There are sooooo many sandbox providers out there.
They do spike on different features like:
- snapshotting and forking
- good SSH and VPN access for end-users
- agent-friendly features, like obscuring secrets at network layer
Then there's also the option to use libkrun to run local sandboxes on your own computer. That doesn't scratch the itch for hosted services, but works if your goal is to run agents inside isolated environments for your own work.
I've been working on some open-core stuff[1] to coordinate sandboxes, and we're making changes to have a library that lets people coordinate any number of remote or local sandboxes using any provider, kinda like how the Docker CLI works for managing containers, git repos, and coding agents. Flue[2] is another player in this space, and is more of a pure framework, while we're building it as an interactive product for using sandboxed agents and workflows.
My personal belief is that the future of an "app" is a combo:
1. micro VM
2. agent on the VM
3. software bundled into the VM
So, it should be stupid simple to run these local sandboxed apps/agents. Right now, not too hard for technical users (esp. with things like https://smolmachines.com/ and https://microsandbox.dev/), but not as easy as clicking an app icon or typing `/path/to/binary` in the CLI
Microsandbox claims to start faster than docker, and it is isolated from the host, and to work with OCI. Why would I still want to use docker? The only reason I can imagine is that I actually want to be able to dynamically share resources between containers instead of dividing up VMs a priori.
I was going to add a comment praising smolmachines' smolvms. Simple, fast (sub-200ms cold start), OCI-compat, and has trivial packing to standalone 0-dep executables. No need for Docker Desktop / colima / orbstack. For those who prioritize security, kernel isolation is a meaningful benefit.
Firecracker has more tooling for the orchestration layer that manages many sandboxes at once. Stuff like K8S integration, an external REST API control plane, more first-class support for snapshotting, etc.
What people aren't getting with `firecracker` is utilization. Don't get me wrong, `firecracker` is great software and it's what I'm using for lightweight virtualization, but workloads are really bursty over really short periods of time now, even with the snapshot and restore that you can get if you're willing to hack on `firecracker` substantially, you hit walls where it's like, this is too much against the grain, this thing wasn't designed to bounce from 1 core to 32 to 8 to 16 to 4 to 32 to 1 seamlessly, and that's what it takes to get extreme utilization even with extremely good ML on the prediction.
I am quite sure I'm not the only person working on post-firecracker KVM.
I designed my take to basically eliminate the concept of vm being a rigid box of cpu/memory with CPU oversubscription and virtio-ballooning on memory + sparse ext4.
That way it can be elastic in CPU, memory and somewhat disk.
The holy grail microVM for me is one that can properly share a GPU across VMs, similar to what you can do with containers.
Shout out to https://smolmachines.com/ for supporting Vulkan over virtio-gpu/Venus. Currently the best implementation I'm aware of. Unfortunately my use case is running a full desktop inside the VM, and streaming it out over something like Sunshine/Moonlight. For this you need GPU rendering and video encoding. Venus rendering works, but you have to pass the frames back and forth between the host and the guest multiple times which is inefficient. Also Venus doesn't support video encode as far as I can tell.
The problem is that this feature is generally restricted to enterprise customers because VDI systems are such a profitable market. NVIDIA and AMD both only offer this on enterprise cards, and Intel has been very wishy-washy on support in their cards.
If you're looking for a thing to google, look up SR-IOV support on (consumer) GPUs.
Also if you're wondering who the customers of these things tend to be, it's generally the CAD market, law firms, etc. If no one's laptop contains sensitive data and can only stream the desktop of a remote system, the loss or theft of an employee's computer isn't nearly the same kind of a security worry.
I'm aware of SR-IOV. Widespread support would go a long way, but doesn't it require pre-slicing the GPU into discrete chunks? I want microVMs that can share share a GPU dynamically the same way they share overprovisioned CPU resources. Much more like containers.
It's about time AWS got into the agent sandbox game.
The startups in this space right now don't provide much value on top of the cloud providers they're wrapping. They don't tend to be run by experienced infra people either so they seem very vibecoded, insecure, janky, etc. They're also significantly overpriced because they're marking up already expensive providers.
