The web application at a client was hosted using a combination of services on Azure. The architecture was quite simple and used the following services. Front Door, Api Manager, App Service, SQL Database, Service Bus, Redis Cache, and Azure Functions. As the application matured, little did we think of all the hidden costs of the cloud at the start of the project.
Azure Front Door used for efficient load balancing, WAF, Content Delivery Network, and as a DNS. However, the global routing of requests through Microsoft's network incurred data transfer and routing costs. What started as a seamless solution for enhanced user experience turned into a realization that global accessibility came at a price. Also, the complexity of configuring backend pools, health probes, and routing rules can lead to unintended expenses if not optimized.
App Services had a modest cost to begin with on low-scale Premium servers. But as the application garnered a lot of hits, so did the number of users and, subsequently, the resources consumed. The need for auto-scaling to handle increased traffic and custom domains brought unforeseen expenses, turning the initially reasonable hosting costs into a growing concern. So, keep an eye on the server configuration and the frequency of scaling events.
Azure SQL Database brought both power and complexity. Scaling to meet performance demands led to increased DTU consumption and storage requirements. The once manageable monthly expenses now reflected the intricate dance between database size, transaction units, and backup storage. Not scaling down the backups also incurred costs, especially for databases with high transaction rates. Inefficient queries and suboptimal indexing can increase resource consumption, impacting DTU usage and costs.
Azure Service Bus, the messenger between the application's distributed components, began with reasonable costs for message ingress and egress. Yet, as the communication patterns grew, the charges for additional features like transactions and dead-lettering added expenses to the budget. Also, long message TTLs can lead to increased storage costs.
Azure Cache for Redis, used for in-memory data storage, initially provided high-performance benefits. However, as the application scaled, the usage to accommodate larger datasets, the costs associated with caching capacity, and data transfer began to rise, challenging the notion that performance came without a price. Eviction of data from the cache, may result in increased data transfer costs, especially if the cache is frequently repopulated from the data source. Also, fine-tuning cache expiration policies is crucial to avoid unnecessary storage costs for stale or rarely accessed data.
Lastly, the Azure Functions, with its pay-as-you-go model, was supposed to be the least cost of all services as it allowed to invoke functions as needed. But, the cumulative charges for execution, execution time, and additional resources reminded me that serverless, too, had its hidden cost. Including unnecessary dependencies in your function can inflate execution times and costs.
Demystifying the expenses after moving to Azure required a keen understanding of its pricing models and a strategic approach to balancing innovation with fiscal responsibility.