---
title: "We Raised $6M to Build the Future of (AI) Cloud Infrastructure"
description: "Today, and after years of hard work, we’re ecstatic to announce the launch of Unikraft Cloud, a radical, new cloud platform based on years of research and providing exponentially better scalability and efficiency (think running millions of strongly isolated instances on a few servers instead of an entire datacenter). In support of this, we’re also super proud to announce our latest $6M seed raise with Heavybit as lead investor, with the participation of Vercel Ventures, Mango Capital, Firestreak Ventures, Fly VC and First Momentum Ventures, along with a host of world class angels."
doc_version: "1.0"
last_updated: "2025-10-10"
---

Today, and after years of hard work, we’re ecstatic to announce the launch of
Unikraft Cloud, a radical, new cloud platform based on years of research and
providing exponentially better scalability and efficiency (think running
millions of strongly isolated instances on a few servers instead of an entire
datacenter). In support of this, we’re also super proud to announce our latest
$6M seed raise with Heavybit as lead investor, with the participation of Vercel
Ventures, Mango Capital, Firestreak Ventures, Fly VC and First Momentum
Ventures, along with a host of world class angels.

With that in place, it might be worth saying a few words about where we started.
It may seem antithetical for a startup to say that our journey began close to 10
years ago: certainly our path hasn't been a typical one to say the least (and
I'm not even sure it's been one I'd recommend either!).

We come from a research and open source background, back then looking into how
to make [software packet processing fast in
software](https://dl.acm.org/doi/10.1145/2774993.2775065), at 10-40Gb/s (this
used to be blazingly fast!); this work was a precursor to the now
well-established Intel DPDK framework. From there, being the optimization geeks
we are, we started moving towards doing packet processing within a virtual
machine efficiently, essentially creating an operating system whose only purpose
was to do packet processing, and wrapping a virtual machine around that. For
those familiar with the Xen hypervisor, the beginnings of this we did with
[mini-os](https://wiki.xenproject.org/wiki/Mini-OS-DevNotes), a very simple,
reference OS implementation to which we slapped the [Click modular router
software](https://github.com/kohler/click) out of MIT, something we ended up
calling
[ClickOS](https://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-martins.pdf)
-- long story short, this sort of work eventually became known as Network
Function Virtualization.

As we started getting into virtualization, this got us thinking about cloud
infrastructure and how it was built: no-one could argue, even back then, against
the power of the cloud and its amazing functionality, but was it built
efficiently, and did, and do, its components scale well, without having to throw
obscene amounts of money, hardware, and electricity at the problem?

The answer for us back then, and still today, is a resounding no, and the
relatively recent explosion in AI agents and AI-generated workloads is putting
severe strain on how scalable legacy cloud infra actually is (again, without
throwing silly amounts of money at the problem).

But I'm getting ahead of myself; getting back to the story of our beginnings,
our first port of call when it came to building a radically more efficient and
scalable cloud infra platform were the images themselves: GB-sized images being
deployed to run applications that only needed MBs to run -- no wonder virtual
machines (VMs) had (still have?) a reputation for being chunky and resource
hungry.

Debunking the myth that VMs, by definition, are heavyweight, was one of our
initial missions; we went as far as publishing a paper at SOSP, the top systems
conference in the world, cheekily titled "[My VM is Faster (and Safer) than your
Container](https://dl.acm.org/doi/pdf/10.1145/3132747.3132763)", to try to
highlight that VMs need not be heavyweight, and that containers are not safe for
production deployments in the cloud (that seems obvious today, but it was far
from it back then); in that work we were getting VMs to cold start in as little
as 2 milliseconds, roughly comparable to `fork`/`exec` on Linux.

![](/images/blog/my-vm-is-lighter-and-safer-than-your-container.png)

To make VMs small, a key insight was that for cloud deployments, we know, at
build time, what the target service or application will be, so we shouldn't be
using general-purpose operating systems like Linux that can run anything.
Likewise, why use extremely bloated distributions that come with everything but
the kitchen sink, when all I might want to do is run a simple Python script? And
in case you're wondering why we were, and still are, so bullish about VMs as the
cloud's workhorse: it's the only tech that provides the strong, hardware-level
isolation that is required for production workloads in the cloud (no, containers
aren't a good solution for production, and in public clouds they're more often
than not deployed within a VM; and don't even get me started on isolates and
other language-level isolation primitives).

So the goal was to be able to easily create efficient VMs tailored to specific
target applications; the technical term for such specialized VMs is
**unikernels**. For those who have heard of them, unikernels are famous for
providing great performance metrics (start times in milliseconds, images
consisting of just a few MBs, faster and more efficient I/O, etc), but infamous
for being difficult to build and generally use. To tackle this problem, back in
2018 or so we created the [Linux Foundation Unikraft](https://unikraft.org/)
open source project with the explicit goal of making it easy to build efficient
VMs without having to modify apps nor having to have any knowledge about
operating systems, virtualization nor unikernels -- and to integrate it all with
familiar tooling such as Dockerfiles, Kubernetes and Prometheus.