Something surprising from my own experience is that while there's certainly a huge role for async agents in cloud sandboxes, async agents running locally seem more useful in many cases.
To be fair to jacobgold, at this point there is more or less an AWS services announcement singularity: if you didn't see the announcement when it happened you may never catch up or even find it in the wretched console website.
Though I did know about this one! (Because I saw the announcement.)
It just seems pretty different to me? I've lots of similar stuff and yet I still don't understand what it's for and how it works after scanning the docs quickly.
Major Sandbox providers (e.g. Modal) run on non-hyperscaler bare metal not AWS and so don't need to markup on AWS's markup. Thus, prices are comparable or better than AWS.
In that case it's still overpriced because they're charging hyperscaler prices without offering a hyperscaler level service in terms of scalability, reliability, security, trust, etc.
Most of the startups are just wrappers around AWS and significantly more expensive.
Agents need sandboxes that are cheaper so that they can run thousands
I feel that AWS, GCP and all the other cloud providers can provide this natively.
But still it would be nice to self host.
The best part of self hosting is that you own it as well, no rug pulls from the laundry list of reselling providers that could go away at anytime.
It would be nice to have a one click sandbox agent on a self hosted instance that is, free, fast (can pay a bit more for more intensive operations) and that is open source.
What's the best provider to self-host Firecracker? I feel that AWS is not a safe or cost-effective option for a self-funded startup or small business. Although is anything cost effective anymore? Hetzner just had a massive price hike.
Part of it might just be that I am old and inflation is catching up with my understanding of prices.
But as far as AWS I still have to say no thanks. Imagine some group actually started using my hosted AI agent service for something compute and network intensive. It could turn into $2000 overnight and if I didn't account for one of the numerous types of AWS charges, I might have only collected $500 for credits purchases.
Or it could easily be ten times that. But who am I kidding. No one is going to use my agents. So it doesn't matter if it's gvisor or Firecracker or whatever.
Are you looking for highly ephemeral nodes, where you are writing automation that will use the API to orchestrate it? Or do you just want small microVMs that you launch and kill?
Firecracker just has a ReSTful unix socket with a defined API and launches KVM vms with limited options.
For custom SMB I still think libvirt is a lower entry cost and may have transferable use cases to longer lived VMs, so you can just launch a qemu microvm[0] and use virsh and/or libvirt xml to set up the networking.
The ~400ms boot time of a qemu microvm vs ~120ms for firecracker may not be an issue for some loads, but qemu will also allow you a bit more density of placement than firecracker. qemu microvms will use a bit more memory individually, but they will also tend to use less real system memory with a larger number of microVMs.
It is all tradeoffs, and kata containers are yet another option that may apply depending on your use case.
You can run your own firecracker or qemu/kvm microvms on most instances that allow nested hypervisors, or on a local host. If cost containment is critical to you this is one possible way forward.
Really it just depends on if you want/need ReSTful control, or need to support short lived serverless functions, or if CLIs fit better and you many want to support full VMs.
They both are just Virtual Machine Monitors that targeted different use cases and decided on different tradeoffs.
Just be careful about hosting traditional containers and microVMs on the same system, that config is going to be problematic do to fundamental reasons that are too complex to properly address here.
Why do you want to self-host vs. using one of the many providers out there?
Daytona, E2B, OpenComputer, Freestyle, Blaxel, Vercel, Modal, Cloudflare, Tensorlake, Superserve, etc. etc.
Some of them work by pre-purchasing credits, so you can control the blast radius of spend.
Also, if you want a more embedded sandbox runtime as a library instead of a daemon + REST API, you can check out libkrun (and friendly layers on top of it like https://microsandbox.dev/ and https://smolmachines.com/)
The simplest worthwhile DIY sandbox you can have is to layer two tools: bwrap and gvisor.
bwrap args -- gvisor args do args -- /path/sandboxee args
bwrap will set up the environment and then gvisor elevates it into a true sandbox.
