Billing for LLM tokens

21 min

This guide shows how to bill customers for an AI chatbot product using:

API calls (per request)

LLM tokens (input + output, priced differently by model)

This is a real-world billing pattern used by most AI products.

By the end, you will have:

Multiple meters (API calls + tokens)

A pricing plan with multiple rate types

Dimensional pricing (model + token type)

A customer receiving a fully broken-down invoice

The Scenario

You are building an AI chatbot SaaS.

Your pricing model:

$0.01 per API request

Token usage billed based on:

Model (Model A vs Model B)

Token type (input vs output)

This reflects how real AI systems are priced.

What You’ll Build

You will implement:

API Request → Flat charge LLM Tokens → Dimensional pricing (model + type) → Combined into a single invoice

Step 1: Create Meters

You need two meters.

1A. API Calls Meter (Simple)

Navigate

Go to Meters

Click Create Meter

Configure

Label: API Calls

API Name: auto-generated

Meter Type: Count

No dimensions required.

Note: You can add dimensions (endpoint, region, etc.) for analytics or cost tracking, but they are not needed for billing here.

Click Create.

1B. LLM Tokens Meter (Dimensional)

Navigate

Go to Meters

Click Create Meter

Configure

Label: LLM Tokens

Meter Type: Count

Add Dimensions

Add:

model

type (input, output)

These are required for realistic AI pricing.

Click Create.

Step 2: Create a Pricing Plan

Navigate

Go to Pricing

Click Create Pricing Plan

Configure

Name: Chatbot Usage Plan

Billing Period: Monthly

Click Continue.

Step 3: Add Rates

You will add two rates to the same plan.

3A. API Calls Rate (Per Unit)

Click Add Rate

Select Usage-Based

Configure:

Meter: API Calls

Rate Model: Per Unit

Price: 0.01

This means:

Every API request = $0.01

3B. LLM Tokens Rate (Dimensional)

Click Add Rate again

Select Usage-Based

Configure:

Meter: LLM Tokens

Rate Model: Dimensions

Tier Model: Per Unit

Define Pricing Matrix

You will define rates based on:

model

type

Example:

model	type	price per token
model_a	input	0.00001
model_a	output	0.00002
model_b	input	0.000005
model_b	output	0.00001

Each row is a separate pricing rule.

Important

You are pricing each combination independently

This is how real AI pricing works

Click Save.

Step 4: Create a Customer

Navigate

Go to Customers

Click Create Customer

Configure

Customer Name: Test Customer

Customer ID: test_customer

Click Save.

Step 5: Assign Pricing Plan

Open the customer

In Pricing Plans, click Assign Plan

Configure:

Plan: Chatbot Usage Plan

Start Date: Now

Click Assign Plan.

Step 6: Send Usage

Now you simulate real usage.

Navigate

Go to Meters

Open LLM Tokens

Click Event Upload

Example Event (Tokens)

[ { "meterApiName": "llm_tokens", "meterValue": 1000, "dimensions": { "model": "model_a", "type": "input" } } ]

Send multiple events for different combinations:

model_a + input

model_a + output

model_b + input

API Calls Event

Send another event using the API Calls meter:

[ { "meterApiName": "api_calls", "meterValue": 10 } ]

Step 7: View the Invoice

Navigate

Go to Customers

Open your customer

What You’ll See

Your invoice will include:

API call charges

Token usage charges

By default, token charges are broken down by:

Model

Token type

Example:

Model A - Input Tokens → $X Model A - Output Tokens → $Y Model B - Input Tokens → $Z API Calls → $W Total → $T

Important Note

You do not have to expose this level of detail to your customers.

This breakdown is the default

You can simplify how invoices are presented

If You Don’t See Data

Refresh the page

Amberflo does not auto-refresh

If still empty:

Confirm events were sent

Confirm dimensions match pricing

Confirm plan is assigned

What You Just Built

You implemented a real AI billing system:

Multiple meters

Mixed pricing models

Dimensional pricing

Unified invoice

Why This Matters

This pattern lets you:

Price different models differently

Charge input vs output tokens separately

Combine request-based and usage-based billing

This is how modern AI products monetize.

Next Steps

Add tiered pricing for tokens

Introduce discounts or credits

Connect to cost tracking for margin visibility

For internal cost attribution, see: WorkloadsWorkloads