# How Anthropic Handles Billing: A Complete Breakdown

> A deep analysis of Anthropic's hybrid billing model - from Claude subscription tiers (Free, Pro, Max) and API token pricing to prompt caching, batch discounts, and spend-based rate limits. Learn how to build the same system.
- **Author**: Ayush Agarwal
- **Published**: 2026-04-22
- **Category**: AI, Billing
- **URL**: https://dodopayments.com/blogs/anthropic-billing-model

---

Anthropic operates one of the most sophisticated billing systems in the AI industry. On one side, millions of people use Claude through flat-rate subscriptions ranging from free to $200+ per month. On the other, developers pay per token through the Claude API, with pricing that varies by model, caching strategy, and processing tier. These two systems run in parallel, serving fundamentally different audiences.

This is not a straightforward "pick a plan" setup. Anthropic's billing combines [subscription pricing](https://dodopayments.com/blogs/subscription-pricing-models) with [usage-based billing](https://dodopayments.com/blogs/usage-based-billing-saas), layered with prompt caching discounts, batch processing at half price, spend-based rate tiers, and managed agent runtime charges. Understanding how these pieces fit together reveals a billing architecture that any AI company can learn from.

For context on how this compares to the competition, see our breakdown of [how OpenAI handles billing](https://dodopayments.com/blogs/openai-billing-model).

## Anthropic's Pricing at a Glance

As of April 2026, Anthropic sells to two distinct audiences, and the pricing reflects that split.

**Consumer products** (Claude chat, Claude Code, Claude Cowork) use flat-rate monthly subscriptions. Users pick a plan, pay a fixed amount, and get access to a set of models with usage limits that vary by tier.

**Developer products** (the Claude API) use pay-as-you-go token billing. Every API call is metered by input and output tokens, with the cost determined by which model you call and whether you use cost-saving features like prompt caching or batch processing.

This hybrid approach mirrors what we see across the AI industry. [OpenAI runs a similar dual model](https://dodopayments.com/blogs/openai-billing-model), as do [Cursor](https://dodopayments.com/blogs/cursor-billing-model) and [ElevenLabs](https://dodopayments.com/blogs/elevenlabs-billing-model). The pattern works because consumers want predictability while developers want granular pay-for-what-you-use pricing. Anthropic's implementation has some unique twists worth examining.

```mermaid
flowchart LR
    subgraph Consumer ["Consumer Side"]
        A[User Signs Up] --> B[Picks Plan]
        B --> C[Monthly Charge]
        C --> D[Soft Usage Caps]
        D -->|Cap Hit| E[Reduced Access Until Reset]
    end
    subgraph Developer ["Developer Side"]
        F[Developer Signs Up] --> G[API Key + Billing Setup]
        G --> H[API Calls Metered]
        H --> I[Input + Output Tokens Counted]
        I --> J[Monthly Invoice or Prepaid]
        J --> K{Within Rate Tier?}
        K -->|Yes| H
        K -->|No| L[Rate Limited]
    end
```

## Claude Subscription Tiers

Anthropic currently offers five Claude plans for consumers and teams, ranging from free to enterprise-grade. Here is how they break down as of April 2026:

| Plan | Price | Billing Type | Key Features |
| --- | --- | --- | --- |
| Free | $0/month | N/A | Chat on web/mobile/desktop, basic Sonnet access, web search, memory, extended thinking |
| Pro | $20/month ($17 annual) | Subscription | More usage, Claude Code, Cowork, Research, all models, projects, Excel/PowerPoint plugins |
| Max | From $100/month | Subscription | 5x or 20x more usage than Pro, higher output limits, early access to features, priority access |
| Team | $20-$100/seat/month | Per-seat subscription | SSO, domain verification, enterprise search, admin controls, mix standard and premium seats |
| Enterprise | $20/seat + API rates | Invoiced | SCIM, audit logs, HIPAA-ready, custom retention, compliance API, role-based access |

Several things stand out about this structure.

**Pro is the anchor.** At $20/month (or $17/month billed annually at $200), Pro is where most paying users land. It includes Claude Code for agentic coding, Cowork for collaborative work, Research for deep investigation, and access to all Claude models including Opus. The annual discount is modest but meaningful - a 15% savings that locks in commitment.