Standalone gvisor (not the 'do' subcommand) used to be a mess with the OCI json requirement, but recently they began work on presenting their own bwrap interface (likely to pursue AI agent uses) though I wouldn't use it myself yet.
People often look down on gvisor because they think it's some kind of syscall filter, it is not. It can use one of ptrace, seccomp or even KVM to intercept ALL syscalls and service them with it's own logic (which is in Go). Basically it's a VMM and kernel in one.
Any reason why you wouldn't use gVisor's bwrap interface yet? We're working on it precisely to make DIY sandboxing on Linux as easy as possible in order to get Linux-sandboxing-at-home to mature beyond the current syscall-filter-and-namespaces duct tape stage, so I'm curious to know what you'd like to see.
For self-hosting, have a look at what we're building with SlicerVM.com (disclosure: I'm the founder). Also runs just as well on Apple Silicon.
We run quite a few Slicer instances on mini PCs and Ryzen builds - also on Hetzner (and yes ouch 120 EUR / mo up to ~ 550 EUR / mo for 16core / 128GB RAM feels almost unfair)
Interesting. How does this compare to Firecracker? Also PhoenixNap looks really interesting. Do you happen to know if Linux software compatibility holds up on Ampere? 80 cores for $400 a month seems pretty good.
I specifically complained to a fly.io staff on here about their "gotcha, b*tch" usage based pricing which they basically copied from AWS, and they stood by it and other people here backed them up. No one is giving me a pile of free money, so I can't risk that kind of thing.
For those looking to run agents: the short lifecycle of the typical “sandbox” seems surprisingly limiting to me. I have no actual workflow where I want one of these products. Sometimes a VM can live for 30 minutes, but it also might need to live for a month, and I don’t know beforehand.
This is why I have been avoiding the word sandbox for exe.dev. I don’t think developers agents need something “sandbox” shaped.
Do you mind adding https://isorun.ai? We just launched last week. Founder here (Staff SRE with 20 years in Linux, fastest and cheapest SaaS agentic runtime running on heavily modified Firecracker)
I’ve been working with AgentCore that uses the same MicroVMs. They are capable in many ways but for coding agents that load a big got repo they get bloated quickly with the git repo.
I’m building this google3 style mounting to address this.
ramp i think just prebuilts the image/snapshot with the latest checkout version regularly [0]. do you think putting it into the image would address it?
> Containers launch in seconds, yet their shared-kernel architecture requires significant custom hardening to safely contain untrusted code
That's literally why they made Fargate. It's managed firecracker VMs with containers. They invented firecracker for this purpose. This new product is competing with Fargate, but they don't mention Fargate at all in the announcement.
> you create a MicroVM Image by supplying a Dockerfile and code packaged as a zip artifact in Amazon S3
>
> MicroVMs support up to 8 hours of total runtime
So you're already using containers with this new thing, same as Fargate! And not only that, it's more limited in runtime than Fargate! The only thing different with this service is stateful file storage, which is actually a problem you later have to engineer around, which is why containers are stateless.
This smells like a competing team building something to capitalize on AI hype, but the product isn't differentiated enough for this to make sense long term. If this was a service called managed AI agents, and you added features specific to AI agents, that has value. But "here's Fargate with a different name" isn't gonna last.
I don't think Fargate fits for the use case they are describing. If you're running your own (trusted) code, then of course there's no reason to worry about containment threats. But the threat here is that you have to execute arbitrary, untrusted code that is presumptively malicious. It's a very different scenario and requires considerable measures to safeguard properly. You can't have a Fargate Task that runs multiple containers, one for each user, for instance, or even run multiple Fargate Task instances, one for each user, because you're still having them all share a virtual EC2 host (well technically a pool of EC2 servers but it's one hypervisor and shared virtual kernel, essentially) that would be compromised if any one container escapes. If you need true hypervisor-level host kernel isolation on a per-user basis due to the risk of containment, with guest worker microVM threads, plus the whole thing needs to scale and also needs to pause and restore very quickly and keep track of state upon restoration, it's actually a pretty hard challenge to build on AWS with existing tools. The problem arises with any interactive AI agent environment that scales on a per-user basis, for instance, but it also applies to any scenario in which the user needs to execute untrusted arbitrary code on your infrastructure in a sandbox. Fargate isn't the secure choice in that scenario; you would instead use VPC + EC2 + Firecracker + Docker (plus S3 and many others) and use a lot of orchestration scripting and fiddling with load balancers and the like to try to get everything working and scaling. When you combine it with tracking state and also restoring quickly from a paused or suspended state, I can see reasons why this might be the right choice if you want to implement something with an interactive AI agent that isolates at the per-user or per-session layer from the host kernel and is highly secured against containment escape and other vulnerabilities. I'm curious if anyone has used this for the use case described, maybe from AWS? Is this like the AgentCore orchestration that came out maybe like last year?
