> ## Documentation Index
> Fetch the complete documentation index at: https://docs.paygentic.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Pricing Overview

> Build flexible billing for your services

Paygentic lets you implement any pricing model through five components that work together seamlessly.

## Core components

### 1. Products

Organize your services. Each product represents something you sell - an LLM API, data processing pipeline, or analytics platform.

### 2. Features

Gate access and enforce quotas. Attach features to prices so customers automatically receive the right entitlements when they subscribe — boolean flags, static config values (e.g., seat limits), or metered quotas that decrement as they consume.

### 3. Billable metrics

Track consumption. Whether it's tokens processed, queries run, or gigabytes stored - if you can measure it, you can bill for it.

### 4. Fees

Add fixed charges. Setup fees, monthly subscriptions, platform access - any predictable cost that doesn't vary with usage.

### 5. Plans

Package your pricing. Offer different tiers to different customer segments - from hobbyists to enterprises.

### 6. Prices

Define costs with standard per-unit pricing — including percentage-style multipliers and revenue share (a unit price of `0.1` charges 10% of the metered value).

<Info>
  Features and metered entitlements require Standard Billing (`billingVersion: 1`). See
  [Billing Versions](/platform/billing/billing-versions) for details.
</Info>

## How it works

Start with a **product**, add **metrics** to track usage, define **fees** for fixed charges, create **plans** for different customers, attach **prices** to each metric within those plans, and link **features** to prices to control what each tier can access.

Each product can have multiple plans, and each plan combines fees with metric prices and feature entitlements. This lets you offer the same service at different price points to different customer segments.

## Real-world example: LLM provider

Let's say you're launching an LLM service:

**Product:** "Neural Language Engine"

**Metrics to Track:**

* Input tokens consumed
* Output tokens generated
* Fine-tuning compute hours
* Model API calls

**Pricing Strategy:**

*Developer Tier*

* Input: \$0.002/1K tokens
* Output: \$0.006/1K tokens
* 10K free tokens monthly

*Business Tier*

* Input: \$0.001/1K tokens (50% volume discount)
* Output: \$0.003/1K tokens
* Fine-tuning: \$3/hour
* Priority queue access

*Enterprise*

* Custom negotiated rates
* Committed usage discounts
* SLA guarantees

## Data platform example

Running a data warehouse service:

**Product:** "Cloud Analytics"

**Metrics:**

* Query compute seconds
* Storage GB-months
* Data egress GB
* Concurrent connections

**Billing Models:**

* **On-demand:** Pay only for queries run
* **Reserved:** Pre-purchase compute at 40% discount
* **Committed:** Annual contracts with guaranteed minimums

## Payment flexibility

Paygentic supports both:

* **Real-time billing** - Charge instantly as usage occurs
* **Post-paid invoicing** - Bill monthly/annually for accumulated usage

Mix and match within the same plan. Charge upfront for subscriptions, bill usage monthly.

## Get started

1. [Products](/platform/pricing/products) - Set up your service structure
2. [Features](/platform/pricing/features) - Gate access and enforce quotas
3. [Billable Metrics](/platform/pricing/billable-metrics) - Configure usage tracking
4. [Fees](/platform/pricing/fees) - Add fixed charges
5. [Plans](/platform/pricing/plans) - Design customer tiers
6. [Prices](/platform/pricing/prices) - Implement pricing strategies