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Show HN: Katakate – Dozens of VMs per node for safe code exec

Show HN: Katakate – Dozens of VMs per node for safe code exec

28 comments

·October 21, 2025

I've built this to make it easy to host your own infra for lightweight VMs at large scale.

Intended for exec of AI-generated code, for CICD runners, or for off-chain AI DApps. Mainly to avoid Docker-in-Docker dangers and mess.

Super easy to use with CLI / Python SDK, friendly to AI engs who usually don't like to mess with VM orchestration and networking too much.

Defense-in-depth philosophy.

Would love to get feedback (and contributors: clear & exciting roadmap!), thx

gbxk

Thanks everyone for the amazing feedback and discussion!

For anyone curious:

– Docs: https://docs.katakate.org

- LangChain Agent tutorial: https://docs.katakate.org/guides/langchain-agent

It's getting late where I am, so I'm heading to bed — looking forward to replying to any new comments tomorrow!

mentalgear

I would really like to see a good local sandboxing solution in this space, something that is truly local-first. This is especially important since many coding models / agentic builders will eventually become lightweight enough to run them on-device instead of having to buy tokens and share user data with big LLM cloud providers.

dloss

Anthropic recently released a sandboxing tool based on bubblewrap (Linux, quite lightweight) and sandbox-exec (macOS). https://github.com/anthropic-experimental/sandbox-runtime

I wonder if nsjails or gVisor may be useful as well. Here's a more comprehensive list of sandboxing solutions: https://github.com/restyler/awesome-sandbox

gbxk

wow that's super new! Thanks for that, will look deeply into it and compare

elric

Are there any such solutions that can adequately protect against side-channel attacks (à la rowhammer, meltdown, spectre, ...)? I mean protecting local file access and network access is pretty easy, but side-channels and VM escaping attacks seem like a bigger concern.

gbxk

That's an interesting direction! TEE support would be relatively straightforward with current stack (and it's on my roadmap), so that could be a first step forward.

ATechGuy

Side-channel attacks apply to multi-tenant cloud environments, not local.

elric

That seems like a naive take. If any of your local VMs are internet connected and are compromised, side channel attacks could be used to exfiltrate data from other VMs or the host.

gbxk

(sorry I didn't reply in-thread, I'm new to HN, re-posting response here:)

Exactly! The main local requirement is to have hardware virtualization available (e.g. /dev/kvm) but that should be fine on your local linux machine. Won't work in cloud machines or on Mac ARM in current form but maybe if I extend

ofrzeta

There are some providers that offer KVM nested virtualization, I think Google Cloud, Digital Ocean ... any others?

gbxk

True! GCP does. I haven't tested it yet. I didn't know D.O does. If anyone knows others, I'm interested too!

Bnjoroge

Great project. There's been multiple approaches/tools in the space(top of my head I can think of e2b, arrakis, claude's new tool). how is this different?

alexeldeib

as someone in the space this ticks a lot of boxes: kubernetes-native, strong isolation, python sdk (ideal for ML scenarios). devmapper is a nice ootb approach.

Glancing at the readme, is your business model technical support? Or what's your plan with this?

Anything interesting to share around startup time for large artifacts, scaling, passing through persistent storage (or GPUs) to these sandboxes?

Curious what things like 'Multi-node cluster capabilities for distributed workloads' mean exactly? inter-VM networking?

gbxk

No business model short-term. My goal is broad adoption, 100% open-source.

By multi-node I mean so far I only support 1 k8s node, i.e. 1 machine, but soon adding support for multiple. Still, on 20 CPUs I can run +50 VM pods with fractional vCPU limits.

For GPU passthrough: not possible today because I use Firecracker as VMM. On roadmap: Add support for Qemu, then GPU passthrough possible.

Inter-VM networking: it's already possible on single-node: 1 VM = 1 pod. Can have multiple pods per node (have a look at utils/stress-test.sh). Right now I default deny-all ingress for safety (because by default k8s allows inter pod communication), but can make ingress configurable.

Startup time: a second, or a few seconds, depending on which base image (alpine, ubuntu, etc...) and whether you use a before_script or not (what I execute before the network lockdown)

Large artifacts: you can configure resource allocated to a VM pod in the sandbox config and it basically uses k8s resource limits.

Let me know if any other question! Happy to help

yjftsjthsd-h

> No business model short-term. My goal is broad adoption, 100% open-source.

IMHO that's kind of a red flag. There's a happy path here where it's successful but stays low-maintenance enough that you just work on it in your spare time, or it takes of and gets community support, or you get sponsorships or such. But there's also an option where in a year or two it becomes your job and you decide to monetize by rug-pulling and announce that actually paying the bills is more important than staying 100% open source. Not a dig at you, just something that's happened enough times that I get nervous when people don't have a plan and therefore don't have a plan to avoid the outcome that creates problems for users.

gbxk

Sure one day if it really kicks off I could think of offering additionally a SaaS solution with paid enterprise features like SOC 2 compliance, RBAC, multiple clouds supported, etc. Why not. But I strongly believe that for it to be successful, it needs a strong open-source base. Then, billing huge companies for compliance features or huge usage makes sense. That would support development of the open-source part too.

I like the Docker model, for instance: free for companies under 250 employees or $10m/y revenue.

In any case, it will always be open-source.

Those paid enterprise features wouldn't come from closed-source: they would come from compliance of a particular SaaS-offered infra setup, that anybody else could reproduce. Just like HuggingFace.

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ed_mercer

Why do I need this if I already have containers and k8s for running agents?

gbxk

It is well known that containers do not provide you safe isolation. It is not their purpose. They share kernel and page cache with the host. Any kernel exploit gives to someone in a container potential root control of the host (see DirtyPipe, DirtyCow). That's why you need VM-level isolation.

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empath75

This seems like an amazing stack that ticks a lot of boxes for me, but I really dislike cli or a custom api as the UX for this and would prefer to manage all of this with CRDs so i can just use the k8s client for everything.

gbxk

Actually you can! After you run "k7 install" you'll have a k3s cluster up and running, with Kata as a runtime class, and Firecracker specified in Kata config. So nothing prevents you from hitting the Kubernetes API! kubectl will work.

Note: I use k3s' internal kubectl and containerd, to avoid messing with your own if you have some already installed. That means you can run commands like "k3s kubectl ..."

And thank you for the compliments on the stack.

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gbxk

If you have any suggestion on how I can make this more friendly UX-wise to your personal usage, I am most interested to hear! And this will shape my roadmap.

whalesalad

From an outside perspective, this looks silly. Like fitting a square peg in a round hole. But I do ack "what if we could run vm's as easily as we run containers" use case and atm it seems like things like this (and katacontainers) are the only ways to do it. Wondering a few things: do all the layers of abstraction make things brittle and how is performance impacted?

gbxk

It uses Kata with Firecracker which gives you as light of a boot as it gets. Subsecond booting for instance is accessible with a lighter rootfs, which is also on the roadmap (one of the easiest items, actually). The k8s layer doesn't add overhead either compared to any other VM. If you want to compare to bare containers, depending on workload, you could see a 5% overhead due to virtualization. Exact overhead would depend on workload.

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