Every month you rent AI, the meter resets to zero. This page shows exactly what owning your AI costs - four costed build tiers, the honest local-vs-cloud trade-off, and interactive calculators that find your break-even month to the day.
Move the sliders to your team. Watch the cumulative cost diverge and find your break-even month.
Illustrative estimate. Cloud ≈ ChatGPT Team / Claude Team seats (~RM130-280/user/mo) at ≈ RM4.6/USD; local figure includes hardware + commissioning; electricity ~RM1-6/day. Your exact crossover is calculated in the free audit.
No single answer is right for everyone. Here's the comparison without the spin - including when cloud or hybrid is genuinely the better call.
| Dimension | Local / On-prem | Cloud API | Hybrid |
|---|---|---|---|
| Data privacy | Highest - never leaves you | Lowest - sent to a third party | High - sensitive stays local |
| Recurring cost | Low & fixed (power + support) | Variable - can balloon | Mixed - base + overflow |
| Upfront cost | Higher - hardware | Near zero | Moderate |
| Latency | Low, predictable (your LAN) | Internet + provider load | Depends on path |
| Offline operation | Yes | No | Partial |
| Frontier reasoning ceiling | Very good (hardware-capped) | Highest | Best of both |
| Scaling to spikes | Hardware-limited | Elastic | Burst to cloud |
| Maintenance | Managed by VYROX | Vendor-managed | Most complex |
For most SMEs handling private or regulated data with steady daily volume, local pays for itself in months and removes per-token billing risk. If cloud or hybrid fits you better, we'll tell you - and build that instead.
Complete, VYROX-commissioned systems - hardware sized, models loaded, runtime and agents wired in, staff trained.
How "users supported" is calculated: concurrent users = free VRAM after model weights ÷ KV-cache per session (≈ 2 × layers × kv-dim × context × precision), capped by GPU throughput ÷ a 15 tok/s per-user floor and by the runtime's parallel slots (Ollama ~8, vLLM many). "Team size" assumes typical intermittent office use (~1 active generation per 5 staff). Numbers shown are at ~8K context with FP16 KV-cache; Q8 KV-cache roughly doubles concurrency and shorter context increases it further. Use the cost configurator to model your exact model, context and concurrency.
Pick what you'll use AI for and your team - the recommendation, hardware, model and 5-year savings update instantly.
Two ways to use it: set how many concurrent users you need and leave hardware on Auto - we pick the cheapest build that serves them - or drive every variable yourself (LLM size, quantization, GPU, context, hours/day). The itemised build cost, capacity, payback and cloud savings update live. Indicative estimates; your exact quote comes from the audit.
Indicative estimate. Hardware ≈ Malaysia street pricing during the 2026 GPU/DRAM shortage; commissioning, electricity (TNB commercial ~RM0.50/kWh) and optional support included as shown. Cloud baseline ≈ Team-tier seats (+ agent API where selected). VYROX confirms exact specs, prices and a measured savings projection in the free audit.
Book a free 45-minute Local-AI Audit. We measure your current cloud spend, spec the exact build, and give you the costed break-even date - in writing, no obligation.
No deck pitch. Just engineers sizing your build.