Pretty sure they invented Firecracker for Lambda. Iirc they were previously using a hot pool of EC2 instances behind the scenes with each customer getting their own instances and lambdas sharing capacity on an instance. Firecracker made it possible to spin up VMs in realtime instead of having spare capacity laying around.
That said, Fargate does kind of seem like a superior option
Edit: I guess this supports suspend and fast resume so invocation time should be somewhat better than Fargate.
And also, you’ll notice that Fargate takes minutes to launch while Lambda takes a second or less. You’re waiting on AWS to launch a EC2 with your config and pull your containers into it.
(that article matches things I heard from Amazon when I asked why my stuff is slow)
It's in the very first body paragraph of the article:
"A new class of multi-tenant applications has emerged that all share the need to hand each end user their own dedicated execution environment in which to safely run code that the application developer did not write. AI coding assistants, interactive code environments, data analytics platforms, vulnerability scanners, and game servers that run user-supplied scripts all fit this pattern."
AI agents. Chatbot session of 8 hours is a lot. 16 vCPU might be useful when developing heavy application and agent need run application tests. You can think what infrastructure https://claude.ai/code needs.
SaaS offering the usage of LLMs via API. You want to launch something isolated, as quickly as possible, do the minimal amount of work and not have to throwaway all your state.
Not informational but I kept reading that as 'MicroVMS' which would be a scaled down version of the DEC VMS operating system?!? And I was trying to figure out if they had added containers or something to it.
Does this mean you effectively can't use them as long-lived developer environments? It sounds like even if you suspend them, this is the hard limit on the total time it can run.
It just a time limit of the life of a single MicroVM.
Using this for a long lived "developer environment" would be extraordinarily expensive anyhow. Scaling the vCPU + RAM cost of these to the same shape compute optimized Graviton On-Demand EC2 instance (16 vCPU x 32 GB RAM) shows about 4x the cost.
But I think the point is that they should be cheap to set up, and because of the short life, never really contain anything except the potential to compute when needed, not important data.
EFS is extremely slow for many workloads. We tried it for builds and various other common use cases for coding agents and the performance just isn't there. I'm guessing lots of small random reads/writes just isn't going to ever work well.
I think they have one of the best sandbox environments on the market with pay per utilized resources pricing, it's a huge cost reduction for agentic workloads when you have 95%+ idle CPU time and occasional spikes for CPU heavy work (e.g. agent run tests or something like this).
I use railway to host my openclaw like personal agent for friends and family (9 instances) and it costs like 1-2$/mo with scale to zero.
Interesting, I have recently started working on a project which is similar and fully open source, maybe interesting to some here. Happy to receive any kind of feedback on it.
> Didn't mean to highjack for self advertisement.
>
> As the topic matches, .... my project might be appealing to some here
That's exactly what you intended to do. That is the definition of advertising. It is true, many people might like it, so own it. Don't lie about it, even to yourself.
Can you provide a link to your project? Self-plugs are fairly common around here, and usually appreciated (or at least not frowned upon) when it comes with juicy source code.
Thanks! These tabs render badly on mobile, but you can click on “Functions” to hide it and then click the “MicroVMs” tab to show it.
This pricing model looks very complicated and unfriendly for hobbyists. Maybe it’s cheaper than exe.dev’s $20/month, but I have no idea. I’d have to a complicated calculation based on guesses to tell.
