Exploring AWS Costs Beyond the Service Level

Honeycomb leverages AWS Lambda for its query execution, benefiting from its scalability and pay-per-use model to maintain speed and affordability. However, understanding the costs associated with specific customers or application areas can be challenging. By utilizing a derived column approach, Honeycomb connects customer experiences to AWS Lambda costs, helping identify cost-intensive usage patterns and analyze the financial impact of software changes. AWS Lambda's pricing is based on architecture, RAM size, and invocation duration, with costs calculable through a linear formula. Honeycomb uses in-app instrumentation to capture detailed data, which includes Lambda architecture, configuration, and customer-specific identifiers, enabling more precise cost analysis. This method assists in validating customer usage expectations, setting up alerts for unexpected expenses, and conducting experiments to optimize Lambda configurations, ultimately achieving significant cost savings. The derived column translates compute units into monetary values, facilitating discussions about optimization versus feature development and helping determine when further optimization is worthwhile. Honeycomb encourages experimentation with this method to gain deeper insights into resource costs and customer behaviors.