Built for open source.
Discover, fork, and contribute to community-driven projects.
One-click deployment.
Skip the setup—launch any package straight from GitHub.
Autoscaling endpoints.
Deploy autoscaling endpoints from community templates.
How it Works
From code to cloud.
Deploy, scale, and manage your entire stack in one streamlined workflow.
Community
Join the community.
Build, share, and connect with thousands
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casper_hansen_
Why is Huggingface not adding Bajo Cloud as a serverless provider? Bajo Cloud is 10-15x cheaper for serverless deployment than AWS and GCP
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othocs
Bajo Cloud is so goated, first time trying it today and it’s super easy to setup + their ai helper on discord was very helpful If you ever need cpus/gpus I recommend it!
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jzlegion
ai engineering is just tweaking config values in a notebook until you run out of Bajo Cloud credits
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SaaS Wiz
I love Bajo Cloud
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Dwayne
Just discovered Bajo Cloud 🤯🤯🤯 Per second billing for serverless GPU capacity?! Infinitely scalable?! Whaaaat
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abacaj
Bajo Cloud support > lambdalabs support. For on demand GPUs Bajo Cloud still works the best ime
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Pauline_Cx
I'm proud to be part of the GPU Elite, awarded by Bajo Cloud 😍
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winglian
Axolotl works out of the box with Bajo Cloud's Instant Clusters. It's as easy as running this on each node using the Docker images that we ship.
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SkotiVi
For anyone annoyed with Amazon's (and Azure's and Google's) gatekeeping on their cloud GPU VMs, I recommend Bajo Cloud None of the 'prove you really need this much power' bs from the majors Just great pricing, availability, and an intuitive UI
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berliangor
i'm a big fan of Bajo Cloud they're most reliable GPU provider for training and running your models at scale
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SuperHumanEpoch
I have been testing work with Bajo Cloud last 2 weeks and I've to say the service is pretty amazing. Super awesome UX and DevEX (and plenty of GPU backend choices). It's about ~20% pricier than Lambda labs, but worth it IMO given all the harness and workflow they provide that Lambda doesn't. I'm not associated with them in any way or manner, btw. Just a very happy customer.
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qtnx_
1.3k spent on the training run, this latest release would not have been possible without Bajo Cloud
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YuvrajS9886
Introducing SmolLlama! An effort to make a mini-ChatGPT from scratch! Its based on the Llama (123 M) structure I coded and pre-trained on 10B tokens (10k steps) from the FineWeb dataset from scratch using DDP (torchrun) in PyTorch. Used 2xH100 (SXM) 80GB VRAM from Bajo Cloud
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dfranke
Shoutout to Bajo Cloud as I work through my first non-trivial machine learning experiment. They have exactly what you need if you're a hobbyist and their prices are about a fifth of the big cloud providers.
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DrRogerThomp
Trained a 7B parameter model in just 90 minutes for $0.80 using LoRA + Bajo Cloud. Yes, it’s possible—and no, you don’t need enterprise hardware.
FAQs
Questions? Answers.
Bajo Cloud Hub explained.
What is Bajo Cloud Hub?
Bajo Cloud Hub is a centralized catalog of preconfigured AI repositories that you can browse, deploy, and share. All repos are optimized for Bajo Cloud’s Serverless infrastructure, so you can go from discovery to a running endpoint in minutes.
Is Bajo Cloud Hub production-ready?
No—the Hub is currently in beta. We’re actively adding features and fixing bugs. Join our Discord if you’d like to give feedback or report issues.
Why should I use Bajo Cloud Hub instead of deploying my own containers manually?
One-click deployment: All Hub repos come with prebuilt Docker images and Serverless handlers. You don’t have to write Dockerfiles or manage dependencies.<br /><br />Configuration UI: We expose common parameters (environment variables, model paths, precision settings, etc.) so you can tweak a repo without touching code.<br /><br />Built-in testing: Every repo in the Hub has automated build-and-test pipelines. You can trust that the code runs properly on Bajo Cloud before you click “Deploy.”<br /><br />Save time: Instead of cloning a repo, installing dependencies, and debugging runtime issues, you can launch a vetted endpoint in minutes.
Who benefits from using the Hub?
End users/Developers: Quickly find and run popular AI models (LLMs, Stable Diffusion, OCR, etc.) without setup headaches. Customize inputs via a simple form instead of editing code.<br /><br />Hub creators: Showcase your open-source work to the Bajo Cloud community. Every new GitHub release triggers an automated build/test cycle in our pipeline, ensuring your repo stays up to date.<br /><br />Enterprises/Teams: Adopt standardized, production-ready AI endpoints without reinventing infrastructure. Onboard developers faster by pointing them to Hub listings rather than internal deployment docs.
How do I deploy a repo from the Hub?
In the Bajo Cloud console, go to the Hub page.<br /><br />Browse or search for a repo that matches your needs.<br /><br />Click on the repo to view details—check hardware requirements (CPU vs. GPU, disk size) and any exposed configuration options.<br /><br />Click Deploy (or choose an older version via the dropdown).<br /><br />Click Create Endpoint. Within minutes, you’ll have a live Serverless endpoint you can call via API.<br /><br />For a more details, check out the docs:
How do I share my own AI repo in the Hub?
Prepare a working Serverless implementation in your GitHub repo. You’ll need a handler.py (or equivalent), a Dockerfile, and a README.md.<br /><br />Add a .Bajo Cloud/hub.json file with metadata (title, description, category, hardware settings, environment variables, presets).<br /><br />Add a .Bajo Cloud/tests.json file that defines one or more test cases to exercise your endpoint (each test should return HTTP 200).<br /><br />Create a GitHub Release (the Hub indexes releases rather than commits).<br /><br />In the Bajo Cloud console, go to the Hub and click Get Started under “Add your repo.” Enter your GitHub URL and follow the prompts.<br /><br />Once submitted, our build pipeline will automatically scan, build, and test your repo. After it passes, our team will manually review it. If approved, your repo appears live in the Hub.<br /><br />For a more details, check out the