I don't think it's that complicated, but yeah, it's not as simple as $X/month.
The primary difference is that with Lambda you pay by the second, not by the month. According to my math, the break-even point for a 8GB allocation (the minimum exe.dev supplies) would be about 1.65 days of continuous runtime. Less than that, and you're better off with Lambda. More than that, and you're better off with exe.dev (assuming we're just talking about money and not opportunity cost). Lambda allows you to use just 2GB of memory, though, so being more memory efficient would change the break-even point to 6.61 days.
You don’t have to pay that much. I did pay a couple hundred for a while, but not since I switched to Chinese models along with a $20 ChatGPT subscription.
You absolutely can run agents on a regular VM. But if you want to build multi-tenant and multi-agent systems with strong security boundaries, then having a VM or MicroVM per agent session (or session with a group of agents) really simplifies things.
When we did AWS AgentCore Runtime last year we introduced session isolation, with MicroVMs per session. You can think of Lambda MicroVMs as the same stack, but generalized to fit a larger number of application patterns.
Isn’t the point that you wanna be able to spin up and down thousands of VM:s on demand (literally a VM just to run a tool and then shut it down until the next tool call)
This seems roughly similar to Google's Cloud Run gen2 instance types. My understanding is with the second generation, they are running microvms which are bootstrapped from a container image.
Are you guys literally spinning up agents where a 100 ms boot time vs a 3 seconds boot time makes a difference?
I'm asking because I understand the appeal of micro VMs but every time the subject comes up people talk about "isolating agents": what's wrong about isolating agents in a regular VM (or in a container which, itself, is in a VM)?
FWIW I've got my stuff nicely isolated in regular VMs that are regularly up for hours and hours.
It's like the microVMs boots in 100 ms, then the agent does... What? And exits after another 100ms and now you need to launch another one?
What's the use case of "microVMs to isolate agents"?
I imagine you can have a situation where you let an agent run in a shared env but to access certain tools you spin up a VM just for the tool call duration and then shut it down again. Let’s say you wanna allow the agent to write and run code then you need it to run it somewhere safe
Microvms are better for the VM provider. They use less memory and have a smaller attack surface. Also starting in 100ms means you don't need to add a bunch of async machinery when launching the vms.
imagine installing an agent in slack at a company with 1000 employees, and you want each request to have its own VM for data analysis, downloading repos and working on them, ...
regular VMs just use too much memory, a typical ubuntu uses 512 MB as a baseline
Fly.io doesn't set a maximum of 8 hours of alive time on your instance.
Also, MicroVMs can't be exposed directly to the web. Your code running in them can only be executed via API calls with attached auth tokens - so if you wanted to host a public facing API or website with them you'd need to implement your own additional layer in front.
Something I appreciate about Fly (disclaimer: they support my work) is that the pricing is fixed - you pay $1.94/month (less if you suspend your machine) for the smallest instance, up to $976.25/month for the largest (16 CPUs, 128GB) plus predictable costs for volume storage.
The only variable outside your control is bandwidth, and that's unlikely to cause a nasty shock.
Contrast with any of the more "elastic" hosting providers - Vercel, Cloud Run - and you're much less likely to get a horrifying bill if something gets overly-crawled or goes viral.
A way we simply suck at business: we didn't keep beating the drum about this after we wrote the policy up. We just sort of figured everyone read the blog post and moved on. We probably should have been continuously making noise about it.
What you get from having a company made almost entirely of engineers.
To a first approximation everything in this space has dynamic pricing. If it's not priced dynamically, you're presumably paying a premium either on a commit or in gym pricing.
i’d say what AWS released looks closer to a bare compute primitive. E2B is up the stack and ships everything around VM like snapshots, networking, integrations.
also, there’s no lock-in, E2B is open-source and can be hosted on any cloud (AWS included).
plus supports bigger boxes, higher concurrency, longer timeouts (24hr).
1. We support more than 32GB disk (as a shareable device, ideal for agentic memory)
2. We provide egress control
3. We provide vault for secret injection (to counter prompt injection)
4. Snapshot / forking.
5. long lived sandboxes.
