Large Language Models (LLMs) are revolutionizing the development of generative AI applications by utilizing a retrieval augmented generation (RAG) approach, which enhances output by integrating external context into the LLM's context window. Vectorstores, particularly those utilizing semantic similarity search, have become crucial for storing and retrieving relevant information in RAG applications, yet challenges remain in transitioning from prototypes to production. Pinecone Serverless has emerged as a popular solution to address these challenges, offering scalable, cost-effective vectorstore management by eliminating fixed monthly fees and enabling usage-based pricing. Additionally, LangServe facilitates the rapid deployment of RAG applications as web services, while LangSmith enhances observability for these applications. Together, these tools help bridge the gap between prototyping and production, enabling seamless integration and monitoring of RAG applications with scalable infrastructure.