enki.stack in production

Self-hosted inference is easier than ever. Building, extending and operating a production AI platform takes more.

Illustration of AI infrastructure

No black box—a modular, open AI platform.

Building your own AI platform means making decisions about serving, GPUs, routing, monitoring, security and operations. enki.stack brings together core open-source building blocks your team can inspect, adapt and extend.

01

Keep control of your infrastructure

Build on Kubernetes and infrastructure you operate yourself, with room for the controls your use case requires.

02

Start with connected building blocks

Bring model serving, GPU support, AI routing and monitoring together instead of starting with isolated components.

03

Adapt it to your environment

Inspect the open foundation, make your own changes and integrate existing services.

enki.stack

enki.stack brings open-source building blocks together into an AI platform: from model serving and GPU management to authentication, routing and monitoring. Its open, modular architecture gives your team control over the models you use, the services you connect and how you operate the platform. Build chat, RAG, agents and your own AI applications on top, then extend the platform to meet your needs. Kubernetes is the foundation, on your own infrastructure or in the cloud.

APPLICATIONS

AI applications

  • Chat
  • RAG
  • Agents
  • Custom applications
CORE PLATFORM

AI services

  • Authentication
  • Inference
  • Model registry
  • Guardrails
  • GPU management
INFRASTRUCTURE

Kubernetes

On-premises, managed Kubernetes or bare-metal cloud

OBSERVABILITY MetricsLogsTraces

Two ways to use enki.stack

01 / DIY

Everything is available. You put it to work.

For teams who want to learn, experiment with and operate the stack themselves.

  • Use the source code and documentation
  • Install, configure and operate it independently
Explore the repository

02 / TAILORED

Your platform. Our expertise.

For teams and businesses who want support adapting the stack to their environment.

  • Plan and carry out the installation together
  • Integrate models, services and platform components
  • Review configuration, monitoring and operational effort
  • Transfer knowledge and document key decisions
  • Arrange ongoing support
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