**Max is the power-user tier.** Starting at $100/month for 5x Pro usage and going higher for 20x, Max targets heavy users who need guaranteed capacity. The "early access to advanced Claude features" is a soft perk that creates FOMO-driven upgrades. Priority access during peak traffic is the practical reason power users pay 5-10x more than Pro.

**Team uses mixed seat types.** This is a clever approach. Instead of forcing every team member onto the same tier, Anthropic lets organizations assign standard seats ($20/seat annual, $25 monthly) and premium seats ($100/seat annual, $125 monthly). A 20-person team might have 3 premium seats for power users and 17 standard seats, optimizing cost without sacrificing capability where it matters.

**Enterprise bills per-seat plus API-rate usage.** This is where Anthropic diverges from [OpenAI's enterprise model](https://dodopayments.com/blogs/openai-billing-model), which uses custom invoiced pricing. Anthropic's Enterprise charges a flat $20/seat base fee, then layers on usage-based charges at API rates. Admins can set spend limits per user and per organization, giving finance teams the cost controls they need.

## API Pricing: The Token Economy

The API side of Anthropic's business uses per-token billing. A token is roughly three-quarters of a word in English (though Opus 4.7 uses a new tokenizer that may consume up to 35% more tokens for the same text). Both input tokens (what you send) and output tokens (what Claude generates) are billed at different rates.

Here are the current flagship model prices as of April 2026:

| Model | Input (per 1M tokens) | Output (per 1M tokens) | Cache Read (per 1M tokens) | Cache Write 5min (per 1M tokens) |
| --- | --- | --- | --- | --- |
| Claude Opus 4.7 | $5.00 | $25.00 | $0.50 | $6.25 |
| Claude Sonnet 4.6 | $3.00 | $15.00 | $0.30 | $3.75 |
| Claude Haiku 4.5 | $1.00 | $5.00 | $0.10 | $1.25 |

Several pricing mechanics are worth calling out.

**Output tokens cost 5x more than input tokens.** Across all three model tiers, Anthropic maintains a consistent 5:1 output-to-input price ratio. This reflects the computational reality that generating text is significantly more expensive than processing it. For developers, this means response length has a direct and outsized impact on cost.

**The model range spans 5x in price.** Haiku 4.5 costs $1 per million input tokens. Opus 4.7 costs $5. This gives developers a clear cost-performance tradeoff and encourages them to [use the right model for each task](https://dodopayments.com/blogs/ai-pricing-models) rather than defaulting to the most capable option. A classification task that Haiku handles well should not be routed to Opus.

**Prompt caching reads are 90% cheaper.** When you structure your API calls to reuse the same system prompt or context, cached reads cost just 10% of the base input price. For Opus 4.7, that drops input costs from $5/MTok to $0.50/MTok. The write cost is 1.25x the base price for a 5-minute TTL, which means caching pays for itself after a single cache hit.

**Extended caching doubles the write cost but extends TTL to 1 hour.** For applications that need longer-lived caches, Anthropic offers a 1-hour cache duration at 2x the base input price for writes (still 0.1x for reads). This is useful for persistent system prompts in chatbot applications.

### Batch API: 50% Across the Board

For workloads that can tolerate asynchronous processing, Anthropic's Batch API cuts both input and output costs in half:

| Model | Batch Input (per 1M tokens) | Batch Output (per 1M tokens) |
| --- | --- | --- |
| Claude Opus 4.7 | $2.50 | $12.50 |
| Claude Sonnet 4.6 | $1.50 | $7.50 |
| Claude Haiku 4.5 | $0.50 | $2.50 |

This is a textbook example of [dynamic pricing for usage-based SaaS](https://dodopayments.com/blogs/dynamic-pricing-usage-based-saas) - filling idle compute capacity at a discount while maintaining premium pricing for real-time requests. For batch processing 10,000 support tickets through Haiku 4.5, the cost works out to roughly $37 total.

### Beyond Tokens: Platform Feature Pricing

Anthropic also charges separately for platform features that extend Claude's capabilities:

| Feature | Price | Notes |
| --- | --- | --- |
| Web search | $10 per 1,000 searches | Plus standard token costs for search content |
| Managed Agents | $0.08 per session-hour | Active runtime only, plus token costs |
| Code execution | $0.05 per hour per container | 1,550 free hours/month per organization |
| US-only inference | 1.1x standard pricing | Data residency for compliance |
| Fast mode (Opus 4.6) | 6x standard rates | Significantly faster output |

This multi-dimensional pricing surface is similar to [OpenAI's approach](https://dodopayments.com/blogs/openai-billing-model) of charging separately for text tokens, tool calls, and compute. Each dimension has its own pricing logic, creating a billing system that requires robust [metered billing infrastructure](https://dodopayments.com/blogs/metered-billing-accurate-billing) to operate.

