog·Mar 25
From Today to A2A: Crossing the Imagination Chasm
Essay on six missing pieces for an agent-native future.
NewsBlog·Mar 11
Everyone is building agents. But what does that actually mean?
Defines what an AI agent is and how to build one.
FeatureBlog·Feb 28
Introducing External URL Support
MCP servers deployed on Dedalus can now connect to any agent via external URLs.
FeatureBlog·Jan 19
Introducing Dedalus Auth
Multi-tenant auth layer ensuring MCP agents never handle raw secrets.
What independent users actually report about Dedalus Labs
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
8 mentions across 3 sources (Hacker News, Bluesky, Lemmy).
70% positive30% critical
Recurring strengths
- +Sub-250ms boot times via snapshot restore for instant agent startup.
- +Zero-cost idle — no charges when machine is asleep.
- +Per-second billing for active compute only saves money on bursty workloads.
- +Full root access and hardware isolation for complete system control.
- +Persistent filesystem and memory across sessions without cold starts.
Recurring frustrations
- −Very few independent user reviews — early adopters risk unknown issues.
- −Stateless runner design limits complex stateful agent workflows.
- −Authentication features still work in progress, not production-ready.
- −No published uptime guarantees, SLAs, or enterprise compliance info.
- −Proprietary snapshot restore could make migration off-platform hard.
Patterns worth knowing
Fast boot times and zero-cost idle are the standout value propositions
Seen on Hacker News, Bluesky
Platform is very early stage with limited independent user feedback
Seen on Hacker News, Lemmy
Auth implementation is recognized as an area needing improvement
Seen on Hacker News
Learning curve
beginnerProductive in ~A few hours
Hidden costs people mention
- • GPU usage may cost extra — not clearly detailed in pricing tiers
- • Data transfer and storage beyond included amounts may incur charges
Viability Score
77/100
Safe BetHow likely is Dedalus Labs to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Full Linux VM boot in <250ms via snapshot restore
- Persistent filesystem and memory across sessions
- Zero-cost idle: no charge when machine is asleep
- Per-second billing for active compute only
- Scalable vCPU (1-32) and RAM (1-128 GB) per machine
- Root access, apt/pip/npm/cargo, system packages
- GPU and CUDA support for ML workloads
- Disaggregated compute and storage with live VM migrations
- POSIX-compliant persistent storage (ext4; xfs, btrfs, OpenZFS on roadmap)
- Nested virtualization for custom sandboxes
- Hardware-isolated VMs with dedicated guest kernel (Cloud Hypervisor)
- SSH, CLI, and HTTP API access
- External URL support for MCP servers
- Multi-tenant auth layer (Dedalus Auth) for secure MCP agent secrets
About Dedalus Labs
FreemiumAdvancedAPI availableAPI · CLI
Dedalus Labs delivers full Linux virtual machines that boot in under 250 milliseconds, persist filesystem and memory across sessions, and charge nothing for idle time. Built for AI agents that need stateful, low-latency compute — think autonomous agents running multi-step tasks, web scrapers maintaining browser sessions, or ML pipelines that resume instantly. The service uses snapshot restore technology to avoid cold starts, giving you root access, GPU/CUDA support, and complete system-level control inside hardware-isolated VMs. Key features include per-second billing for active compute only, scalable vCPU (1–32) and RAM (1–128 GB) per machine, zero-cost idle, POSIX-compliant persistent storage, and live VM migration via disaggregated storage and compute. The CLI, SSH, and HTTP API make it easy to create and manage machines in a single command. Dedalus supports any runtime (Python, Node.js, Rust, Go, Java, Ruby) and any package manager (apt, pip, npm, cargo, brew). Pricing is transparent: a free Hobby tier with $20 sign-up credit, a $20/month Pro tier with $20 monthly compute credit, and Enterprise with custom options including dedicated fleets, SSO, and BYOC. Storage is included up to tier limits (10 GB Hobby, 20 GB Pro), with additional storage at $0.08/GB per month. Compared to ephemeral sandboxes like E2B or Modal, Dedalus eliminates idle charges and provides stronger VM-level isolation with full root access — ideal for stateful agentic workloads.
Behind the Verdict
Dedalus Labs is solving a real pain: paying for idle compute in agent sandboxes. The zero-idle billing model is a genuine differentiator — most container-based competitors charge even when your runtime is asleep. Dedalus also offers stronger isolation (hardware VMs vs Docker) and true persistence (memory + filesystem survive reboots). When to pick this: You're building AI agents that need state (browser sessions, long-running workflows) and you're tired of cold starts or idle costs. Dedalus' VM-level isolation and root access make it suitable for running untrusted code or custom kernels. When to pass: You need serverless functions (AWS Lambda style) or a fully managed Kubernetes platform. Dedalus gives you raw VMs — there's no abstraction layer for auto-scaling or orchestration. Also, storage is limited to 20 GB on Pro (500 GB max) and GPU support is present but not detailed. Compared to E2B: E2B offers sandboxed environments but with Docker-level isolation and idle charges. Dedalus gives stronger isolation (KVM/VMM) and completely free idle. Modal offers serverless GPU compute but is not designed for persistent state. Real-world usage caveats: still in waitlist as of mid-2026, so not fully public. The CLI and documentation look solid, but enterprise features like SSO, audit logs, and BYOC are only on Enterprise. The startup credit ($10k) is generous but requires application. In practice, Dedalus is the most developer-friendly option for stateful agents today — transparent pricing, fast boot, and no surprise bills. If you need persistent, low-latency compute for agents, it's worth joining the waitlist.
