Dedalus Labs
Full Linux VMs in <50ms for AI agents — persistent, idle-free, pay-per-active-second.
If you're building stateful AI agents and tired of paying for sleeping sandboxes, Dedalus is worth the waitlist wait. Sub-50ms boots with persistent filesystem and memory are exactly what agentic workloads need, and per-second active-only billing beats E2B and Modal on cost. But it's not GA yet — evaluate those alternatives if you need production access today.
Verified 7d ago · liveness 68/100 · cite: rightaichoice.com/tools/dedalus-labs
- AI agent developers needing fast, stateful compute
- Teams building multi-step agentic workflows
- ML engineers running training/inference on ephemeral VMs
- Startups building sandboxed code execution environments
- Users seeking simple serverless function execution (e.g., AWS Lambda style)
- Teams needing a fully managed Kubernetes platform
- Projects requiring short-lived, stateless compute
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Skip Dedalus Labs if you need production-ready compute today, require SLAs and compliance, or prefer serverless platforms where you don't manage VMs.
Storage overage on Hobby is $0.0001/GiB-hour ($0.073/GiB-month) — if you exceed the 10 GiB cap, costs can add up for data-heavy workloads.
Dedalus Labs pricing fits startups and indie hackers who prioritize low upfront cost and avoid paying for idle time. At $0.04536/vCPU-hour and $0.01458/GiB-hour, it's competitive with E2B and Modal for active compute, but saves 60-71% on typical monthly usage. Hobby tier is free with a $20 credit; Pro at $20/mo includes a $20 credit, making effective cost $0 for moderate use.
In short
Dedalus Labs — Full Linux VMs in <50ms 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.
What's new in Dedalus Labs
Checked 7 days agoAcross the latest 4 updates: 1 feature update and 3 news mentions.
Why we are building virtual machines for AI agents
Dedalus Labs explains its focus on VMs for AI agents, addressing the need for fast, persistent, and cost-efficient compute.
From Today to A2A: Crossing the Imagination Chasm
Argues agent-native future is blocked by imagination, not tech; lists six missing pieces for agent adoption.
Everyone is building agents. But what does that actually mean?
Dedalus Labs defines what an AI agent is and offers a practical guide to building one.
Introducing Dedalus Auth
New multi-tenant auth layer ensures MCP agents never handle raw secrets — security feature for agent deployments.
What people actually say about Dedalus Labs — is it worth it?
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) · researched Jul 6, 2026.
- +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.
- −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.
- • GPU usage may cost extra — not clearly detailed in pricing tiers
- • Data transfer and storage beyond included amounts may incur charges
Viability Score
How well maintained and how widely used is Dedalus Labs? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Full Linux VM boot in <50ms
- Persistent filesystem and memory across sessions
- Zero-cost idle time
- Per-second billing for active compute
- Scalable vCPU (1-32) and RAM (1-128 GB)
- Root access with apt, pip, npm, cargo, brew
- GPU and CUDA support
- Live VM migration
- POSIX-compliant storage (ext4)
- Nested virtualization
- Hardware-isolated VMs (Cloud Hypervisor)
- SSH, CLI, and HTTP API access
- External URL support for MCP servers
- Multi-tenant auth (Dedalus Auth)
About Dedalus Labs
Dedalus Labs sells full Linux virtual machines that boot in under 50 milliseconds, keep that 500 GB disk and your memory intact while they sleep, and bill nothing for idle time. The pitch is aimed squarely at AI agent developers: you get root access, GPU/CUDA, Docker, and any runtime (Python, Node.js, Rust, Go, Java, Ruby) inside a hardware-isolated VM, so you can run stateful, multi-step agentic workloads without fighting sandbox limits or paying for cold starts. The core trick is snapshot restore: no cold starts, no pulling images, no waiting for a runtime to spin up. Machines preserve filesystems, installed packages, environment variables, and even background processes across sleep and wake cycles. You manage everything through a CLI, SSH, or HTTP API — a single command creates a machine, and the dashboard shows live CPU and memory usage per VM. Billing is per-second and only for active compute; storage is included (10 GiB on the free Hobby tier, 20 GiB on Pro), with overage at $0.0001/GiB-hour. vCPU scales 1–32 and RAM 1–128 GiB per machine, and you can scale config on the fly. The Pro plan ($20/mo) throws in a $20 monthly compute credit, unlimited compute, and priority support. Compared to ephemeral sandboxes like E2B or Modal, Dedalus eliminates the idle-charge tax — their own calculator shows roughly 60–71% savings on a typical 200-active-hour month — and gives you stronger VM-level isolation with full root access. It's still waitlist-only, so it's a tool to adopt when you need real VM control and want to avoid paying for sleeping containers.
Behind the Verdict
Dedalus Labs is targeting a real pain point: the cold-start tax and idle-time billing that plague agent sandboxes. By keeping full VMs warm and billing only for active compute, it removes the two biggest financial and latency hurdles in stateful agent development. The sub-50ms boot time is genuinely impressive and directly addresses the user experience of agents that need to spin up quickly during a conversation. The persistent filesystem and memory mean you don't have to reinstall dependencies or re-authenticate services on every run, which is a massive productivity win for multi-step workflows. The VM-level isolation is a strong security story for running untrusted code from LLMs, and root access gives you full flexibility to install any tool or runtime you want. The pricing is transparent and per-second, so you only pay for what you use, and the $20 sign-up credit actually covers meaningful trial time. However, the waitlist-only status is a significant drawback for teams that need production reliability today. You cannot depend on a product that isn't generally available. Additionally, the free tier's 50-hour monthly ceiling may be tight for some workloads, and storage overage at $0.0001/GiB-hour could add up for data-heavy applications. Compared to E2B and Modal, Dedalus offers a different cost model — you avoid idle charges but may pay more for sustained active compute. It's also not a serverless platform; you get VMs, not functions, so you'll need to manage your own infrastructure to some degree. For indie hackers and startups that prioritize cost and control over turnkey simplicity, Dedalus is compelling. For enterprises that need immediate SLAs and compliance, waiting for GA or building on a more mature platform may be wiser.
