Your AI. Your data. Your control.
Built for the full AI workload lifecycle.
From your first GPU environment to large-scale production deployments — automation and lifecycle management support AI workloads as they scale.
- Cluster lifecycle managementProvisioning, upgrades and decommissioning supported through automation and lifecycle management.
- Multi-model servingSeveral models served in parallel through container orchestration.
- OpenAI-compatible APIYour existing applications and tools connect by changing one address. No code to rewrite.
- Health monitoringContinuous checks across nodes, clusters and workloads.
- GPU resource sharingAccelerator capacity shared across workloads instead of sitting idle.
- Tenant isolationWorkloads, data and capacity separated per tenant.
Private AI, under your control.
Everything runs in your own environment. No prompts or answers leave it; the only outbound traffic fetches the models you choose to deploy.
Pick a model from the tested list and deploy it from the console. The technology checks first that it fits on your GPUs, so your team can focus on using or delivering AI services.
Kumorion brings engineering expertise across GPU infrastructure, Kubernetes and AI workload management, with the layers tuned to fit together. Automation and lifecycle management support your environment as it grows.
From GPU capacity to AI workloads.
Kumorion combines container orchestration, GPU resource management and lifecycle automation to run and scale AI workloads on infrastructure you control.
Your GPU capacity
GPU infrastructure available for AI workloads.
Kumorion AI Workspace
Container orchestration, GPU resource sharing, scaling and lifecycle management for AI workloads.
Your AI workloads & services
Run your own AI workloads or deliver scalable AI services to your customers — on infrastructure you control.
Supports multi-model serving and the management of AI workloads.
Bring private AI into your organization.
Let's explore how AI Workspace fits your data privacy requirements, infrastructure and plans — whether you run AI for your own organization or deliver services to customers.
Answered by engineers.
Q1How does AI Workspace keep our data private when GPU capacity is shared?+
Models run on your infrastructure, in your environment — no prompts or answers leave it. Tenant isolation keeps each customer's workloads separate even when GPU capacity is shared. The only outbound traffic fetches the models you choose to deploy.
Q2Can customers bring their own open models — Llama, Mistral, DeepSeek and others — served through OpenAI-compatible endpoints so existing apps just work?+
Yes. Bring your own open models, and they are served through OpenAI-compatible endpoints so existing applications work unchanged. There is no proprietary model lock-in.
Q3Which GPU hardware do you support?+
Built on NVIDIA GPUs, from workstation cards to data-centre servers.
Q4How do you keep expensive GPUs busy and avoid paying for idle accelerators as demand moves?+
Live GPU telemetry shows which models are used, and models can be deployed or evicted from the console in minutes, so GPUs go to the models that are in demand. Continuous batching keeps each GPU busy by serving many requests at once. This is where the operator makes or loses margin.
Q5What inference engines and optimizations do you run to lower our cost per request?+
vLLM for serving, with batching, quantization and caching among the levers. Each one feeds the same outcome: a lower cost per request or token, which is the margin you can quote when reselling.
Q6Can AI Workspace serve RAG and agent workloads, and how is each customer's data and vector store kept separate?+
Yes — RAG is the most common first use case, and agent workloads are supported. Each customer's data and vector store are isolated per tenant, so multi-customer privacy is handled by design.
Q7Can this run fully air-gapped for the most sensitive public sector and regulated customers?+
Yes. AI Workspace can run fully air-gapped for sensitive environments. The tradeoff is operational: updates arrive offline, which we handle as part of the service.
Q8How do we run or deliver AI services without building all the infrastructure expertise in-house?+
Kumorion provides the engineering expertise, automation and lifecycle management behind AI Workspace. Pick a model from the tested list and deploy it from the console, which first checks that it fits on your GPUs. Your team can focus on its applications or service offering while retaining control of the infrastructure and data.
Q9How does running open models on our own infrastructure compare on cost to per-token cloud APIs once agent workloads scale up?+
Honestly: at low volume, per-token APIs are convenient. As agent workloads scale, the math shifts toward self-hosting — your own capacity at a predictable cost beats per-token pricing. We can walk through the crossover point with your volumes. Sovereignty comes on top of the cost case, not as a premium.
Q10How does AI Workspace support organizations with strict regulatory requirements?+
AI Workspace keeps models and data in your environment, with visibility into the models in use and isolation between tenants. These capabilities support your privacy and governance requirements; they do not make your organization legally compliant on their own. Assess obligations such as the EU AI Act, GDPR and DORA as part of your own governance.
Q11What is the path from a proof of concept to production — and what do we bring versus what you provide?+
Start with a proof of concept and a clear success definition, then expand into production. You bring the infrastructure and your applications or service offering; Kumorion provides the technology, automation, lifecycle management and expertise. If you deliver AI services to customers, your brand and customer relationships remain yours.