They do spike on different features like:
Then there's also the option to use libkrun to run local sandboxes on your own computer. That doesn't scratch the itch for hosted services, but works if your goal is to run agents inside isolated environments for your own work.I've been working on some open-core stuff[1] to coordinate sandboxes, and we're making changes to have a library that lets people coordinate any number of remote or local sandboxes using any provider, kinda like how the Docker CLI works for managing containers, git repos, and coding agents. Flue[2] is another player in this space, and is more of a pure framework, while we're building it as an interactive product for using sandboxed agents and workflows.
[1] https://github.com/gofixpoint/amika/blob/main/ROADMAP.md
[2]: https://flueframework.com/
My personal belief is that the future of an "app" is a combo:
So, it should be stupid simple to run these local sandboxed apps/agents. Right now, not too hard for technical users (esp. with things like https://smolmachines.com/ and https://microsandbox.dev/), but not as easy as clicking an app icon or typing `/path/to/binary` in the CLIAh, the significant compute overhead: https://josecastillolema.github.io/podman-wasm-libkrun/. Much more cpu and ram usage at worse performance.
You'd have to build more of that with libkrun
The core tech of both are great though.
I am quite sure I'm not the only person working on post-firecracker KVM.
That way it can be elastic in CPU, memory and somewhat disk.
How far are you on your take?
Shout out to https://smolmachines.com/ for supporting Vulkan over virtio-gpu/Venus. Currently the best implementation I'm aware of. Unfortunately my use case is running a full desktop inside the VM, and streaming it out over something like Sunshine/Moonlight. For this you need GPU rendering and video encoding. Venus rendering works, but you have to pass the frames back and forth between the host and the guest multiple times which is inefficient. Also Venus doesn't support video encode as far as I can tell.
If you're looking for a thing to google, look up SR-IOV support on (consumer) GPUs.
Also if you're wondering who the customers of these things tend to be, it's generally the CAD market, law firms, etc. If no one's laptop contains sensitive data and can only stream the desktop of a remote system, the loss or theft of an employee's computer isn't nearly the same kind of a security worry.
The startups in this space right now don't provide much value on top of the cloud providers they're wrapping. They don't tend to be run by experienced infra people either so they seem very vibecoded, insecure, janky, etc. They're also significantly overpriced because they're marking up already expensive providers.
Something surprising from my own experience is that while there's certainly a huge role for async agents in cloud sandboxes, async agents running locally seem more useful in many cases.
Though I did know about this one! (Because I saw the announcement.)
Most of the startups are just wrappers around AWS and significantly more expensive.
Agents need sandboxes that are cheaper so that they can run thousands
I feel that AWS, GCP and all the other cloud providers can provide this natively.
But still it would be nice to self host.
The best part of self hosting is that you own it as well, no rug pulls from the laundry list of reselling providers that could go away at anytime.
It would be nice to have a one click sandbox agent on a self hosted instance that is, free, fast (can pay a bit more for more intensive operations) and that is open source.
Part of it might just be that I am old and inflation is catching up with my understanding of prices.
But as far as AWS I still have to say no thanks. Imagine some group actually started using my hosted AI agent service for something compute and network intensive. It could turn into $2000 overnight and if I didn't account for one of the numerous types of AWS charges, I might have only collected $500 for credits purchases.
Or it could easily be ten times that. But who am I kidding. No one is going to use my agents. So it doesn't matter if it's gvisor or Firecracker or whatever.
Firecracker just has a ReSTful unix socket with a defined API and launches KVM vms with limited options.
For custom SMB I still think libvirt is a lower entry cost and may have transferable use cases to longer lived VMs, so you can just launch a qemu microvm[0] and use virsh and/or libvirt xml to set up the networking.
The ~400ms boot time of a qemu microvm vs ~120ms for firecracker may not be an issue for some loads, but qemu will also allow you a bit more density of placement than firecracker. qemu microvms will use a bit more memory individually, but they will also tend to use less real system memory with a larger number of microVMs.
