Would love to understand how you compare to other providers like Modal, Daytona, Blaxel, E2B and Vercel. I think most other agent builders will have the same question. Can you provide a feature/performance comparison matrix to make this easier?
I'm working on an article deep diving into the differences between all of us. I think the goal of Freestyle is to be the most powerful and most EC2 like of the bunch.
I haven't played around with Blaxel personally yet.
E2B/Vercel are both great hardware virtualized "sandboxes"
Freestyle VMS are built based on the feedback our users gave us that things they expected to be able to do on existing sandboxes didn't work. A good example here is Freestyle is the only provider of the above (haven't tested blaxel) that gives users access to the boot disk, or the ability to reboot a VM.
Fly.io sprites is the most similar to us of the bunch. They do hardware virtualization as well, have comparable start times and are full Linux. What we call snapshots they call checkpoints.
The big pros of Sprites over us is their advanced networking stack and the Fly.io ecosystem. The big cons are that Sprites are incredibly bare bones — they don't have any templating utilities. I've also heard that Sprites sometimes become unavailable for extended periods of time.
The big pros of Freestyle over Sprites is fork, advanced templating, and IMO a better debugging experience because of our structure.
Thanks for the thoughtful response. I'm predominantly a self-hoster, but I think your product makes a lot of sense for a wide variety of users and businesses. I'm excited to try out freestyle!
Freestyle/other providers will likely provide better debugging experience but thats something you can probably get past for a lot of workloads.
The time when you/anyone should think about Freestyle/anyone is when the load spikes/the need to create hundreds of VMs in short spikes shows up, or when you're looking for some of the more complex feature sets any given provider has built out (forks, GPUs, network boundaries, etc).
I also highly recommend self hosting anything you do outside of your normal VPC. Sandboxes are the biggest possible attack surface and it is a feature of us that we're not in your cloud; If we mess up security your app is still fine.
Obviously your service/approach is different than exe, more like sprites but like you said more targeted/opinionated to AI coding/sandboxing tasks it looks like. Interesting space for sure!
I built something like this at work using plain Docker images. Can you help me understand your value prop a little better?
The memory forking seems like a cool technical achievement, but I don't understand how it benefits me as a user. If I'm delegating the whole thing to the AI anyway, I care more about deterministic builds so that the AI can tackle the problem.
So first MicroVM != Container, and container is not a secure isolation system. I would not run untrusted containers on your nodes without extra hardening.
The memory forking was originally invented because for AI App Builders and first response driven applications its extremely important that they are instant (difference between running bun dev and the dev server already being running).
However its much more generally applicable, Postgres is a great example of this. You can't fork the filesystem under postgres and get consistency. Same thing with a browser state, a weird server state, or anything that exists in memory. The memory forking gives a huge performance boost while snapshotting whats actually going on at one instant.
Agreed, the thing I'd be most interested in is the isolated execution environment you mentioned. Agents running autopilot are powerful. Agents running unsupervised on a machine with developer permissions and certificates where anything could influence the agent to act on an attacker's behalf is terrifying
I recommend running the agent harness outside of the computer. The mental model I like to use is the computer is a tool the agent is using, and anything in the computer is untrusted.
So isolation is correct. Forking a sandbox gives you multiple exact duplicates of isolated environments.
When your coding agent has 10 ideas for what to do, to evaluate them correctly it needs to be able to evaluate them in isolation.
If you're building a website testing agent and halfway down a website, with a form half filled out a session ongoing, etc and it realizes it wants to test 2 things in isolation, forking is the only way.
We also envision this powering the next generation of devcycles "AI Agent, go try these 10 things and tell me which works best". AI forks the environment 10 times, gets 10 exact copies, does the thing in each of them, evaluates it, then takes the best option.
Yep I can see this especially when the agent is spinning up test servers/smokes and you don't want those conflicting. How do we reconcile all the potential different git hashes though, upstream I guess etc (this might be an easy answer and I'm not super proficient with git so forgive)
So we recommend branch per fork, merge what you like.