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Real-world workflow fit
Concrete scenarios for the personas Dedalus Labs actually fits — and what changes day-one when you adopt it.
Agent developer
You're building an autonomous research agent that needs to run Python scripts, browse the web, and store state over several hours. With Dedalus, you create a machine with 2 vCPU, 4 GiB RAM, and 10 GB storage, SSH in, install dependencies, and run your agent. The machine persists across sessions and you only pay for active compute.
Outcome: Agent runs continuously without cold starts; costs $0 when idle; total monthly bill under $20 for moderate usage.
ML engineer
You need to train a model on a GPU instance for a few hours a day, but want to keep the environment (datasets, installed packages) around. Use Dedalus to spin up a GPU-backed VM, run training jobs, then let it sleep. The storage persists, so next day you resume instantly.
Outcome: Training environment always ready; no idle charges overnight; pay only for GPU compute time, saving 60% vs. GPU spot instances.
Scraper operator
You run a web scraping service that needs to maintain browser sessions across pages. Deploy a fleet of Dedalus machines, each with persistent memory and filesystem. When a scraping batch finishes, machines idle at no cost. The CLI lets you orchestrate thousands of machines.
Outcome: Scraping throughput high with no cold start delays; cost per page scraped drops by 70% because idle time isn't billed.
Use Cases
- Spin up a full Linux VM in <250ms to run an agentic code generation pipeline
- Persist a web scraping agent's browser state across sessions without cold starts
- Run ML training jobs on GPU-backed VMs with pay-per-second active billing
- Build a multi-agent orchestration system with live VM migrations for zero downtime
- Create nested VMs inside Dedalus Machines to test sandboxed environments
- Deploy an MCP server with persistent storage and secure auth for agent tool access
- Run CI/CD pipelines with persistent environments for faster iteration
- Host a multi-step agent workflow that uses 32 vCPU and 128 GB RAM temporarily
Limitations
- As of mid-2026, Dedalus is in a waitlist phase and not fully publicly available.
- The Hobby plan caps vCPU at 2 and RAM at 4 GiB per machine, with only 50 compute hours per month.
- Storage is limited to 10 GB on the free tier.
- GPU support and advanced storage filesystems (xfs, btrfs) are still on the roadmap.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Dedalus Labs tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/mo
Ideal for
Solo developer or student wanting to test Dedalus with minimal commitment — includes $20 one-time credit, no credit card required.
What this tier adds
Free entry point; capped at 2 vCPU / 4 GiB RAM / 10 GB storage / 5 machines / 50 compute hours per month.
Pro
$20/mo
Ideal for
Individual developer or small team running moderate workloads — includes $20 monthly compute credit for active usage.
What this tier adds
Adds $20 monthly usage credit, configurable timeout, priority support; ups vCPU to 4, RAM to 8 GiB, storage to 20 GB (expandable), machines to 25, unlimited compute.
Enterprise
Custom
Ideal for
Teams needing dedicated infrastructure, custom specs, SSO, audit logs, and SLAs — e.g., a funded startup running 10+ machines.
What this tier adds
Custom vCPU/RAM/storage per machine, unlimited machines and compute, dedicated fleet option, SSO/RBAC/audit logs, SLA, BYOC, dedicated support.
Where the pricing makes sense
The company stage and team size where Dedalus Labs's pricing actually pencils out — and where peers do it cheaper.
Dedalus pricing fits startups and small teams that want predictable compute costs without idle charges. Compared to AWS EC2 or GCP, you save 60-70% for intermittent workloads. For teams running many VMs with low utilization, Dedalus's $0/idle is a game changer. Enterprise is custom, but the free Hobby tier with $20 credit lets you test without risk.
Setup time & first value
How long it actually takes to get something useful out of Dedalus Labs — broken out by persona, not the marketing-page minute.
For agent developers: install the CLI and set your API key in under 2 minutes. First machine boot in <250ms. For teams: enterprise onboarding with custom configuration may take a few days, but the API and docs allow self-service deployment.
Switching to or from Dedalus Labs
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From AWS EC2: Migrate by creating a Dedalus machine with the same OS, install your software, and use rsync or docker to transfer data.
- →From E2B: Recreate your sandbox images as Dedalus machines with root access and persistent storage; update your agent's API calls to use Dedalus endpoints.
- →From Modal: Redesign your ephemeral modal functions to run as long-running Dedalus VMs; mount persistent storage and adjust invocation patterns.
Migrating out
- ↗To AWS EC2: Export your data via rsync or create an image, then launch an EC2 instance with the same configuration.
- ↗To GCP: Use gcloud compute to create a VM and transfer your disk image via cloud storage.
- ↗To On-Prem: Set up your own hypervisor and migrate the VM image from Dedalus using standard disk export tools (if available).
Resources & Guides
Frequently Asked Questions
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