# OpenAI API Pricing Calculator: Cost of ChatGPT API for ERP Workloads

> Source: https://netray.co/tools/openai-api-pricing-calculator-for-erp-workloads
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Estimate monthly OpenAI-style API cost for ERP tasks like quote drafting, order summaries and BOM questions, and compare small, standard and frontier price tiers.

## Category

on-prem-ai

## Audience

- CIO
- ERP manager
- AI project lead

## Tool

**Kind:** calculator

**Currency:** $

**Inputs:**

#### ERP workload preset

**Key:** preset

**Type:** select

**Default:** 1

**Options:**

##### Quote draft from RFQ

**Value:** 1

##### Order summary

**Value:** 2

##### BOM question with context

**Value:** 3

##### Custom token counts

**Value:** 4

**Help:** Presets use typical prompt plus retrieved-data sizes. Choose Custom to use your own.

#### Requests per working day

**Key:** req_day

**Type:** number

**Default:** 400

**Min:** 0

**Step:** 10

**Unit:** requests

#### Working days per month

**Key:** days

**Type:** number

**Default:** 22

**Min:** 1

**Max:** 31

**Step:** 1

**Unit:** days

#### Custom input tokens per request

**Key:** custom_in

**Type:** number

**Default:** 2500

**Min:** 0

**Step:** 100

**Unit:** tokens

**Help:** Used only when the preset is Custom.

#### Custom output tokens per request

**Key:** custom_out

**Type:** number

**Default:** 500

**Min:** 0

**Step:** 50

**Unit:** tokens

**Help:** Used only when the preset is Custom.

#### Your input price

**Key:** in_price

**Type:** number

**Default:** 2.5

**Min:** 0

**Step:** 0.05

**Unit:** $ per 1M tokens

**Help:** Copy the current price from the provider price page. The default is illustrative.

#### Your output price

**Key:** out_price

**Type:** number

**Default:** 10

**Min:** 0

**Step:** 0.05

**Unit:** $ per 1M tokens

#### Cached share of input

**Key:** cached

**Type:** slider

**Default:** 20

**Min:** 0

**Max:** 90

**Step:** 5

**Unit:** %

**Help:** Repeated prefix such as system prompt. Assumes cached tokens bill at half price; check your provider.

#### Machines or users served

**Key:** machines

**Type:** number

**Default:** 40

**Min:** 1

**Step:** 1

**Unit:** machines

**Outputs:**

#### Input tokens per request

**Key:** in_tok

**Expr:** if(eq(preset,1),3200,if(eq(preset,2),1800,if(eq(preset,3),6500,custom_in)))

**Format:** integer

#### Output tokens per request

**Key:** out_tok

**Expr:** if(eq(preset,1),700,if(eq(preset,2),300,if(eq(preset,3),400,custom_out)))

**Format:** integer

#### Requests per month

**Key:** monthly_req

**Expr:** req_day * days

**Format:** integer

#### Effective input price per 1M after caching

**Key:** eff_in

**Expr:** in_price * (1 - cached / 100 * 0.5)

**Format:** decimal

#### Monthly API cost at your prices

**Key:** monthly_cost

**Expr:** monthly_req * (in_tok * eff_in + out_tok * out_price) / 1000000

**Format:** currency

**Primary:** true

#### Annual API cost

**Key:** annual_cost

**Expr:** monthly_cost * 12

**Format:** currency

#### Monthly cost per machine or user

**Key:** per_machine

**Expr:** monthly_cost / machines

**Format:** currency

**Primary:** true

#### Monthly cost, small tier (0.15 / 0.60 per 1M)

**Key:** cost_small

**Expr:** monthly_req * (in_tok * 0.15 + out_tok * 0.6) / 1000000

**Format:** currency

**Description:** Illustrative small-model rates, no caching.

#### Monthly cost, standard tier (2.50 / 10 per 1M)

**Key:** cost_std

**Expr:** monthly_req * (in_tok * 2.5 + out_tok * 10) / 1000000

**Format:** currency

**Description:** Illustrative mid-tier rates, no caching.

#### Monthly cost, frontier tier (10 / 40 per 1M)

**Key:** cost_large

**Expr:** monthly_req * (in_tok * 10 + out_tok * 40) / 1000000

**Format:** currency

**Description:** Illustrative top-tier rates, no caching.

**Chart:**

#### Monthly cost vs requests per day (small, standard and frontier tier)

**Type:** line

**X:** req_day

**From:** 50

**To:** 3000

**Steps:** 30

**Series:**

- cost_small
- cost_std
- cost_large