You have to change the branch on each fork individually currently and thats unlikely to change in the short term due to the complexity of git internals, but its not that hard to do yourself `git checkout -b fork-{whateverDiscriminator}`
The technical challenges in getting memory forking to deliver those sub-second start and fork times are significant. I've seen the pain of trying to achieve that level of state transfer and rapid provisioning. While "EC2-like" gets the point across for many, going bare metal reveals the practical limits of cloud virtualization for high-performance, complex workloads like these. It shows a real understanding of where cloud abstraction helps and where it just adds overhead.
The cost argument for owning the hardware for this specific use case also makes sense, considering the scale these agent environments will demand. Also worth noting, sandboxes are effectively an open attack surface; architecting them not to be in your main VPC is a sound security decision from the start.
It doesn't seem very easy to calculate how much it would cost per month to keep a mostly-idle VM running (for example, with a personal web app). The $20/month plan from exe.dev seems more hobbyist-friendly for that. Maybe that's not the intended use, though?
Cool! I've been using your API for running sandboxed JS. Nice to see you also support VMs now.
> we mean forking the whole memory of it
How does this work? Are you copying the entire snapshot, or is this something fancy like copy-on-write memory? If it's the former, doesn't the fork time depend on the size of the machine?
We're using copy on write with the memory itself. Fork time is completely decoupled from the size of the machine.
Creating snapshots takes a 2-4 second interruption in the VM due to sheer IO that we didn't want here.
Whats especially cool about this approach is not only is fork time O(1) with respect to machine size, but its also O(1) with respect to the amount of forks.
We're working on a similar solution at UnixShells.com [1]. We built a VMM that forks, and boots, in < 20ms and is live, serving customers! We have a lot of great tools available, via MIT, on our github repo [2] as well!
I have so many interesting problems on Ai, sandboxing isn't one of them. It's a pointless excercise yet disproportionately so many people love to to do this. Probably because sandboxing doesn't feel as magic as Agents itself and more like the old times of "traditional" software development.
Believe it or not, once you start working for a regulated industry, it is all you would ever think of. There, people don't care if you are vibing with the latest libraries and harnesses or if it's magic, they care that the entire deployment is in some equivalent of a Faraday cage. Plus, many people just don't appreciate it when their agents go rm -rf / on them.
It is a mostly pointless exercise if the goal is trying to contain negative impact of AI agents (e.g. OpenClaw).
It is a very necessary building block for many common features that can be steered in a more deterministic way, e.g. "code interpreter" feature for data analysis or file creation like commonly seen in chat web UIs.
With respect to the market, every single sandbox sucks. I'm not gonna shit talk competitors but there is not a good sandboxing platform out there yet — including me — compared to where we'll be in 6 months.
We've heard all the platforms have consistent uptime, feature completeness, networking and debugging issues. And in our own platform we're not 1/10ths of the way through solving the requests we've gotten.
Next generation of Agents needs computers, and those computers are gonna look really different than "sandboxes" do today.
I don't think you're wrong, but if you really want to really re-think the approach, building an orchestration layer for Firecracker like every other company in the space is doing is probably not it.
Would love to understand how you compare to other providers like Modal, Daytona, Blaxel, E2B and Vercel. I think most other agent builders will have the same question. Can you provide a feature/performance comparison matrix to make this easier?
I'm working on an article deep diving into the differences between all of us. I think the goal of Freestyle is to be the most powerful and most EC2 like of the bunch.
Daytona runs on Sysbox (https://github.com/nestybox/sysbox) which is VM-like but when you run low level things it has issues.
Modal is the only provider with GPU support.
I haven't played around with Blaxel personally yet.
E2B/Vercel are both great hardware virtualized "sandboxes"
Freestyle VMS are built based on the feedback our users gave us that things they expected to be able to do on existing sandboxes didn't work. A good example here is Freestyle is the only provider of the above (haven't tested blaxel) that gives users access to the boot disk, or the ability to reboot a VM.
I'd also be interested in a comparison with exe.dev which I'm currently using.
And fly.io sprites
Fly.io sprites is the most similar to us of the bunch. They do hardware virtualization as well, have comparable start times and are full Linux. What we call snapshots they call checkpoints.