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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.
Building a multi-step agent that needs to keep state between tool calls.
Outcome: You create a persistent VM with 2 vCPU, 4 GiB RAM, and install Python dependencies. The agent boots in <50ms for each step, filesystem and memory persist, and you only pay for active seconds.
Training a model on GPU but want to avoid paying for idle time.
Outcome: You spin up a GPU-backed VM, run training, and pause the machine when not in use. Billing stops instantly, and you resume later with the exact state intact.
Building a sandboxed code execution environment for an AI product.
Outcome: You create isolated VMs for each user session with root access and VM-level isolation. Nested VMs and live migration handle scaling, and you only pay per active second.
Use Cases
- Run an agentic code generation pipeline on a full Linux VM booting in <250ms
- 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 multi-agent orchestration with live VM migrations for zero downtime
- Create nested VMs for testing sandboxed environments
- Deploy an MCP server with persistent storage and secure auth
- Run CI/CD pipelines with persistent environments
- Host multi-step agent workflows using up to 32 vCPU and 128 GB RAM
Limitations
- Dedalus Labs machines are in waitlist phase for general access.
- The Hobby plan caps vCPU at 4, RAM at 16 GiB, and storage at 10 GiB, with a 50 compute hours per month ceiling.
- The Pro plan offers up to 16 vCPU, 64 GiB RAM, and unlimited storage, with $20 monthly compute credits.
- Billing is per-second for active compute, with idle time free.
as of 2026-08-16
Verification history
We have re-verified Dedalus Labs 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
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
Individual developers experimenting with stateful AI agents, who want a free tier with a $20 one-time credit to test sub-50ms VMs.
What this tier adds
Free entry point with up to 4 vCPU, 16 GiB RAM, 10 GiB storage, 5 machines, and a 50 hr/mo compute ceiling.
Pro
$20/mo
Ideal for
Active agent developers and small teams that need more compute, unlimited hours, and priority support, willing to pay $20/mo for a $20 credit.
What this tier adds
Adds $20 monthly compute credit, up to 16 vCPU, 64 GiB RAM, unlimited storage, 20 machines, unlimited compute, configurable timeout, and priority support.
Enterprise
Custom
Ideal for
Organizations that need dedicated infrastructure, custom provisioning, SSO/RBAC, audit logs, and SLA guarantees for production agent workloads.
What this tier adds
Custom vCPU/RAM/storage, unlimited machines and compute, dedicated fleet option, SSO/RBAC/audit logs, SLA, bring your own cloud, and 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 Labs pricing fits startups and indie hackers who prioritize low upfront cost and avoid paying for idle time. At $0.04536/vCPU-hour and $0.01458/GiB-hour, it's competitive with E2B and Modal for active compute, but saves 60-71% on typical monthly usage. Hobby tier is free with a $20 credit; Pro at $20/mo includes a $20 credit, making effective cost $0 for moderate use.
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 developers, first value in under 5 minutes: install CLI with one curl command, create a machine with 'dedalus machines create', SSH in. The free $20 credit covers initial experimentation. For ML engineers, setting up GPU with CUDA takes about 15 minutes. For teams, additional config like auth and fleet management may take an hour, but the core flow is fast.
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.
- →From E2B: recreate your sandbox code using CLI or API; persistent VMs remove the need for state management.
- →From Modal: adapt your functions to run in a full VM; you gain root access and persistent filesystem.
- →From Docker: move your containers into a VM for stronger isolation and persistence; you can use apt or any package manager.
- ↗To E2B: if you need public availability, E2B offers GA with similar sandboxing but idle billing.
- ↗To Modal: for serverless functions with scale-to-zero, Modal provides a mature platform with pay-per-use.
- ↗To AWS EC2: for full control and SLAs, EC2 offers reserved instances but with higher cost and management overhead.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Dedalus Labs
Common stack mates teams adopt alongside Dedalus Labs, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Dedalus Labs vs Spider Cloud
For AI agents that need persistent, stateful compute with zero cold start, choose Dedalus Labs. For agents that need fast, reliable web data extraction, choose Spider Cloud. Both have free tiers and complement each other.
Dedalus Labs vs Temporal Ai
If you need instant, persistent virtual machines for your AI agents to run code and drive browsers, Dedalus Labs is the clear choice. If you need durable, fault-tolerant orchestration across services and agents, Temporal AI is unmatched. They are complementary—use Dedalus for compute and Temporal for coordination.
Dedalus Labs vs Presto Voice
Dedalus Labs and Presto Voice serve completely different markets. Dedalus is a compute platform for AI agents needing fast, stateful VMs with persistent storage and pay-per-use pricing. Presto Voice is a drive-thru voice AI for QSR chains, focused on automation and upselling. Buyer choice depends on whether you need agent infrastructure or restaurant automation.
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