As organizations increasingly adopt large language models (LLMs) for diverse applications like personalized recommendations and fraud detection, the need for specialized observability practices has become critical to ensure their efficient functioning and integration. Observability for LLMs presents unique challenges due to the complexity of real-time monitoring, interpreting outputs, and addressing ethical concerns, unlike traditional machine learning models. Emerging solutions focus on tracking model performance metrics, versioning, and detecting concept drift, with tools like Weights & Biases and Arize AI providing capabilities for attention pattern analysis and drift detection. Application Performance Monitoring (APM) for LLMs is essential to monitor the performance of applications utilizing these models, ensuring key metrics such as response times and error rates are managed effectively. Coralogix stands out for its cost-efficient approach, integrating in-stream data analysis and flexible data storage solutions, allowing organizations to build custom dashboards for AI monitoring. As the symbiotic relationship between AI and observability evolves, these tailored solutions will continue to advance, pushing the boundaries of what is possible in the field.