AI Economics practical guide
Cloud AI vs Private AI: How to Compare Total Cost
Cloud or API AI is generally usage-based. Private AI turns GPUs, electricity, hosting, amortisation, maintenance and technical staff into a more fixed cost. There is no universal winner; compare workload, utilisation, data, latency, resilience and operating capability.
Compare the cost shape first
APIs suit low or variable volume, rapid trials and access to current models. Private AI needs a stable workload and manageable hardware utilisation. Do not simply divide a GPU purchase price by token price.
Then compare non-financial factors
Data routes, permissions, latency, resilience, maintenance, model updates, provider lock-in, technical staff and incident ownership all affect the choice. Review the complete architecture before making a claim that data never leaves.
Decision checklist
- What are monthly input and output tokens and requests?
- Is demand steady, batch-oriented or spiky?
- What GPU utilisation and availability are expected?
- Who owns patches, models, monitoring, backup and incidents?
Method references and review date
These sources provide related definitions, frameworks or implementation considerations. The method is synthesised by iGears and is not a fixed provider quotation or an outcome promise.
Last reviewed: 30 August 2026
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