**Disclaimer:** Tier rates and presets are illustrative planning numbers, not a quote. API prices change often. Replace them with the current published prices for the exact models you plan to use, and measure real token counts from a pilot.

## Content

**Intro:** This OpenAI API pricing calculator turns requests per day and token counts into a monthly bill, using ERP workload presets for quotes, order summaries and BOM questions. It also compares small, standard and frontier price tiers and shows cost per machine.

**Sections:**

#### How the API cost is calculated

**Body:** API pricing is per million tokens, billed separately for input and output. Monthly cost equals requests per month times (input tokens times input price plus output tokens times output price) divided by one million. Output tokens usually cost three to five times more than input tokens, so a short answer to a long prompt is cheaper than the reverse. The cached share setting models prompt caching, where a repeated prefix such as your system prompt and tool definitions is billed at a discount. Set it to zero if you are unsure.

**Bullets:**

- Tokens are roughly four characters or three quarters of a word in English
- ERP data pasted into the prompt is the biggest input driver
- Reasoning models can bill hidden thinking tokens as output
- Check batch and cached-input discounts on the price page

#### ERP workload presets and what they assume

**Body:** The quote draft preset assumes an RFQ email, a few similar past quotes and pricing rules, about 3,200 input tokens, with a 700 token reply. The order summary preset assumes one sales order with lines and status history, about 1,800 tokens in and 300 out. The BOM question preset assumes a multi-level BOM extract plus a question, about 6,500 tokens in and 400 out. These are planning figures; your real numbers depend on how much ERP data you retrieve. Pick Custom and enter measured counts once you have a pilot.

#### Reading the model tier comparison

**Body:** The three tier rows apply the same workload to a small, a standard and a frontier price level, so you can see how much model choice matters compared with volume. Often a small model handles summaries and extraction, while a larger one is reserved for hard reasoning, which is why routing cheap tasks to cheaper models saves most of the bill. The chart sweeps requests per day so you can see where each tier crosses a budget line. The cost per machine figure divides the bill by the machines or users you serve, which is the number finance usually asks for.

#### When API cost is not the real question

**Body:** At low volume, API pricing is almost always cheap relative to the project. The questions that decide the architecture are data residency, confidentiality of drawings and pricing, and whether ERP data may leave your network. If those rule out a public API, an open-weight model on your own GPUs is the alternative, and the comparison then becomes hardware and operations cost rather than tokens. Use this calculator to size the cloud option honestly, then compare it with an on-prem estimate before deciding.

**Cta:** Netray designs ERP AI for private and on-prem deployment as well as cloud APIs, so you can compare both honestly. We can size your workloads from real ERP data.

**Faq:**

#### How much does the OpenAI API cost per month?

**Answer:** It depends on model, tokens per request and volume. A few hundred ERP requests a day on a small or mid tier model is usually tens to a few hundred dollars per month. Enter your own counts and current prices above for a real estimate.

#### Is the ChatGPT API cost the same as a ChatGPT subscription?

**Answer:** No. ChatGPT subscriptions are per user per month, while the API bills per token used. For an ERP integration the API is the relevant model, because software sends requests on behalf of many users.

#### What does ChatGPT cost per machine?

**Answer:** Divide the monthly API bill by the number of machines or users it serves. The calculator shows this figure. It is useful for chargeback, but token cost per request is the more stable unit.

#### How do I reduce OpenAI API cost?

**Answer:** Send less ERP data per request, cache the repeated prompt prefix, cap output length, and route simple tasks to a smaller model. Measuring real token usage in a pilot usually reveals the largest savings.

## Search terms

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