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Munito AI (YC F26)

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@munitoai

Open-model inference provider for Europe, private by default. Open and independent intelligence, behind an API you already know. Backed by @ycombinator
Paris, France munito.ai Joined September 2026
27 Following 38 Followers 19 Posts

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Munito AI (YC F26) Verified account @munitoai
Munito Sandboxes is in public preview: a cloud computer on demand for each task, on bare metal in Europe.

A sandbox is a Linux microVM with its own kernel, its own disk and its own network.

What you get
→ Docker inside, SSH, and a URL for any port
→ Pause, resume and fork, so an experiment branches instead of starting over
→ A desktop when the agent needs eyes and hands
→ Internet access stays off until you turn it on
→ 1,000 of them started in about a second, each with its own kernel

What it costs
→ $0.0072 per vCPU-hour and $0.0009 per GiB-hour, up to 8 vCPUs and 16 GiB
→ So a 2 vCPU, 4 GiB sandbox is $0.0180 an hour, against $0.1656 at @e2b and at @daytonaio
→ Billed per second while it runs. A paused sandbox pays for no time, and a persistent one pays only for its saved state, $0.0002 per GiB-hour

That is about 90% less for CPU and memory.

Start at docs.munito.ai/sandboxes
docs.munito.ai Sandboxes · Munito AI Start a Linux microVM for your code, run commands in it, and read and write its files.
Alexey Verified account @alexeyeryshev 4d
I'm a founder @ycombinator. My inference optimization loop needed hundreds of cloud computers at once, so I built them.

This is the third complex large scale system I've built during the batch. But who's counting!

Today it's a product: Munito Sandboxes, in public preview. Cloud computers on demand, a Linux microVM for each task, on bare metal in Europe.

@munitoai serves open models on European compute. Our margin depends on serving them well, so the loop runs against our own stack: rewrite a serving setup, measure it, keep what wins. It needs a clean computer for every experiment, hundreds at once. Nothing was built or priced for that, so I built it. Munito has run on it for months.

Measured from my laptop against the public API:
→ 1,000 sandboxes started in about a second
→ a 16 vCPU / 32 GiB machine running 2 seconds after the call
→ for the launch, 32,752 started at once, each painting one pixel. The picture finished in under a minute, zero failures.

Each one is a real computer: its own kernel, disk and network. Docker inside. Pause, resume and fork, so an experiment branches instead of starting over. A URL for any port. A desktop when the agent needs eyes and hands.

If you run AI agents, RL rollouts, evals or CI at volume, this is for you. Tell me what you're building: founders@munito.ai
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Munito AI (YC F26) Verified account @munitoai
Abliterated GLM-5.3-Flash, DeepSeek V4.1 Flash and Qwen3.8-27B are available on Munito AI for verified teams: inference and storage in the EU, no retention by default, access after a KYC check and a support request.

Get access: support@munito.ai