The big pros of Sprites over us is their advanced networking stack and the Fly.io ecosystem. The big cons are that Sprites are incredibly bare bones — they don't have any templating utilities. I've also heard that Sprites sometimes become unavailable for extended periods of time.
The big pros of Freestyle over Sprites is fork, advanced templating, and IMO a better debugging experience because of our structure.
Thanks for the thoughtful response. I'm predominantly a self-hoster, but I think your product makes a lot of sense for a wide variety of users and businesses. I'm excited to try out freestyle!
Self hosting can be doable for constant small/medium size workloads
You can handroll a lot with: https://github.com/nestybox/sysbox?tab=readme-ov-file https://gvisor.dev https://github.com/containers/bubblewrap?tab=readme-ov-file
For hardware virtualized machines it much harder but you can do it via: https://github.com/firecracker-microvm/firecracker/ https://github.com/cloud-hypervisor/cloud-hypervisor
Freestyle/other providers will likely provide better debugging experience but thats something you can probably get past for a lot of workloads.
The time when you/anyone should think about Freestyle/anyone is when the load spikes/the need to create hundreds of VMs in short spikes shows up, or when you're looking for some of the more complex feature sets any given provider has built out (forks, GPUs, network boundaries, etc).
I also highly recommend self hosting anything you do outside of your normal VPC. Sandboxes are the biggest possible attack surface and it is a feature of us that we're not in your cloud; If we mess up security your app is still fine.
This is what I do (my project) for self hosting on a VPS/server:
https://GitHub.com/jgbrwn/vibebin
Also I'm a huge proponent of exe.dev
Obviously your service/approach is different than exe, more like sprites but like you said more targeted/opinionated to AI coding/sandboxing tasks it looks like. Interesting space for sure!
I built something like this at work using plain Docker images. Can you help me understand your value prop a little better?
The memory forking seems like a cool technical achievement, but I don't understand how it benefits me as a user. If I'm delegating the whole thing to the AI anyway, I care more about deterministic builds so that the AI can tackle the problem.
So first MicroVM != Container, and container is not a secure isolation system. I would not run untrusted containers on your nodes without extra hardening.
The memory forking was originally invented because for AI App Builders and first response driven applications its extremely important that they are instant (difference between running bun dev and the dev server already being running).
However its much more generally applicable, Postgres is a great example of this. You can't fork the filesystem under postgres and get consistency. Same thing with a browser state, a weird server state, or anything that exists in memory. The memory forking gives a huge performance boost while snapshotting whats actually going on at one instant.
I’m super interested since it seems like you have given everything a lot of thought and effort but I am not sure I understand it.
When I’m thinking of sandboxes, I’m thinking of isolated execution environments.
What does forking sandboxes bring me? What do your sandboxes in general bring me?
Please take this in the best possible way: I’m missing a use case example that’s not abstract and/or small. What’s the end goal here(
Agreed, the thing I'd be most interested in is the isolated execution environment you mentioned. Agents running autopilot are powerful. Agents running unsupervised on a machine with developer permissions and certificates where anything could influence the agent to act on an attacker's behalf is terrifying
I recommend running the agent harness outside of the computer. The mental model I like to use is the computer is a tool the agent is using, and anything in the computer is untrusted.
So isolation is correct. Forking a sandbox gives you multiple exact duplicates of isolated environments.
When your coding agent has 10 ideas for what to do, to evaluate them correctly it needs to be able to evaluate them in isolation.
If you're building a website testing agent and halfway down a website, with a form half filled out a session ongoing, etc and it realizes it wants to test 2 things in isolation, forking is the only way.
We also envision this powering the next generation of devcycles "AI Agent, go try these 10 things and tell me which works best". AI forks the environment 10 times, gets 10 exact copies, does the thing in each of them, evaluates it, then takes the best option.
Yep I can see this especially when the agent is spinning up test servers/smokes and you don't want those conflicting. How do we reconcile all the potential different git hashes though, upstream I guess etc (this might be an easy answer and I'm not super proficient with git so forgive)
So we recommend branch per fork, merge what you like.
