AI + Kubernetes

Bring your own GPU.
Run AI next to your Kubernetes app.

We add GPU nodes to your Kubernetes cluster so you can run AI workloads on your own infrastructure. You pick the model and the GPU provider. We handle the platform.

Get started

GPU nodes on managed Kubernetes

The platform supports GPU node pools, serving engines like vLLM, and AI workloads alongside your existing applications.

Any GPU provider, your choice

Plug in hardware from the provider that fits your model, region, and budget. We configure it on the cluster. No vendor lock-in.

You deploy the model, we run the platform

Push your model to the cluster. We handle Kubernetes, the serving layer, monitoring, and rollouts underneath.

GPU providers your cluster can use

Open Models

Open models we suggest for getting started

We like Mistral for their European identity and open-source stance. Other model families work too. Each page covers workload fit, GPU requirements, and deployment.

6 models

Mistral

Flagship general-purpose multimodal lineup spanning Small, Medium, and Large tiers for complex reasoning and creation.

3 models

Ministral

Compact 3B-14B multisize models balancing efficiency with strong text and vision quality.

2 models

Magistral

Reasoning-tuned multimodal experts aimed at more deliberate analytical tasks.

4 models

Devstral

Agentic SWE-focused family for code understanding, editing, and automation.

5 models

Voxtral

Speech-centric models for transcription, realtime captioning, and audio-first interfaces.

1 models

Mistral Nemo

Multilingual open-weight model optimized for broad language coverage.

Open full model catalog

Our research

When a private LLM actually makes sense

Running a private LLM used to be a compromise. Worse models, rough tooling, and compliance as the only justification. That changed. Open-weight models now perform within 2% of closed APIs on most benchmarks. Private deployment is a genuine engineering choice, not a fallback.

Read the full perspective

6 workload patterns

29 use cases

7 min read

Chapter-01

29 workloads that fit private inference

Chapter-02

Why private deployment is viable now

Chapter-03

When to go private, hybrid, or API

Chapter-04

What the infrastructure stack looks like

FAQ

Frequently Asked Questions

Here are some of the most frequently asked questions about Asergo. If you have any other questions, please contact us.

Bring your own GPU.Run AI next to your Kubernetes app.