Running Agentic AI in production costs more than running a simple chatbot because you are paying for multiple interactions per task, not just a single response. Costs typically come from three areas: model usage (reasoning and planning steps), tool execution (API calls, services), and memory/storage (logs, embeddings, retrieval). The total cost depends on how complex tasks are and how many steps the agent takes to complete them.
Model usage is often the most visible cost. An agent may make several reasoning calls per task: planning, re-planning, summarizing results, and validating outcomes. Tool calls can add indirect costs, such as database queries or external service usage. Memory systems also contribute: storing embeddings and running similarity search in a vector database such as Milvus or Zilliz Cloud has both storage and query costs. These are usually predictable and scale with data volume and query frequency.
The key to controlling cost is constraint-driven design. Limit the number of steps an agent can take, cache intermediate results, and avoid re-embedding unchanged data. Use retrieval selectively so the agent pulls only the context it needs. Measure cost per task rather than cost per request, and tune the system based on real usage patterns. Well-designed Agentic AI systems are not inherently expensive, but poorly bounded ones can quickly become inefficient. Cost control is an engineering responsibility, not something to fix after deployment.