## How the Billing Model Actually Works

Anthropic's billing is a hybrid of three distinct mechanisms. Understanding how they interact is key to understanding why the system works.

### 1. Flat-Rate Subscriptions (Claude Consumer)

Claude plans charge a fixed monthly fee regardless of actual usage. But "more usage" does not mean unlimited. Anthropic uses **soft caps** - when a user exceeds their plan's allocation, they are not blocked entirely. Instead, access is throttled or temporarily reduced until the usage period resets.

This approach is identical to [OpenAI's soft cap strategy](https://dodopayments.com/blogs/openai-billing-model), where Plus users who exceed their GPT-5.4 allocation get downgraded to a smaller model. Soft caps beat hard caps because they keep users engaged. A user who gets a "try again later" message is less frustrated than one who sees "account blocked." It is a pattern that works well for [subscription-based products with variable consumption](https://dodopayments.com/blogs/subscriptions-usage-based-billing-saas).

The Max tier solves this problem with a straightforward multiplier: 5x or 20x the Pro allocation. For users who consistently hit Pro limits, the upgrade path is clear and the value proposition is obvious.

### 2. Pay-As-You-Go Token Billing (API)

The API uses post-paid usage billing. Developers set up a payment method, make API calls, and Anthropic bills based on actual token consumption at the end of each billing cycle. There is no prepaid credit system like [OpenAI's](https://dodopayments.com/blogs/openai-billing-model) - usage is tracked and invoiced.

This creates a different risk profile. OpenAI collects revenue before delivering service through prepaid credits. Anthropic extends credit to developers and bills afterward. The tradeoff is lower friction for developers (no need to estimate and pre-purchase credits) but higher credit risk for Anthropic.

To mitigate this, Anthropic ties rate limits to spend tiers. New accounts start with restrictive limits that increase as cumulative spending grows. This functions as an implicit trust system - you prove you are a reliable payer before getting access to higher throughput.

### 3. Spend-Based Rate Tiers

Anthropic uses a tiered rate limit system based on cumulative spending history:

- **Tier 1**: Entry-level, restrictive limits for testing and prototyping
- **Tier 2**: Moderate limits for growing applications
- **Tier 3**: Higher throughput for established production apps
- **Tier 4**: Maximum standard limits for scale
- **Enterprise**: Custom limits negotiated with sales

This pattern maps directly to [tiered pricing models](https://dodopayments.com/blogs/tiered-pricing-model-guide) used across the SaaS industry. The key insight is that rate limits serve a dual purpose: they protect Anthropic's infrastructure from abuse, and they create a monetization lever that rewards loyalty. A developer who has spent $10,000 on the platform gets better service than one who has spent $100.

## What Makes Anthropic's Billing Work

Several design decisions make this system effective. These reflect deliberate choices about balancing revenue, user experience, and operational simplicity.

**Consistent pricing ratios across models.** Every Claude model maintains a 5:1 output-to-input price ratio and a 0.1x cache read discount. This consistency makes cost estimation straightforward. Developers can model costs without memorizing different ratios for different models.

**Prompt caching as a first-class feature.** Most LLM providers offer caching, but Anthropic's implementation stands out for its simplicity. A single `cache_control` field enables automatic caching. The 90% read discount is aggressive enough to meaningfully change application economics. For a chatbot with a 2,000-token system prompt, caching saves roughly $4.50 per million requests on Opus 4.7.

> The most underappreciated billing pattern in AI right now is the dual-track model - subscriptions for consumers and per-token billing for developers running off the same infrastructure. It sounds obvious, but the metering, rate limiting, and soft cap logic required to make both tracks work simultaneously is genuinely hard engineering. Most startups should not build this from scratch.
>
> \- Ayush Agarwal, Co-founder & CPTO at Dodo Payments

**Batch processing at exactly 50% discount.** The Batch API discount is simple and memorable. No complex tiered discounts or volume commitments. Process requests asynchronously and pay half. This clarity reduces the cognitive load of cost optimization and makes it easy for developers to calculate ROI on refactoring synchronous workflows.

