# ChatGPT vs On-Prem LLM TCO Calculator

> Source: https://netray.co/tools/chatgpt-vs-on-prem-llm-tco-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Compare yearly cost of a hosted LLM API with a self-hosted GPU server for the same workload and find the break-even volume.

## Category

on-prem-ai

## Audience

- CIO
- Finance controller
- IT manager

## Tool

**Kind:** calculator

**Currency:** $

**Inputs:**

#### Active users

**Key:** users

**Type:** number

**Default:** 400

**Min:** 1

**Step:** 10

**Unit:** users

#### Questions per user per working day

**Key:** q_day

**Type:** number

**Default:** 8

**Min:** 0

**Step:** 1

#### Working days per year

**Key:** days

**Type:** number

**Default:** 250

**Min:** 1

**Max:** 366

**Step:** 1

**Unit:** days

#### Total tokens per question (input + output)

**Key:** tok_q

**Type:** number

**Default:** 5000

**Min:** 100

**Step:** 100

**Unit:** tokens

#### API blended price per 1M tokens

**Key:** price

**Type:** number

**Default:** 3.5

**Min:** 0

**Step:** 0.25

**Unit:** $

**Help:** Weighted average of input and output list prices for your expected mix

#### GPU server purchase and setup

**Key:** capex

**Type:** number

**Default:** 60000

**Min:** 0

**Step:** 5000

**Unit:** $

#### Depreciation period

**Key:** life

**Type:** slider

**Default:** 4

**Min:** 2

**Max:** 7

**Step:** 1

**Unit:** years

#### Average server power draw

**Key:** kw

**Type:** number

**Default:** 2.5

**Min:** 0

**Step:** 0.1

**Unit:** kW

#### Electricity price

**Key:** kwh

**Type:** number

**Default:** 0.12

**Min:** 0

**Step:** 0.01

**Unit:** $/kWh

#### Operations effort

**Key:** fte

**Type:** number

**Default:** 0.25

**Min:** 0

**Step:** 0.05

**Unit:** FTE

**Help:** Share of one engineer for patching, monitoring, model updates and support

#### Loaded annual cost per engineer

**Key:** fte_cost

**Type:** number

**Default:** 110000

**Min:** 0

**Step:** 5000

**Unit:** $

#### Hardware support and spares

**Key:** support

**Type:** number

**Default:** 8

**Min:** 0

**Max:** 30

**Step:** 1

**Unit:** % of capex per year

**Outputs:**

#### Tokens per year

**Key:** tok_year

**Expr:** users * q_day * days * tok_q

**Format:** integer

#### Hosted API cost per year

**Key:** api

**Expr:** tok_year / 1000000 * price

**Format:** currency

**Primary:** true

#### Hardware depreciation per year

**Key:** dep

**Expr:** capex / life

**Format:** currency

#### Power and cooling per year

**Key:** power

**Expr:** kw * 8760 * 1.4 * kwh

**Format:** currency

**Description:** Assumes continuous operation and a facility overhead factor of 1.4

#### Operations staff per year

**Key:** staff

**Expr:** fte * fte_cost

**Format:** currency

#### Support and spares per year

**Key:** sup

**Expr:** capex * support / 100

**Format:** currency

#### On-prem cost per year

**Key:** onprem

**Expr:** dep + power + staff + sup

**Format:** currency

**Primary:** true

#### Break-even tokens per year

**Key:** breakeven

**Expr:** if(gt(price, 0), onprem / price * 1000000, 0)

**Format:** integer

**Description:** Above this yearly volume the on-prem server is cheaper, if one server can serve it

**Chart:**

#### Annual cost vs active users

**Type:** line

**X:** users

**From:** 25

**To:** 3000

**Steps:** 30

**Series:**

- api
- onprem

**Disclaimer:** On-prem cost is shown for one server and does not rise with users, so check capacity in the GPU sizing calculator. Excludes software, networking, redundancy, one-time integration work, model evaluation effort and data transfer. Hosted API prices change frequently.

## Content

**Intro:** Hosted APIs are cheaper at low volume, and owned GPUs are cheaper at high, steady volume. This calculator puts both on one yearly basis so you can see where the lines cross for your workload.

**Sections:**

#### How to use it

**Body:** Enter your expected user count, questions per day and total tokens per question including retrieved context. Use the blended API price that matches your input and output mix. For the on-prem side, enter a server price you have quoted, its power draw and the share of an engineer who will run it. The outputs show yearly cost for each path, and the break-even token volume at which they match.

#### The method

**Body:** API cost is tokens per year divided by one million, times the blended price. On-prem cost is straight-line depreciation plus power with a facility overhead factor, a staff allocation and annual hardware support. The two paths scale differently: API cost grows with every token, while a server has a fixed cost until you hit its capacity, then needs a second one. That is why the chart shows a flat on-prem line against a rising API line.

**Bullets:**

- Include staff time, which is often the biggest on-prem line
- Include a spare or second node if the assistant becomes business critical
- Price the API on the model quality you actually need

#### Reading the result

**Body:** With the defaults, the hosted API costs far less than the server at 400 users. The lines cross at much higher volume, which is why a pilot should almost always start on an API or a rented GPU. On-prem wins on cost mainly when usage is heavy and constant, such as agents running overnight batches. It can also win on non-cost grounds: data that may not leave your network, export control, latency inside the plant, or independence from provider pricing.

#### Common mistakes

**Body:** Comparing the API bill with the server price alone leaves out staff, power and support, which flatters on-prem. The opposite error is ignoring agent loops, which can multiply tokens tenfold and move the break-even a long way. Do not compare a small open-weight model with a frontier API without testing quality. Re-run the numbers each year, as both token prices and GPU prices keep moving.

**Cta:** If you want the numbers checked against your own prompts and a quoted server, we can benchmark open-weight models on your workload and help you build the case.

**Faq:**

#### When does on-prem become cheaper?

**Answer:** When yearly token volume exceeds the break-even shown and one server can handle the load. That generally means large user bases, heavy agent workloads or batch processing, rather than light chat usage by a few hundred people.

#### Is cost the only reason to go on-prem?

**Answer:** No. Data control, export-control rules, air-gapped sites, latency and vendor independence often decide it. Many companies pay more for private deployment because the data cannot be sent to a public service.

#### What about renting GPUs instead?

**Answer:** Renting converts capex to a monthly fee and suits pilots and uncertain demand. Replace depreciation with the rental cost for a quick comparison, and check that data residency and contract terms fit your needs.

#### Do open-weight models match ChatGPT quality?

**Answer:** For many ERP lookup, summarization and drafting tasks they are adequate, but results vary by task. Evaluate candidate models on your own questions before using cost as the deciding factor.

## Search terms

chatgpt vs on prem llm cost, llm tco calculator, self hosted llm vs api cost, private llm total cost of ownership, openai api vs local llm break even, on premise llm roi, gpu server cost vs api, enterprise llm build vs buy

---
Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json