It is all tradeoffs, and kata containers are yet another option that may apply depending on your use case.
You can run your own firecracker or qemu/kvm microvms on most instances that allow nested hypervisors, or on a local host. If cost containment is critical to you this is one possible way forward.
Really it just depends on if you want/need ReSTful control, or need to support short lived serverless functions, or if CLIs fit better and you many want to support full VMs.
They both are just Virtual Machine Monitors that targeted different use cases and decided on different tradeoffs.
Just be careful about hosting traditional containers and microVMs on the same system, that config is going to be problematic do to fundamental reasons that are too complex to properly address here.
[0] https://www.qemu.org/docs/master/system/i386/microvm.html
Daytona, E2B, OpenComputer, Freestyle, Blaxel, Vercel, Modal, Cloudflare, Tensorlake, Superserve, etc. etc.
Some of them work by pre-purchasing credits, so you can control the blast radius of spend.
Also, if you want a more embedded sandbox runtime as a library instead of a daemon + REST API, you can check out libkrun (and friendly layers on top of it like https://microsandbox.dev/ and https://smolmachines.com/)
Standalone gvisor (not the 'do' subcommand) used to be a mess with the OCI json requirement, but recently they began work on presenting their own bwrap interface (likely to pursue AI agent uses) though I wouldn't use it myself yet.
People often look down on gvisor because they think it's some kind of syscall filter, it is not. It can use one of ptrace, seccomp or even KVM to intercept ALL syscalls and service them with it's own logic (which is in Go). Basically it's a VMM and kernel in one.
We run quite a few Slicer instances on mini PCs and Ryzen builds - also on Hetzner (and yes ouch 120 EUR / mo up to ~ 550 EUR / mo for 16core / 128GB RAM feels almost unfair)
This is why I have been avoiding the word sandbox for exe.dev. I don’t think developers agents need something “sandbox” shaped.
https://engine.build/lab/agent-sandboxes
Will add MicroVMs there today (and any others that are missing if you let me know!)
https://taoofmac.com/space/blog/2026/06/18/1845
https://github.com/rcarmo/pve-microvm
I’m building this google3 style mounting to address this.
https://github.com/mohsen1/git-lazy-mount
Still work in progress but for now I am seeing promising results
[0] https://builders.ramp.com/post/why-we-built-our-background-a...
This smells like a competing team building something to capitalize on AI hype, but the product isn't differentiated enough for this to make sense long term. If this was a service called managed AI agents, and you added features specific to AI agents, that has value. But "here's Fargate with a different name" isn't gonna last.
That said, Fargate does kind of seem like a superior option
Edit: I guess this supports suspend and fast resume so invocation time should be somewhat better than Fargate.
https://aws.amazon.com/blogs/aws/firecracker-lightweight-vir... says
> Battle-Tested – Firecracker has been battled-tested and is already powering multiple high-volume AWS services including AWS Lambda and AWS Fargate.
And also, you’ll notice that Fargate takes minutes to launch while Lambda takes a second or less. You’re waiting on AWS to launch a EC2 with your config and pull your containers into it.
(that article matches things I heard from Amazon when I asked why my stuff is slow)
They give a tiny example and insist on micro, fast start, but the say it lasts up to 8 hours and is up to 16 vCPU.
What sort of app require faster boot (than lambda or ec2), but only for a limited interval, and with possibly plenty of processing power...
Maybe I am not the right target, but if you have examples so that I can better appreciate, I'd love that
"A new class of multi-tenant applications has emerged that all share the need to hand each end user their own dedicated execution environment in which to safely run code that the application developer did not write. AI coding assistants, interactive code environments, data analytics platforms, vulnerability scanners, and game servers that run user-supplied scripts all fit this pattern."
Does this mean you effectively can't use them as long-lived developer environments? It sounds like even if you suspend them, this is the hard limit on the total time it can run.
Using this for a long lived "developer environment" would be extraordinarily expensive anyhow. Scaling the vCPU + RAM cost of these to the same shape compute optimized Graviton On-Demand EC2 instance (16 vCPU x 32 GB RAM) shows about 4x the cost.
