What does local AI really cost to run?

Compare the same work on both sides

Start with your actual cloud invoice and the work behind it. Separate API charges from employee subscriptions, storage and other services. Buying a local server will not automatically replace every subscription or integration your company uses.

Then check the local model against representative tasks. Compare output quality, response time and the number of people working at once. A comparison between a small local model and a more capable hosted model only helps if both deliver an acceptable result for the job. Include the time people spend checking and correcting answers.

Include the costs that continue after delivery

A purchase comparison should include the agreed hardware configuration, installation, shipping, any applicable taxes and the period you expect to use the system. Add electricity, cooling where relevant, maintenance, software or model licensing, and the staff time needed to operate it.

If you lease or finance the system, use those payments and the contract terms instead of treating the purchase price as cash paid upfront. Do not count the same hardware expense twice. Keep one-time costs separate from recurring costs so the assumptions remain easy to change.

A worked example, with the assumptions visible

Suppose a system costs $12,000 and you compare it over 36 months. Allocating that hardware cost evenly gives $333.33 per month for this comparison. This is an illustrative budgeting calculation, not a quote or an accounting depreciation schedule.

If its average measured draw is 0.5 kW, running for 730 hours at $0.15 per kWh costs $54.75 in electricity. Add an illustrative $250 per month for support and the subtotal is $638.08 per month. Cooling, taxes, financing, other licenses and internal staff time are excluded from that example and must be added when they apply.

Compare that total with the cloud spending the machine would actually replace. A $1,500 cloud bill and a $400 cloud bill lead to different decisions. Neither comparison establishes a payback period until you account for equivalent output, workload coverage and the timing of the cash payments.

Utilization can change the answer

An owned server has capacity whether or not anyone uses it. Consistent workloads can spread that cost across more useful output. Light use may make a metered service cheaper. Peaks can also exceed local capacity, creating queues unless you size for them or arrange another way to handle the excess.

Model size, context length and simultaneous requests affect memory use and throughput. A specification sheet alone does not tell you how many employees will be happy using the system. Measure demand at busy times and leave room for the workload to grow.

Use an estimate you can revise

Keep the assumptions alongside the result: model, hours of use, power price, expected life, support and replaced cloud usage. Test a quieter month as well as a busy one. This makes the decision easier to revisit when your team or its workload changes.

Our savings calculator lets you explore a starting estimate. Use your own usage and cost figures, then discuss a configuration against those requirements. There is no universal saving percentage for local AI; the useful number is the cost of delivering your work reliably.