AI Pricing Models
How to price AI features, meter token consumption, implement credit burn-down systems, and stay agile as AI model costs evolve. From per-token billing to agent-based pricing — every model your AI product needs.
AI Pricing Models
Every model for AI monetization
AI products need pricing models that handle variable, high-frequency consumption — from individual token metering to complex multi-step agent workflows.
Token-based pricing
Charge per input and output token processed by your AI models. Different models can have different per-token rates. The foundational pricing unit for LLM-powered products.
GPT-4: $0.03/1K tokens, Claude: $0.025/1K, Llama: $0.008/1K
Credit-based pricing
Pre-purchased credit pools consumed by different AI actions. Each action has a credit cost, abstracting compute complexity into a simple, predictable unit for customers.
1,000 credits for $99. Text query: 1 credit. Image gen: 10 credits.
Tiered AI access
Subscription tiers with different AI usage allowances. Free tiers include limited AI, premium tiers unlock higher limits and advanced models. Natural upgrade path as usage grows.
Free: 10K tokens/mo. Pro: 500K tokens/mo. Enterprise: unlimited.
Hybrid subscription + tokens
Monthly fee includes a token or credit allocation. Usage above the included amount is charged per unit. The most popular model for AI products — predictability meets scalability.
$99/mo includes 500K tokens. Overage: $0.002 per 1K tokens.
Agent and workflow pricing
Price AI agent executions, autonomous workflows, or multi-step reasoning chains. Per-run, per-step, or compute-time billing for agentic AI that chains multiple actions.
Per agent run: $0.10. Per action step: $0.01. Compute: $0.05/min.
Outcome-based pricing
Charge based on successful AI-generated outcomes rather than raw consumption. Higher per-outcome prices, but customers only pay for value delivered.
Per successful analysis: $1.00. Per document processed: $0.50.
Credit Enforcement
Metering, burn-down, and enforcement
AI pricing only works if you can measure consumption accurately, show it in real time, and enforce limits before costs run away.
Meter every event
The Monetization Engine ingests every token, inference call, agent action, and compute unit at high frequency — and rates them against customer rate cards in real time.
Real-time burn-down
Both you and your customers see remaining balance, usage rate, and projected depletion date — full transparency, no end-of-month surprises.
Enforce at the API level
Entitlement management warns customers as they approach their cap, throttles usage, or triggers auto-refill — preventing AI cost overruns before they happen.
AI Pricing Agility
AI costs change. Your pricing should keep up.
AI model costs change constantly — new models launch, providers cut prices, and economics shift. Decouple pricing from your application code and you adapt instantly, without shipping a release.
Rate cards in a control plane
Adjust per-token rates, credit costs, and model-specific pricing from a control plane — not buried in application code.
No code deployments
No engineering sprints, no waiting. Product managers change pricing and it takes effect across every customer immediately.
Capture the margin
When a provider drops prices or you add a model, update the rate card instantly. This is what separates AI companies that capture margin from those that give it away.
Design your AI pricing strategy
Our team can help you design the right AI pricing model — from token metering strategy to credit system design to billing integration.
FAQ
AI pricing FAQ
Monetize your AI features
From token metering to credit burn-down to agent pricing. Nalpeiron handles the AI monetization infrastructure so you can focus on building.