**Mixed seat types for teams.** Letting organizations combine standard and premium seats is a small but meaningful innovation. It acknowledges the reality that not every team member needs the same capacity. This reduces the average per-seat cost and lowers the barrier to team adoption.

## Billing Challenges Anthropic Faces

No billing system is perfect, and Anthropic's complexity creates specific challenges that any company [building a similar model](https://dodopayments.com/blogs/build-billing-system-ai-saas) should anticipate.

### Cost Predictability for Developers

Token-based pricing is inherently unpredictable. A developer building a [Claude-powered agent](https://dodopayments.com/blogs/monetize-ai-agent) cannot easily forecast monthly API costs because they depend on conversation length, tool usage, caching hit rates, and model selection. This unpredictability is one of the main reasons some developers prefer [flat fees over usage-based billing](https://dodopayments.com/blogs/usage-based-billing-vs-flat-fees-ai-saas).

Anthropic provides usage dashboards and spend controls, but the fundamental tension remains. The new tokenizer in Opus 4.7, which uses up to 35% more tokens for the same text, adds another variable that makes cost estimation harder when switching between models.

### Multi-Dimensional Metering Complexity

Anthropic does not just meter tokens. They meter base tokens, cached tokens (reads and writes at different TTLs), batch tokens, web search calls, agent session-hours, code execution container-hours, and data residency multipliers. Each dimension has its own pricing, and some stack multiplicatively (prompt caching discounts combine with batch discounts).

Building [accurate metered billing](https://dodopayments.com/blogs/metered-billing-accurate-billing) at this scale requires robust event ingestion, real-time aggregation, and clear reporting. For companies building on top of the Claude API and reselling access, tracking these dimensions accurately is essential for maintaining margins.

### Enterprise Pricing Complexity

The Enterprise plan's "$20/seat + API rates" model creates a billing surface that combines per-seat recurring charges with variable usage charges. Finance teams need to forecast both components separately, and the variable portion depends on how aggressively individual users leverage Claude. This makes budgeting harder than a pure per-seat model.

### Subscription-API Overlap

A Pro user who also uses the API pays twice for access to the same underlying models. The subscription covers the Claude interface with usage caps, while API usage is billed separately at per-token rates. This dual billing can feel redundant to developer-users who work in both interfaces.

## How to Build Similar Billing with Dodo Payments

If you are building an AI product and want to replicate Anthropic's billing model, you need three core capabilities: [subscription billing](https://docs.dodopayments.com/features/subscription) for consumer plans, [credit-based billing](https://docs.dodopayments.com/features/credit-based-billing) for prepaid API access, and [usage event tracking](https://docs.dodopayments.com/features/usage-based-billing/introduction) for token metering. Dodo Payments supports all three natively.

For a deeper look at this pattern across the industry, see the guide to [building a billing system for AI SaaS](https://dodopayments.com/blogs/build-billing-system-ai-saas).

### Step 1: Create Subscription Products for Consumer Plans

Set up separate [subscription products](https://docs.dodopayments.com/features/subscription) for each tier. These are straightforward recurring charges with feature entitlements.

```typescript
import DodoPayments from "dodopayments";

const client = new DodoPayments({
  bearerToken: process.env["DODO_PAYMENTS_API_KEY"],
});

// Create a checkout session for a Pro-equivalent plan
const session = await client.checkoutSessions.create({
  product_cart: [{ product_id: "prod_claude_pro", quantity: 1 }],
  customer: { email: "user@example.com" },
  return_url: "https://yourapp.com/billing",
});
```

For per-seat billing (like Anthropic's Team plan with mixed seat types), use quantity-based subscriptions where each seat type is a separate line item.

### Step 2: Set Up Credit Packs for API Billing

Create a fiat [credit entitlement](https://docs.dodopayments.com/features/credit-based-billing) for API users who prefer prepaid access. This gives developers a clear balance they can top up as needed.

- **Credit Type:** Fiat Credits (USD)
- **Credit Expiry:** Never
- **Overage:** Disabled (API calls fail at zero balance)

Then create one-time payment products for credit packs ($10, $50, $100, $500) and attach the credit entitlement to each.

