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wiki/PricingModel.md

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+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.
+
+---
+
+### 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      |
+
+---
+
+### 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
+
+---
+
+### 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.
+
+---
+
+### 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.).
+
+
+xxx
+