Loki, a logging system developed by Grafana Labs, emphasizes the use of minimal labels to achieve better performance and lower operating costs, differing from traditional logging systems that rely heavily on indexing. This approach leverages a small index to enhance efficiency and employs horizontal scaling and query time brute force to manage large volumes of data, making it easier to run than fully indexed solutions. While indexing fewer labels might seem counterintuitive, it helps keep the system cost-effective and less complex. Although Loki cannot aggregate on non-label content yet, upcoming updates to its query language, LogQL, aim to address this limitation. The system can manage vast data volumes effectively by parallelizing searches, allowing users to maintain a small index even while handling terabytes of logs. However, challenges remain in environments like Lambda functions, where log ordering can pose issues, and efforts are ongoing to improve this aspect.