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https://github.com/substratusai/kubeai

Private Open AI on Kubernetes
https://github.com/substratusai/kubeai

ai autoscaler faster-whisper inference-operator k8s kubernetes llm ollama ollama-operator openai-api vllm vllm-operator whisper

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Private Open AI on Kubernetes

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# KubeAI: Private Open AI on Kubernetes



Get inferencing running on Kubernetes: LLMs, Embeddings, Speech-to-Text.



✅️  Drop-in replacement for OpenAI with API compatibility  

🧠  Serve top OSS models (LLMs, Whisper, etc.)  

🚀  Multi-platform: CPU-only, GPU, coming soon: TPU  

⚖️  Scale from zero, autoscale based on load  

🛠️  Zero dependencies (does not depend on Istio, Knative, etc.)   

💬  Chat UI included ([OpenWebUI](https://github.com/open-webui/open-webui))  

🤖  Operates OSS model servers (vLLM, Ollama, FasterWhisper, Infinity)  

✉  Stream/batch inference via messaging integrations (Kafka, PubSub, etc.)  



Quotes from the community:



> reusable, well abstracted solution to run LLMs - [Mike Ensor](https://www.linkedin.com/posts/mikeensor_gcp-solutions-public-retail-edge-available-cluster-traits-activity-7237515920259104769-vBs9?utm_source=share&utm_medium=member_desktop)



## Architecture



KubeAI serves an OpenAI compatible HTTP API. Admins can configure ML models via `kind: Model` Kubernetes Custom Resources. KubeAI can be thought of as a Model Operator (See [Operator Pattern](https://kubernetes.io/docs/concepts/extend-kubernetes/operator/)) that manages [vLLM](https://github.com/vllm-project/vllm) and [Ollama](https://github.com/ollama/ollama) servers.






## Local Quickstart



Create a local cluster using [kind](https://kind.sigs.k8s.io/) or [minikube](https://minikube.sigs.k8s.io/docs/).



TIP: If you are using Podman for kind...

Make sure your Podman machine can use up to 6G of memory (by default it is capped at 2G):



```bash

# You might need to stop and remove the existing machine:

podman machine stop

podman machine rm



# Init and start a new machine:

podman machine init --memory 6144 --disk-size 120

podman machine start

```



```bash

kind create cluster # OR: minikube start

```



Add the KubeAI [Helm](https://helm.sh/docs/intro/install/) repository.



```bash

helm repo add kubeai https://www.kubeai.org

helm repo update

```



Install KubeAI and wait for all components to be ready (may take a minute).



```bash

helm install kubeai kubeai/kubeai --wait --timeout 10m

```



Install some predefined models.



```bash

cat < kubeai-models.yaml

catalog:

  gemma2-2b-cpu:

    enabled: true

    minReplicas: 1

  qwen2-500m-cpu:

    enabled: true

  nomic-embed-text-cpu:

    enabled: true

EOF



helm install kubeai-models kubeai/models \

    -f ./kubeai-models.yaml

```



Before progressing to the next steps, start a watch on Pods in a standalone terminal to see how KubeAI deploys models. 



```bash

kubectl get pods --watch

```



#### Interact with Gemma2



Because we set `minReplicas: 1` for the Gemma model you should see a model Pod already coming up.



Start a local port-forward to the bundled chat UI.



```bash

kubectl port-forward svc/openwebui 8000:80

```



Now open your browser to [localhost:8000](http://localhost:8000) and select the Gemma model to start chatting with.



#### Scale up Qwen2 from Zero



If you go back to the browser and start a chat with Qwen2, you will notice that it will take a while to respond at first. This is because we set `minReplicas: 0` for this model and KubeAI needs to spin up a new Pod (you can verify with `kubectl get models -oyaml qwen2-500m-cpu`).



## Documentation



Checkout our documentation on [kubeai.org](https://www.kubeai.org) to find info on:



* Installing KubeAI in the cloud

* How to guides (e.g. how to manage models and resource profiles).

* Concepts (how the components of KubeAI work).

* How to contribute



## Adopters



List of known adopters:



| Name | Description | Link |

| ---- | ----------- | ---- |

| Telescope | Telescope uses KubeAI for multi-region large scale batch LLM inference. | [trytelescope.ai](https://trytelescope.ai) |

| Google Cloud Distributed Edge | KubeAI is included as a reference architecture for inferencing at the edge. | [LinkedIn](https://www.linkedin.com/posts/mikeensor_gcp-solutions-public-retail-edge-available-cluster-traits-activity-7237515920259104769-vBs9?utm_source=share&utm_medium=member_desktop), [GitLab](https://gitlab.com/gcp-solutions-public/retail-edge/available-cluster-traits/kubeai-cluster-trait) |



If you are using KubeAI and would like to be listed as an adopter, please make a PR.



## OpenAI API Compatibility



```bash

# Implemented #

/v1/chat/completions

/v1/completions

/v1/embeddings

/v1/models

/v1/audio/transcriptions



# Planned #

# /v1/assistants/*

# /v1/batches/*

# /v1/fine_tuning/*

# /v1/images/*

# /v1/vector_stores/*

```



## Immediate Roadmap



* Model caching

* LoRA finetuning (compatible with OpenAI finetuning API)

* Image generation (compatible with OpenAI images API)



*NOTE:* KubeAI was born out of a project called Lingo which was a simple Kubernetes LLM proxy with basic autoscaling. We relaunched the project as KubeAI (late August 2024) and expanded the roadmap to what it is today.



🌟 Don't forget to drop us a star on GitHub and follow the repo to stay up to date!



[![KubeAI Star history Chart](https://api.star-history.com/svg?repos=substratusai/kubeai&type=Date)](https://star-history.com/#substratusai/kubeai&Date)



## Contact



Let us know about features you are interested in seeing or reach out with questions. [Visit our Discord channel](https://discord.gg/JeXhcmjZVm) to join the discussion!



Or just reach out on LinkedIn if you want to connect:



* [Nick Stogner](https://www.linkedin.com/in/nstogner/)

* [Sam Stoelinga](https://www.linkedin.com/in/samstoelinga/)