So don't do that. Just use an EC2 instance.
But I think the point is that they should be cheap to set up, and because of the short life, never really contain anything except the potential to compute when needed, not important data.
You just have to finish development in 8 hours.
then when you launch the next one, its like you are still there?
beamshell microvm deploy && beamshell microvm run
I think they have one of the best sandbox environments on the market with pay per utilized resources pricing, it's a huge cost reduction for agentic workloads when you have 95%+ idle CPU time and occasional spikes for CPU heavy work (e.g. agent run tests or something like this).
I use railway to host my openclaw like personal agent for friends and family (9 instances) and it costs like 1-2$/mo with scale to zero.
https://github.com/mitos-run/mitos
That's exactly what you intended to do. That is the definition of advertising. It is true, many people might like it, so own it. Don't lie about it, even to yourself.
This pricing model looks very complicated and unfriendly for hobbyists. Maybe it’s cheaper than exe.dev’s $20/month, but I have no idea. I’d have to a complicated calculation based on guesses to tell.
The primary difference is that with Lambda you pay by the second, not by the month. According to my math, the break-even point for a 8GB allocation (the minimum exe.dev supplies) would be about 1.65 days of continuous runtime. Less than that, and you're better off with Lambda. More than that, and you're better off with exe.dev (assuming we're just talking about money and not opportunity cost). Lambda allows you to use just 2GB of memory, though, so being more memory efficient would change the break-even point to 6.61 days.
Also, a single VM is pretty limiting.
When we did AWS AgentCore Runtime last year we introduced session isolation, with MicroVMs per session. You can think of Lambda MicroVMs as the same stack, but generalized to fit a larger number of application patterns.
Are you guys literally spinning up agents where a 100 ms boot time vs a 3 seconds boot time makes a difference?
I'm asking because I understand the appeal of micro VMs but every time the subject comes up people talk about "isolating agents": what's wrong about isolating agents in a regular VM (or in a container which, itself, is in a VM)?
FWIW I've got my stuff nicely isolated in regular VMs that are regularly up for hours and hours.
It's like the microVMs boots in 100 ms, then the agent does... What? And exits after another 100ms and now you need to launch another one?
What's the use case of "microVMs to isolate agents"?
So that leaves faster boot times.
Faster boot times and then the agent does what? And at how many token/s? And what's the "time to first token" anyway?
How do the time to first token and then the token/s inherent limitations of LLMs not totally dominate the running time?
I just don't get the use case.
regular VMs just use too much memory, a typical ubuntu uses 512 MB as a baseline
I think it's designed for building an image once and then reusing it many, many times.
Which is more cheaper for me?
Ideally maybe self hosting would be better?
Also, MicroVMs can't be exposed directly to the web. Your code running in them can only be executed via API calls with attached auth tokens - so if you wanted to host a public facing API or website with them you'd need to implement your own additional layer in front.
Something I appreciate about Fly (disclaimer: they support my work) is that the pricing is fixed - you pay $1.94/month (less if you suspend your machine) for the smallest instance, up to $976.25/month for the largest (16 CPUs, 128GB) plus predictable costs for volume storage.
The only variable outside your control is bandwidth, and that's unlikely to cause a nasty shock.
Contrast with any of the more "elastic" hosting providers - Vercel, Cloud Run - and you're much less likely to get a horrifying bill if something gets overly-crawled or goes viral.
https://fly.io/blog/accident-forgiveness/
A way we simply suck at business: we didn't keep beating the drum about this after we wrote the policy up. We just sort of figured everyone read the blog post and moved on. We probably should have been continuously making noise about it.
What you get from having a company made almost entirely of engineers.
https://sprites.dev
also, there’s no lock-in, E2B is open-source and can be hosted on any cloud (AWS included).
plus supports bigger boxes, higher concurrency, longer timeouts (24hr).
disclaimer: i work at E2B
will have a hosted platform soon with GPU support (vulkan)
Apart from the above features.
Everything supported in APIs and CLI for agents.Can be used via - npx skills add instavm/skills