## Building the Next (R)evolution in Cloud Infrastructure Platforms

Fast forward several years and we decided that the open source project was
mature enough to start building a commercial offering around it, so in 2022 we
partnered up with US and EU investors and created a startup.

Naively, at first, we thought we could simply wrap our unikernels in popular
public cloud formats like AMIs, deploy them on AWS, and expect performance and
efficiency to go significantly up. What in retrospect seems obvious, is that
when we did so almost nothing changed: when we hit a button to start an instance
on the AWS dashboard, the instance would take seconds to start, not the
single-digit milliseconds we were used to on our local boxes; and then I/O
performance, memory consumption, etc, would be nowhere near our local
benchmarking.

After some level of performance debugging, and understanding the underlying
infra, it became fairly clear that if we wanted to build a platform that could
be reactive to traffic in milliseconds, and that could scale to hundreds of
thousands or millions of (strongly-isolated, VM-based) instances on a single
server we'd have to go back to first principles, and revisit all of the main,
basic components of a cloud infrastructure platform.

To say that this has been a large endeavor is putting it mildly: building
controllers, proxies, snapshotting mechanisms and a number of other components
from scratch has been a major, incredibly challenging (and if we're honest fun!)
undertaking, consuming the better part of 18 months of heavy engineering to get
there -- in addition to all the prior years of work described so far in this
story.

The result is Unikraft Cloud, a platform where all workloads are strongly
isolated by default, that can cold start and scale-to-zero any workload in < 10
milliseconds, and that can scale to 100K+ instances on a single server (and
we're pushing this scalability further everyday). Think running millions of
instances on a few servers rather than a data center, all of it based on
familiar tooling such as Docker and Kubernetes..

## A Platform for AI Scale

Why does this matter for AI? AI workloads (agents, headless browsers,
AI-generated code, etc.) have three main characteristics in terms of deployment:

1. Massive scale: millions of agents or functions generated in short periods of
   time -- and we're only at the beginning of the growth curve.
2. Untrusted code: increasingly code is being generated by AI for
   non-programmers who can't be expected to test it for vulnerabilities or
   issues; in the future, ostensibly most of the deployed code will have been
   (vibe) "coded" by non-programmers.
3. Unpredictable: Rapidly changing workload patterns mean that legacy cloud
   platforms must leave (idle) instances on all the time -- massive scale
   requires massive infrastructure scale (and costs!)

Unikraft Cloud is built from the ground up to cope with the massive scale and
unpredictability of such workloads since it can, just-in-time, in milliseconds,
start instances as requests for them arrive, without end-users ever noticing
instances were scaled down to zero. And since the basic unit of work on the
platform is a virtual machine, strong isolation is a non-negotiable, hard-coded
default. In short, the basic characteristics of Unikraft Cloud are:

- Minimal cloud stack: the images contain your application, and little else --
  no more crud.
- Super fast starts: in 10ms or less -- death to cold starts
- Massive server density: 100K+ instances per server -- no more idling
- Unit economics: offer services at a fraction of the cost -- outpace the
  competition
- Zero compromises: run any service with strong isolation -- no hidden gotchas
- No tooling changes:  use Docker, Kubernetes, Prometheus -- all the standard
  tools you know and love

I should also note what may seem obvious: while the platform is clearly an
amazing fit for AI workloads, it is application-agnostic, and we have found it
to be equally powerful in use cases as diverse as databases, build pipelines,
functions, remote IDEs, ETL pipelines, and logging and monitoring, to name a
few. Basically anything that needs scale, blazingly fast scale to zero, or any
workloads where the ratio of the time it takes for an app to start and it doing
useful work is poor (CI/CD pipelines I'm looking at you) our platform is perfect
for.

But don't take our word for it, sign up for free and take it up for a spin -- or
drop me a line, I'd be happy to talk about how Unikraft Cloud may help you, and,
if you're curious, to tell you more details about the tech behind it. Oh, and if
you love big engineering challenges and would like to help build the next
generation of cloud infra, check out our careers page -- we have some of the top
minds in systems engineering, so we guarantee you will never be bored. Either
way, let's chat!

## Sitemap

- [Home](https://unikraft.com/)
- [How It Works](https://unikraft.com/how-it-works)
- [AI](https://unikraft.com/ai)
- [Pricing](https://unikraft.com/pricing)
- [Customer Stories](https://unikraft.com/customer-stories)
- [Blog](https://unikraft.com/blog)
- [Documentation](https://unikraft.com/docs)
- [Glossary](https://unikraft.com/glossary.md)
- [Contact](https://unikraft.com/contact)