Worked example: small team support
The default scenario uses 500 messages per day, 30 active days, 650 input tokens, 250 output tokens, and 30% cached input on GPT-5.4 mini.
Effective input price is 0.70 x $0.75 + 0.30 x $0.075 = $0.5475 per 1M input tokens. Monthly volume is 15,000 messages, 9.75M input tokens, and 3.75M output tokens.
API input cost is 9.75 x $0.5475 = $5.338125. API output cost is 3.75 x $4.50 = $16.875. Total API cost is $22.213125, displayed as $22.21 per month.
The ChatGPT Plus path is $20.00 per month. Local AI is $1,200 / 24 months plus 0.18 kW x 6 hours x 30 days x $0.20/kWh = $50.00 + $6.48 = $56.48 per month.
In this example the cheapest path is ChatGPT Plus. The API crosses the Plus cost at about 450.2 messages/day and the local monthly cost at about 1,271.3 messages/day, before model-quality, usage-limit, and privacy tradeoffs.
Is ChatGPT Plus worth it for my usage?
ChatGPT Plus is most compelling when your monthly API-equivalent usage is above the selected plan cost and your workflow fits inside ChatGPT plan limits. If usage is light, short, or occasional, API billing can be cheaper.
When does API become cheaper than ChatGPT Plus?
API is cheaper below the break-even messages per day shown in the result. That threshold depends on input tokens, output tokens, cached input share, model price, and active days per month.
How cached input changes API cost per message
Cached input lowers the effective input price only when a reusable context segment qualifies for cached billing. It helps most when prompts reuse the same long system instructions, policy text, or retrieval context.
How much does local AI really cost for a small team?
The direct local monthly cost is hardware amortization plus electricity plus any fixed operating extras. That still omits engineering time, updates, security review, backups, and quality gaps versus the API model.
At what usage does local AI beat cloud APIs?
Local AI beats the API on direct cash cost when the API monthly spend rises above local monthly amortized operating cost. The result still needs a quality check, because a local model may not be a drop-in substitute for a frontier cloud model.
What to do if your real token count is higher than your first estimate
Rerun the calculator with a low, expected, and high scenario after a week of real usage. Long context, file analysis, retries, and agent tool calls can make token volume much higher than a simple chat estimate.
How to use the same calculator for team pilots and private deployments
For team pilots, use real message counts and average prompt sizes. For private deployments, enter the hardware quote, measured wall power, amortization policy, and maintenance cost instead of the defaults.
