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AI cost calculator

Local vs cloud AI: find your break-even

Put in your own token volume, cloud API price and hardware cost. The calculator shows when local AI pays back, if it does, and it models falling cloud prices so the answer isn't tilted toward hardware.

Local AI pays back when your token volume is high enough and steady enough to cover hardware, power and operations faster than cloud API prices fall. This calculator compares those inputs and shows the break-even month, if there is one. An arXiv preprint estimates break-even at a few months for small models, about 2 years for medium models and about 5 years for large ones. Your own numbers decide where you land.

Cumulative cost lines crossing above a server you own

Your inputs

Example inputs - replace with yours

Cost inputs

Input plus output tokens across all workloads. Independent research finds on-premises deployment viable mainly at around 50M tokens a month or more, or under strict data-residency rules.

Your average across input and output tokens and the models you actually use.

One estimate puts the fall at about 10x per year for equal-capability models (a16z). Enter what you expect to pay, not the best case.

Servers or workstations, GPUs and networking. Paid up front, and again when its useful life ends.

How long before you would replace it. Value left in the hardware at month 36 is credited back.

Electricity, cooling, rack space or colocation.

Monitoring, patching, model updates and support for the local stack.

Hybrid routing: the rest still goes to the cloud API. Assumes your hardware can serve this share.

What your inputs say

The chart and break-even appear here once the calculator loads. It compares cloud API spend with hardware you own over three years.

How to use this

How to use this

  1. Volume

    Enter your monthly token volume

    Use real usage if you have it: input plus output tokens across every workload you would move. If you are estimating, enter a low case first, then a high case.

  2. Cloud

    Enter your blended cloud API price

    This is your average price per million tokens across the models you use, with input and output weighted by your actual mix. Your provider invoice is the best source.

  3. Decline

    Set the expected annual API price decline

    Cloud prices for the same capability keep dropping. Enter the yearly decline you expect. A higher number pushes break-even further out, which is the honest way to run this.

  4. Hardware

    Enter hardware cost and useful life

    Use a current quote for the hardware that can run the model you need. Useful life is how many years you expect to run it before replacing it.

  5. Run costs

    Add power, hosting and managed operations

    Enter what you expect to pay each month to power, host and operate the system. Operations covers monitoring, updates, security and the people who keep it running.

  6. Read

    Compare the two cost lines

    The chart shows cumulative cloud cost against cumulative local cost. Where they cross is your break-even. If they never cross within the hardware's useful life, cloud is the better deal for that workload.

Method

Assumptions and what's excluded

The calculator models these inputs: monthly tokens, blended cloud API price per million tokens, expected annual API price decline, hardware cost, hardware useful life, power and hosting per month, and managed operations per month. The cloud line is your monthly volume times a price that falls each year at the rate you set. The local line is the hardware cost up front, then flat monthly power, hosting and operations.

Price decline is built in because it is real. a16z estimates that API prices for LLMs of the same capability fall about 10x per year. Epoch AI measures 9x to 900x per year, depending on the benchmark. Those figures cover API prices, not total cost of ownership. They still mean a break-even that looks close today can drift further out next year.

Managed operations is its own input for a reason. The arXiv preprint behind the break-even estimates counts only GPU cost and GPU electricity. It leaves out staffing, maintenance, cooling, licensing and server and network costs, and its authors list staffing and maintenance as future work. Leave operations at zero and you get that paper's view. Put in a real number and you get a fuller picture.

The calculator does not model accuracy differences between local and cloud models. It does not model data egress fees. It does not put a value on compliance, data residency or keeping data on-site. Each of these can move the decision more than the math does. Weigh them separately and don't read the result as a verdict on them.

Interpretation

How to read the result

A break-even well inside the hardware's useful life means local AI is worth a serious look for that workload. A break-even near the end of the useful life is marginal, and a small change in volume or price can flip it. No break-even means stay on cloud for now.

Volume is usually the deciding input. The same arXiv preprint finds on-prem deployment viable mainly at roughly 50M or more tokens per month, or under strict data-residency rules. If you are well below that and have no residency requirement, expect cloud to win.

The result doesn't have to be all local or all cloud. Many businesses run steady, high-volume, sensitive work on hardware they own and route occasional or harder tasks to cloud models. Run the calculator for each workload, not for your total spend.

Treat the output as a first pass, not a quote. It shows which inputs matter most for you. Hardware prices, power rates and cloud pricing change, so re-run it with current figures before you decide.

Go deeper

Read the honest break-even guide

The calculator gives you a number. The guide explains the evidence behind it: where local AI saves money, where it doesn't, the strongest arguments against self-hosting, and how hybrid routing between local and cloud models changes the math.

Read the guide: /resources/local-vs-cloud-ai-cost/

FAQ

Frequently asked questions

When does local AI become cheaper than cloud AI?
Local AI becomes cheaper when your cumulative cloud spend passes the full cost of owning and running the hardware, within its useful life. One arXiv preprint puts that break-even at a few months for small models, about 2 years for medium models and about 5 years for large models. That estimate leaves out staffing and maintenance, so your volume, prices and operating costs decide where you fall.
Why does the calculator assume cloud prices will fall?
Because they have been falling fast, and ignoring that would make local AI look better than it is. a16z estimates API prices for equivalent-capability LLMs fall about 10x per year, and Epoch AI measures 9x to 900x per year depending on the benchmark. You set the decline rate, so you can test both a cautious case and an aggressive one.
What does the calculator leave out?
It leaves out accuracy differences between models, data egress fees and the value of compliance or data residency. Those factors can outweigh the cost math, so weigh them alongside the result. The calculator does include managed operations, which the arXiv break-even estimate leaves out.
Is a break-even result enough to decide on local AI?
No. It is a first pass that shows which inputs drive your cost. Before you buy hardware, confirm current hardware and power prices, check that a local model is accurate enough for the task, and decide which workloads should stay on cloud. Tekscape, a Managed Intelligence Provider (MIP), can size it with your real usage.

Sources

  1. arXiv preprint: on-prem LLM break-even analysisPreprint, not peer reviewed. Break-even of a few months / about 2 years / about 5 years by model size; viable mainly at roughly 50M+ tokens per month or under strict data residency. Counts GPU cost and electricity only; staffing and maintenance excluded.
  2. a16z: LLMflationAPI prices for equivalent-capability LLMs fall about 10x per year. Covers API prices, not total cost of ownership.
  3. Epoch AI: LLM inference price trendsMeasured decline of 9x to 900x per year depending on the benchmark. Covers API prices, not total cost of ownership.

Want the break-even run on your real numbers?

Tekscape is a Managed Intelligence Provider (MIP). We size local AI against your actual usage, show where cloud stays the better deal, and run the result as a managed service.

Talk to us about your numbers