You have to change the branch on each fork individually currently and thats unlikely to change in the short term due to the complexity of git internals, but its not that hard to do yourself `git checkout -b fork-{whateverDiscriminator}`
The technical challenges in getting memory forking to deliver those sub-second start and fork times are significant. I've seen the pain of trying to achieve that level of state transfer and rapid provisioning. While "EC2-like" gets the point across for many, going bare metal reveals the practical limits of cloud virtualization for high-performance, complex workloads like these. It shows a real understanding of where cloud abstraction helps and where it just adds overhead.
The cost argument for owning the hardware for this specific use case also makes sense, considering the scale these agent environments will demand. Also worth noting, sandboxes are effectively an open attack surface; architecting them not to be in your main VPC is a sound security decision from the start.
how many seconds to provision are we talking about here? 1 sec vs 60 is a dealbreaker for me, some clarity on that would be nice.
500ms. Less than 1 second. We're aiming to get that down to 200ms in the next 3 months.
It doesn't seem very easy to calculate how much it would cost per month to keep a mostly-idle VM running (for example, with a personal web app). The $20/month plan from exe.dev seems more hobbyist-friendly for that. Maybe that's not the intended use, though?
We're not going after hobbyists. We're building the platform for companies like exe.dev to build on. Thats why its all usage based.
That said, our $50 a month plan can be used as an individual for your coding agents, but I wouldn't recommend it.
Ooof, if you are the middleman platform then it's sure gonna get expensive for the end user
Cool! I've been using your API for running sandboxed JS. Nice to see you also support VMs now.
How does this work? Are you copying the entire snapshot, or is this something fancy like copy-on-write memory? If it's the former, doesn't the fork time depend on the size of the machine?We're using copy on write with the memory itself. Fork time is completely decoupled from the size of the machine.
Creating snapshots takes a 2-4 second interruption in the VM due to sheer IO that we didn't want here.
Whats especially cool about this approach is not only is fork time O(1) with respect to machine size, but its also O(1) with respect to the amount of forks.
Interesting!
We're working on a similar solution at UnixShells.com [1]. We built a VMM that forks, and boots, in < 20ms and is live, serving customers! We have a lot of great tools available, via MIT, on our github repo [2] as well!
[1] https://unixshells.com
[2] https://github.com/unixshells
Your pricing page is broken
Reviewing this now. our public pricing at www.freestyle.sh/pricing seems to be working, can you point me in a more specific direction?
I have so many interesting problems on Ai, sandboxing isn't one of them. It's a pointless excercise yet disproportionately so many people love to to do this. Probably because sandboxing doesn't feel as magic as Agents itself and more like the old times of "traditional" software development.
Believe it or not, once you start working for a regulated industry, it is all you would ever think of. There, people don't care if you are vibing with the latest libraries and harnesses or if it's magic, they care that the entire deployment is in some equivalent of a Faraday cage. Plus, many people just don't appreciate it when their agents go rm -rf / on them.
It is a mostly pointless exercise if the goal is trying to contain negative impact of AI agents (e.g. OpenClaw).
It is a very necessary building block for many common features that can be steered in a more deterministic way, e.g. "code interpreter" feature for data analysis or file creation like commonly seen in chat web UIs.
Yeah, idk I guess it’s interesting if you are an engineer looking for something to do,
But like I see multiple sandbox for agents products a week. Way too saturated of a market
I disagree (as a sandboxing company).
With respect to the market, every single sandbox sucks. I'm not gonna shit talk competitors but there is not a good sandboxing platform out there yet — including me — compared to where we'll be in 6 months.
We've heard all the platforms have consistent uptime, feature completeness, networking and debugging issues. And in our own platform we're not 1/10ths of the way through solving the requests we've gotten.
Next generation of Agents needs computers, and those computers are gonna look really different than "sandboxes" do today.
I don't think you're wrong, but if you really want to really re-think the approach, building an orchestration layer for Firecracker like every other company in the space is doing is probably not it.