Indeed, awesome. But I prefer KIE because its much cheaper.

@Razvan Sava Yes, it would be great if they could build those integrations. I built mine myself and they work perfectly fine.

🔁 1. Cost vs. Performance Optimization Choose cheaper/faster models for simple tasks (e.g., summarizing or translating short text), and powerful models for complex tasks (e.g., code generation or detailed answers). ```plaintext If `taskType` = "summarize" → Model 1 (e.g., Gemini 1.5 Flash) If `taskType` = "generate_code" OR "longform_answer" → Model 2 (e.g., GPT-4o) ``` 🧠 2. Model Selection Based on Message Complexity In an AI chatbot, decide which LLM to use based on the length or detected complexity of the input. ```plaintext If `message.length` < 150 → Model 1 (Faster/cheaper model like Gemini) If `message.length` >= 150 → Model 2 (Powerful model like GPT-4) ``` 🛠 3. Hybrid Agent Workflow with Tool Routing Use the Model Selector to control the behavior of your AI Agent — such as when to: * Query a vector store (like Qdrant), * Use embeddings, * Respond directly. ```plaintext If `userIntent` = "search" → Route to Embedding + Qdrant + LLM If `userIntent` = "direct_response" → Route to GPT model only ``` 👌 4. Language- or Region-Based Model Switching Choose the LLM based on the detected input language or user region. ```plaintext If `language` = "EN" → Model 1 (OpenAI Chat Model) If `language` = "RO" or "FR" → Model 2 (Gemini or another multilingual model) ``` 💬 5. Multi-Tenant/Client Customization (SaaS) For white-label or B2B AI services, assign specific models per customer. ```plaintext If `clientId` = "acme_corp" → Model 1 (GPT-4o) If `clientId` = "startx" → Model 2 (OpenRouter/Mistral) ``` 🧪 Confidence-Based Routing (AI Pre-Classifier) Run an LLM only when your classifier is confident enough, else fallback to simpler logic or a cheaper model. ```plaintext If `confidenceScore` < 0.5 → Model 1 (fallback model) If `confidenceScore` >= 0.5 → Model 2 (high-performance model) ```

Thx!