### Step 3: Track Token Usage with Usage Events

Create meters to track token consumption. For an LLM billing system, you need separate meters for input and output tokens since they have different costs.

```typescript
// After each LLM request, ingest usage events
await client.usageEvents.ingest({
  events: [
    {
      event_id: `req_${requestId}_input`,
      customer_id: customerId,
      event_name: "llm.input_tokens",
      timestamp: new Date().toISOString(),
      metadata: {
        model: "claude-opus-4-7",
        tokens: "2400",
        cached: "false",
      },
    },
    {
      event_id: `req_${requestId}_output`,
      customer_id: customerId,
      event_name: "llm.output_tokens",
      timestamp: new Date().toISOString(),
      metadata: {
        model: "claude-opus-4-7",
        tokens: "1200",
      },
    },
  ],
});
```

Link both meters to your fiat credit entitlement. Configure "meter units per credit" to match your pricing. For example, if you charge $5 per 1M input tokens (matching Opus pricing), that is 200,000 tokens per dollar.

For the complete implementation pattern, see the [usage-based billing guide](https://docs.dodopayments.com/features/usage-based-billing/introduction) in the Dodo Payments docs.

### Step 4: Implement Soft Caps for Subscription Users

Soft caps are application-level logic. Track usage for subscription users with the same [usage-based billing](https://docs.dodopayments.com/features/usage-based-billing/introduction) meters, but instead of deducting credits, use the data to make routing decisions:

```typescript
async function getModelForUser(customerId: string, plan: string) {
  const usage = await getCurrentPeriodUsage(customerId);
  const cap = getPlanCap(plan); // Pro = X, Max5x = 5X, Max20x = 20X

  if (usage.totalCalls > cap) {
    // Throttle or degrade - Anthropic reduces access until reset
    return { model: "claude-haiku-4-5", throttled: true };
  }

  return { model: "claude-opus-4-7", throttled: false };
}
```

This replicates Anthropic's pattern of reducing access when subscription users exceed their allocation, rather than blocking them entirely.

### Step 5: Handle Billing Events with Webhooks

Set up [webhooks](https://docs.dodopayments.com/developer-resources/webhooks) to automate billing lifecycle events - low balance alerts, subscription renewals, failed payments, and plan changes.

```typescript
// In your webhook handler
if (event.type === "credit.balance_low") {
  const { customer_id, available_balance } = event.data;
  await sendLowBalanceEmail(customer_id, available_balance);
}

if (event.type === "subscription.renewed") {
  const { customer_id, product_id } = event.data;
  await resetUsageCaps(customer_id, product_id);
}
```

## Anthropic vs OpenAI: Billing Model Comparison

For AI founders deciding which billing architecture to emulate, here is how the two leading providers compare:

| Dimension | Anthropic | OpenAI |
| --- | --- | --- |
| Consumer billing | Flat-rate subscriptions with soft caps | Flat-rate subscriptions with soft caps |
| API billing | Post-paid usage (billed monthly) | Prepaid credits (pay before use) |
| Cost savings | Prompt caching (90%), Batch API (50%) | Cached input (90%), Batch API (50%) |
| Model pricing ratio | 5:1 output/input across all models | 4-6x output/input, varies by model |
| Rate limit system | Spend-based tiers (1-4 + Enterprise) | Spend-based tiers |
| Team billing | Mixed seat types (standard + premium) | Per-seat, single tier |
| Enterprise billing | Per-seat + API-rate usage | Custom invoiced |
| Additional features | Agent runtime, code execution, web search | Tool calls, image/audio tokens, storage |

The biggest architectural difference is API payment timing. OpenAI's prepaid credit system collects revenue before service delivery, eliminating credit risk. Anthropic's post-paid model reduces developer friction but accepts higher credit risk. Both approaches have merit, and the choice depends on your user base and risk tolerance.

For a deeper comparison across more AI providers, see the [AI pricing models guide](https://dodopayments.com/blogs/ai-pricing-models) and [AI billing platforms overview](https://dodopayments.com/blogs/ai-billing-platforms).

## Key Takeaways for AI Founders

Anthropic's billing model is not just a pricing page. It is a revenue architecture that balances multiple competing goals: low barrier to entry, predictable consumer revenue, cost transparency for developers, and scalable monetization.

If you are building an AI product, the core lessons are:

1. **Hybrid billing works.** Subscriptions for consumers and usage-based billing for developers can coexist. Anthropic and [OpenAI](https://dodopayments.com/blogs/openai-billing-model) both prove this pattern at scale.

