Private cloud systems

Your cloud.
Closer. Smarter.
Still yours.

Lyka designs private, AI-ready cloud platforms for teams that need the speed of modern cloud engineering—with ownership, predictable economics, and a deliberate path to hybrid scale.

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Private AI inferencek3s developer platformsHybrid cloud delivery
The premise

Great infrastructure should make a team feel more capable, not more dependent.

Build the platform your work actually needs.

One carefully governed system can give a focused engineering organisation a powerful private development cloud—then expand when the work demands it.

01 / FOUNDATION

Private Cloud Foundation

A secure, observable Kubernetes base with networking, storage, resource boundaries, identity foundations, and a clear operating model.

02 / AI-READY

Local AI for builders

Private inference designed around real hardware limits: model budgets, request controls, team access, and workflows that keep data close.

03 / HYBRID

Development close. Production ready.

Keep development responsive and economical on your own platform while integrating deliberate pathways to AWS or your production estate.

One platform. Clear boundaries. No mystery capacity.

Lyka systems are designed to make ownership visible: what runs where, what it can consume, and how it is observed.

A practical private-cloud stack
Developer experiencesApplications · portals · local AI
Platform servicesIdentity · CI/CD · observability
k3s foundationNamespaces · ingress · storage · policy
Governed computeCPU · memory · GPU / unified memory
Secure connectivityWireGuard · network guard · hybrid links
Designed to evolve

Start with a strong local centre of gravity.

Run the workloads that benefit from proximity: development environments, internal tools, test services, and AI inference. Keep production where it belongs, and connect both worlds intentionally.

Principles that keep a private cloud healthy.

Technology choices are only useful when they create a calm, understandable operating environment for the team.

01Bounded by default

Every workload gets explicit resource requests and limits before it becomes someone else’s problem.

02Private by design

Private access, tight service exposure, and least-privilege become platform defaults—not a cleanup task.

03Observable early

Metrics, logs, and meaningful alerts arrive before scale makes blind spots expensive.

04Cloud compatible

Workflows and deployment patterns stay aligned with the production cloud instead of becoming a dead-end lab.

From powerful box to dependable platform.

The work is phased so every step delivers a usable capability while preparing the next one.

01 / DISCOVER

Map the real workload

Define users, data boundaries, existing production interfaces, and the local workloads worth moving.

02 / FOUNDATION

Secure the base

Establish network access, host governance, Kubernetes primitives, storage, and repeatable deployment paths.

03 / ENABLE

Deliver developer value

Introduce the services teams touch every day: CI/CD, internal environments, identity, and bounded AI.

04 / OPERATE

Measure and evolve

Monitor demand, set service standards, automate the reliable parts, and extend to hybrid workflows when ready.

Questions worth asking early.

The right platform is shaped around operating reality, not a generic infrastructure diagram.

Who is a private cloud platform right for?

It is a strong fit for teams with steady development workloads, sensitive data, latency-sensitive internal services, or a desire to run local AI without routing routine work through external providers.

Does this replace AWS or another production cloud?

Not necessarily. The typical goal is a deliberate split: local infrastructure for development, experimentation, and private workloads; production cloud for internet-scale, customer-facing services. The platform should make that boundary easier to manage.

Can a small team operate it?

Yes, if the foundation is intentionally constrained. Clear resource budgets, a limited platform service catalogue, documented runbooks, and practical monitoring matter more than an oversized stack.

How does private AI fit in?

Inference is treated as a governed platform service: selected models, fixed concurrency, controlled context sizes, access policy, and visible host budgets—rather than an unbounded process that consumes the entire machine.

Private cloud, made practical

Bring the workload. We’ll help design the right footprint.

Whether you are moving development closer to your team, building an AI-ready internal platform, or connecting a private environment to your existing cloud, Lyka is built to turn the architecture into an operating system for delivery.

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