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Who Should Use Paygent?

Paygent is built for teams that ship AI products to users — and need per-user cost control to keep margins healthy as usage scales.

AI Product Companies

If you're building an AI-powered product where end users call the LLM — chatbots, copilots, research agents, content tools — Paygent helps you:

  • Track exactly what each user costs you, per model, per session
  • Enforce per-user spending limits at runtime, before costs are incurred
  • Surface usage data to upgrade flows so heavy users self-select into higher tiers
  • Keep margins healthy even when a small fraction of users drive most of the LLM cost

SaaS Companies Adding AI Features

If you're layering AI into an existing product, Paygent helps you:

  • Add per-user AI cost tracking without rebuilding your billing system
  • Set different AI quotas for each of your existing pricing tiers
  • Identify which users and which features actually drive AI cost
  • Experiment with AI pricing without committing to one model up front

Multi-Tenant AI Platforms

If you're running a platform where each customer has their own end users — agency tools, white-label AI, agent marketplaces — Paygent helps you:

  • Isolate metering per tenant with product-scoped API keys
  • Enforce per-user limits inside each tenant independently
  • Show each tenant exactly what their users consumed
  • Pass AI costs through to tenants with whatever margin you set

Enterprises with Internal AI Tools

If you're deploying AI tools internally and need to track usage by team or department, Paygent helps you:

  • Monitor AI consumption per employee, team, or project
  • Set quotas that match internal budgets
  • Allocate AI costs back to the cost center that incurred them
  • Identify which internal use cases deliver real ROI

Common Use Cases

  • AI Chatbots & Copilots: Per-user spend caps so power users don't erase margins
  • Research & Analysis Agents: Per-model token quotas to control GPT-4o vs GPT-4o-mini routing
  • Document Processing: Track cost per document, per customer, per workflow
  • Code Generation Tools: Cap per-user calls during free trial, lift on upgrade
  • Customer Support AI: Limit cost per resolved conversation
  • Multi-Agent Systems: First-party callback for CrewAI; AutoGen, LangGraph, LlamaIndex, and others work via auto-instrumentation since they call the OpenAI / Anthropic SDKs underneath
  • Voice Agents: Cap session spend so a single conversation can't run away with cost
  • Educational Tutors: Per-student quotas with soft warnings as they approach limits
  • Marketing & Content Tools: Per-user generation caps tied to subscription tier

When Paygent is the Right Fit

Paygent works best when:

  • Your users call OpenAI or Anthropic — directly or through frameworks like LangChain or CrewAI
  • You need to enforce limits, not just observe them
  • You want per-model granularity — cap GPT-4o tokens separately from GPT-4o-mini
  • You'd rather not build per-user metering and gating from scratch

Getting Started

Most teams have Paygent running in under an hour:

  1. Create a product and get an API key
  2. Configure plans with spend limits, model token quotas, and your cost rates
  3. Add Paygent to your code — Paygent.init() once at startup, then wrap LLM call sites with paygent_context(user_id=...)
  4. Register gate callbacks to react to soft warnings and hard blocks