Open Source AI Foundations
Why Open Source AI Matters
Artificial Intelligence is transforming how organizations innovate — but relying on closed, proprietary AI systems often means losing control over your data, your models, and your future. Open Source AI offers a different path: transparent, secure, and fully customizable AI infrastructure that you own and operate.
At Kangaroot, we help companies build and run open AI platforms — from Large Language Model (LLM) deployment and inference to OpenShift AI clusters — ensuring full sovereignty over their data and compute.
LLM Serving & Inference
Running modern AI models requires more than just computing power; it needs orchestration, optimization, and cost control. Kangaroot designs and operates LLM serving environments that make inference scalable, efficient, and reliable — whether you’re deploying models like Llama 3, Mistral, or custom fine-tuned variants.
We implement open-source inference frameworks such as vLLM, Text Generation Inference (TGI), and Ray Serve, enabling intelligent autoscaling, GPU scheduling, and performance tuning. Our experts integrate these pipelines with your CI/CD and monitoring stack, ensuring predictable latency and optimal resource usage — from proof-of-concept to production.
OpenShift AI – Enterprise AI on Open Source Infrastructure
Built on Red Hat OpenShift, OpenShift AI provides a secure, hybrid platform for managing AI workloads at scale. We deploy OpenShift AI clusters that combine Kubernetes-native MLOps, model training environments, and secure GPU orchestration — empowering your data scientists to experiment quickly while your operations teams maintain governance and compliance.
Through integrations with JupyterHub, MLflow, and model registries, we create a seamless environment for model training, versioning, and deployment — all powered by open technologies.
How We Work
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Consulting & Architecture
We assess your data and infrastructure landscape to design AI platforms that balance performance, security, and sovereignty.
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Implementation & Optimization
Our engineers deploy and tune AI clusters — from LLM inference pipelines to full OpenShift AI environments — ready for enterprise workloads.
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Training & Enablement
We help your teams master open-source AI tools, from model hosting to observability and scaling practices.
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24/7 Managed AI Platform Support
Our 24/7 support team monitors and maintains your AI infrastructure, ensuring stability, performance and cost-efficient operations.
AI is not a revolution. It is a stress test.
We call this the AI era. We talk about breakthroughs, disruption & exponential innovation. But what is really happening today is less spectacular and at the same time more fundamental.
AI is not a revolution. AI is a stress test.
A stress test for our architectures. For our data management. For our governance. And above all: for our organisational structures.
Private AI foundations require several technological components.
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1. Suitable hardware
AI models in production require high compute capacity and memory bandwidth. Standard server hardware often does not provide sufficient performance. GPUs are better suited for this purpose, but they are expensive to purchase. Therefore, they can also be rented through data centers, which helps avoid large upfront investments.
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2. Inference infrastructure
To offer an AI model, an API endpoint is needed to receive requests and return responses. The process that performs this transformation is called inference. The software layer that bridges the API, server, and GPU is referred to as an inference server.
There are various solutions available, both for professional production environments (such as vLLM) and for smaller-scale or local use (such as llama.cpp). -
3. Model lifecycle management
In larger environments, it is important to manage models throughout their entire lifecycle: version control, promotion from development to production, and storage. Thanks to OCI standards, models can be stored in container registries as binary artifacts, making it possible to use existing container lifecycle tools for AI models as well.
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4. Scaling & routing
When a single GPU or endpoint is not sufficient, additional architecture is required. An intelligent API gateway can distribute traffic across multiple servers with GPUs. This is particularly relevant at larger scale, but not a requirement in the initial phase.
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