PricingModel

*Pricing for Kaizen agents*

# KaizenAgentic Pricing Model

### 1. Base Assumption

* **Token Cost (C):** The unit price per token for the underlying foundation model (e.g., OpenAI GPT-4o, Anthropic Claude, etc.).
* KaizenAgentic charges are always calculated as a multiple of this base token cost.

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### 2. Capability Multipliers

Each subagent is classified by its **capability tier**, which reflects complexity, optimization overhead, and real-world utility.

| **Tier** | **Agent Capability**   | **Multiplier (x)** | **Example Use Case**                                           |
| -------- | ---------------------- | ------------------ | -------------------------------------------------------------- |
| 1x       | Baseline wrapper agent | 1×                 | Simple automation around base LLM calls                        |
| 2x       | Enhanced agent         | 2×                 | Adds logging, minimal optimization, lightweight feedback loops |
| 3x       | Professional agent     | 3×                 | Integrated metrics, test coverage deltas, developer UX signals |
| 4x       | Expert agent           | 4×                 | Adaptive refinement, A/B testing, rollback mechanisms          |
| 5x       | KaizenAgent premium    | 5×                 | Full meta-optimization loop, cross-subagent orchestration      |

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### 3. Pricing Formula

$$
\text{KaizenAgentic Price per Token} = C \times M
$$

Where:

* **C** = cost per token of the underlying LLM
* **M** = capability multiplier (2x–5x)

Example:

* GPT-4o base token = $0.01 / 1K tokens
* KaizenAgent Premium (5x) = $0.05 / 1K tokens

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### 4. Service Tiers

On top of token-based billing, KaizenAgentic can introduce **subscription layers** to cover operational support:

* **Free Tier** → 1x baseline agents, capped usage, no optimization feedback.
* **Pro Tier** → 2x–3x agents, includes monitoring dashboards.
* **Enterprise Tier** → 4x–5x agents, includes dedicated KaizenAgent meta-optimization + SLAs.

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### 5. Value Rationale

* **Fair:** Always anchored in base token price (transparent to clients).
* **Scalable:** Higher capability → higher multiplier → more value.
* **Predictable:** Clients can forecast spend by capability tier, independent of vendor-specific LLM pricing changes.
* **Flexible:** Basemodel transparent to avoid basemodel lockin supporting various providers (ChatGPT, Claude, Cursor, etc.).


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