2. **Caching discounts drive adoption.** A 90% discount on cached reads is not just a cost optimization. It is a pricing strategy that incentivizes developers to build more deeply integrated applications with consistent system prompts.

3. **Mixed seat types reduce adoption friction.** Letting teams combine standard and premium seats acknowledges that usage varies within organizations. This lowers the average per-seat cost and accelerates team purchases.

4. **Soft caps beat hard caps.** Degrading service is better than blocking it entirely. Users stay engaged and see a clear reason to upgrade.

5. **Multi-dimensional metering is necessary for AI.** Input tokens, output tokens, cached tokens, tool calls, compute time, and agent runtime all have different costs. Your billing system needs this granularity.

Building this kind of billing infrastructure from scratch is a significant engineering investment. Platforms like [Dodo Payments](https://dodopayments.com/pricing) provide the building blocks - [credit-based billing](https://docs.dodopayments.com/features/credit-based-billing), [usage event ingestion](https://docs.dodopayments.com/features/usage-based-billing/introduction), [subscription management](https://docs.dodopayments.com/features/subscription), and [webhook automation](https://docs.dodopayments.com/developer-resources/webhooks) - so you can focus on your AI product instead of reinventing billing.

For hands-on guides, see the [Claude Code monetization tutorial](https://dodopayments.com/blogs/charge-users-claude-code-project) or the [Midjourney billing breakdown](https://dodopayments.com/blogs/midjourney-billing-model) to compare how image generation platforms handle similar challenges.

## FAQ

### How does Anthropic charge for API usage?

Anthropic uses per-token billing for the Claude API. Input and output tokens are priced separately, with output tokens costing 5x more than input tokens across all models. As of April 2026, Opus 4.7 costs $5/$25 per million input/output tokens, Sonnet 4.6 costs $3/$15, and Haiku 4.5 costs $1/$5. Prompt caching reads reduce input costs by 90%, and the Batch API offers a flat 50% discount on all token costs.

### What is the difference between Claude Pro and Claude Max?

Claude Pro costs $20/month ($17 annual) and includes access to all Claude models, Claude Code, Cowork, Research, and projects with standard usage limits. Claude Max starts at $100/month and provides 5x or 20x more usage than Pro, higher output limits, early access to new features, and priority access during peak traffic. Max is designed for power users who consistently hit Pro limits.

### How does Anthropic's prompt caching save money?

Prompt caching lets you reuse previously processed context across API calls. When you send the same system prompt repeatedly, subsequent requests read from cache at just 10% of the standard input price. For Opus 4.7, cached reads cost $0.50/MTok versus $5.00/MTok for standard input. The cache write costs 1.25x the base price for a 5-minute TTL (or 2x for a 1-hour TTL), meaning caching pays for itself after a single cache hit.

### Can you build an Anthropic-style billing system for your own AI product?

Yes. You need subscription billing for consumer plans, credit-based billing for prepaid API access, and usage event tracking for token metering. Dodo Payments provides all three natively, plus webhook automation for billing lifecycle events. Combine subscription products for consumer tiers with usage meters for API billing to replicate the dual-track model.

### How does Anthropic's billing compare to OpenAI's?

Both use hybrid models with consumer subscriptions and developer token billing. The key difference is payment timing: OpenAI uses prepaid credits (pay before use), while Anthropic uses post-paid usage billing (pay after use). Both offer roughly 90% discounts on cached input and 50% batch discounts. Anthropic's Team plan allows mixed seat types, while OpenAI uses a single per-seat rate. For a detailed comparison, see the OpenAI billing model breakdown.

## Final Thoughts

Anthropic's billing model is a case study in how to monetize AI across two very different audiences from the same platform. The combination of flat-rate subscriptions, per-token API billing, aggressive caching discounts, and spend-based rate tiers creates a system that serves casual Claude users and enterprise API developers without compromise.

The complexity is real, but each layer serves a purpose. Subscriptions provide revenue predictability. Token billing aligns cost with value. Caching discounts incentivize deeper integration. Rate tiers reward loyalty. Mixed seat types lower team adoption barriers.

For AI founders looking to implement similar billing, the infrastructure exists today. Start with the [Dodo Payments pricing page](https://dodopayments.com/pricing) to see how the building blocks fit together, or explore the [usage-based billing documentation](https://docs.dodopayments.com/features/usage-based-billing/introduction) to start building your own dual-track billing system.
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