The talk explores streamlined deployment of scalable AI agents using open-source tools. Attendees will learn how Dify's LLMOps platform simplifies workflow orchestration and monitoring for LLMs, paired with vLLM's high-performance inference engine for cost-efficient, low-latency serving. The session demonstrates integrating these tools to optimize resource utilization, accelerate model iteration, and manage complex AI agent pipelines—from fine-tuning to production. Real-world use cases (e.g., chatbots, automation systems) will highlight best practices for balancing speed, accuracy, and scalability. Developers and ML engineers will gain actionable insights into overcoming GPU constraints, reducing inference costs, and leveraging Kubernetes-native workflows for enterprise-grade LLM operations. Ideal for teams adopting open-source AI/ML stacks, this talk bridges the gap between experimental models and robust, maintainable deployments.