ROI Modeling Engine

Cloud vs Local AI Calculator

Estimate when local AI hardware can realistically beat cloud API fees. Includes pricing mix, model fit, rough throughput, utilization, power, growth, and resale assumptions.

ROI modeling engine

Cloud API vs Local Hardware

Local hardware is capacity constrained at this workload.

Hardware

Choose your local machine

Presets use estimated cost, wall power, memory, and rough throughput. Treat them as starting assumptions.

Local model profile

What quality tier are you replacing?

30B-ish coding and analyst tasks. Useful for focused workflows.

API pricing

Compare against cloud tokens

Output token share 35%Blended cloud price: $4.31 per 1M total tokens.

Monthly load

Expected token volume

1M50M tokens/month500M

Capacity check

Can this box keep up?

overloaded

Est. speed

45 tok/s

Monthly capacity

41M

Utilization 35%

Break-even estimate

26months to ROI

Local hardware is capacity constrained at this workload.

Estimate only. Local model quality, latency, context length, batching, and operational effort can change the result.

Monthly cloud

$216

Monthly local power

$76

First-year cloud

$2,592

First-year local

$4,412

24-month delta

$149 higher

Model fit

Overloaded

Confidence

55%

Est. capacity

41M/mo

Cumulative cost projection

24-month ownership curve

Cloud APIs Local hardware

Operating spread

$140 monthly advantage

After the hardware is paid off, local operation is cheaper than the selected cloud API load.

Assumption snapshot

24GB VRAM workstation vs GPT-5 class

50M tokens/month, 35% output tokens, Mid local model, and 0% monthly usage growth.

Honesty layer

This is a decision model, not a benchmark.

The calculator now checks rough throughput and model fit, but real ROI still depends on your prompts, batching, quantization, uptime, engineering time, and whether a local model is good enough to replace the selected cloud API.