UK-based independent AI efficiency projectEvidence before claims · 2026

Token Optimisation

AI efficiency · measured
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Local AI vs Cloud AI: The Break-Even Inputs That Matter

Local hardware and cloud API cost categories converge on a shared workload and verifier; no winner or payback result is displayed.

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Classification: MODELLED break-even method. No production comparison or universal local/cloud winner is claimed.

Local AI vs Cloud AI: Compare the Same Accepted WorkMODELLED FRAMEWORK · NO ROUTE WINNER. LOCAL FIXED COSTS → LOCAL VARIABLE COSTS → CLOUD USAGE COSTS → COMMON QUALITY GATE. Current examples are scenarios, not a measured production Qwen-versus-Luna comparison.MODELLED FRAMEWORK · NO ROUTE WINNER1LOCAL FIXED COSTS2LOCAL VARIABLE COSTS3CLOUD USAGE COSTS4COMMON QUALITY GATEInclude hardware, energy, and upkeep · Include API usage, retries, and review
MODELLED FRAMEWORK · NO ROUTE WINNER. Current examples are scenarios, not a measured production Qwen-versus-Luna comparison.

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Local inference and hosted API service have different cost structures. A fair comparison needs the same workload, acceptance standard and measurement window, then makes fixed and variable costs visible.

Include the full cost boundary

  • Local: hardware purchase/amortisation, power, cooling, maintenance, storage, model setup and operator time needed to keep it usable.
  • Cloud/API: input, cached-input and output rates, request/retry count, tools or grounding fees, storage, taxes and any subscription charge.
  • Both: useful accepted jobs, quality checks, runtime, waiting, human review and capacity utilisation.

At low utilisation, owned hardware’s fixed cost is spread across fewer jobs. At high utilisation, power, maintenance and capacity constraints matter. Cloud pricing can change, while local model quality and speed depend on hardware and configuration. No single token rate captures those differences.

Build a comparable scenario

  1. Choose a representative task and its verifier.
  2. Measure accepted jobs, actual usage and time on each path.
  3. Disclose hardware, electricity price, amortisation period and utilisation assumptions.
  4. Include retries and human review on both sides.
  5. Label user-entered assumptions MODELLED and refresh provider prices from official sources.

KD10 has an offline GPU/cloud calculator package with 34 tests; its examples are illustrative and it is not integrated as a public tool. The live Context Cost Calculator handles API prices and measured subscriptions, not GPU depreciation. Keep those scopes separate.

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