AI Resource Efficiency

Use your AI resources efficiently.

Cademix AIRE helps you decide how software, hardware, cloud AI, local models, storage, data, energy and human time should work together — without buying or subscribing to more than you actually need.

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Your workloads → the right resource mix

☁️

Cloud AIQuality, scale and rapid access

🖥️

Local AIControl, privacy and predictable use

⚙️

Hybrid routingUse each resource where it fits best

Cost
Reduce waste

Capacity
Use existing hardware

Dependency
Avoid one-provider lock-in

Efficiency
Route work intelligently

AI efficiency is not only about choosing a model.

The resource picture includes compute, subscriptions, data, storage, electricity, privacy, workflow volume, staff time and provider dependency.

🧮

Compute

GPUs, CPUs, workstations, servers, VPS resources and idle machines.

🧠

Models & software

Cloud subscriptions, APIs, local models, automation tools and overlapping services.

🗃️

Data & storage

Documents, datasets, context, backups, privacy and retrieval architecture.

⏱️

Human time

Which work should stay human, which should be automated, and where AI actually saves time.

Assess → route → optimize.

Measure the workload

Volume, quality requirement, privacy, latency, model size, context and frequency.

Map available resources

Current hardware, subscriptions, servers, storage, staff and existing workflows.

Choose the mix

Local, cloud or hybrid routing with a clear reason for each workload.

Common AIRE questions

Do we need a new GPU?First test whether existing hardware, quantized models or cloud routing already meet the workload.

Should this run locally or in the cloud?Compare quality, cost, privacy, scale, latency and maintenance.

Why are we paying for several AI subscriptions?Map overlap and decide which capabilities actually need separate providers.

Can small local models handle repetitive work?Often yes, when the task is narrow and the required quality is clear.

How do we reduce provider dependency?Separate memory/data/workflows from the model provider and keep replaceable routes.

What belongs on a VPS?Automation, queues, databases and light models may fit well; heavy inference may not.

Is local AI really cheaper?It depends on utilization, electricity, hardware cost and the cloud workload being replaced.

How should tasks be routed?Use small/cheap resources for routine work and stronger models only when needed.

What about confidential documents?Privacy requirements can make local or controlled infrastructure preferable.

How do we know when optimization is good enough?Set measurable stop criteria for quality, cost, latency and human effort.

AIRE supports both decisions and education.

The site is designed to grow into a large knowledge base covering AI hardware, software, local models, cloud providers, resource planning, sovereignty, automation and efficient workflows.

Explore the knowledge base →

Typical assessment outcomes

Keep current resourcesNo purchase required.
Route repetitive work locallyReduce cloud/API volume.
Use cloud selectivelyReserve stronger models for difficult tasks.
Upgrade one bottleneckSpend only where the workload justifies it.
Reduce dependencyMake providers replaceable.

Consultation pricing

Normal online

€80 / hour

AI resource review, architecture choices and optimization planning.

Emergency online

€200 / hour

Priority support for urgent infrastructure or provider decisions.

Vienna on-location

€500 / 3 hours

Travel included. Additional time €80/hour.

Full pricing details →