I'm a recent college grad working part-time at a small startup focused on marketing but venturing into SaaS. I'm the only infrastructure person supporting two junior developers on an HR management system. We currently use Azure and have only been testing the application internally.
Exciting news is we've got a potential enterprise client with around 1000 employees, and they need a reliable monthly cost estimate for the app. When I first got asked, I gave a rough estimate for about 30 users, and after some research, I realized it varies a lot based on demand, cloud pricing, and right-sizing. To play it safe, I suggested costs for a basic setup. However, now I'm tasked with scaling that estimate up, while ensuring the app doesn't go idle during off-peak times.
I've only got usage data from a month of internal testing with 30 users. I've projected costs at about $9.5 per user, but my manager thinks it's too high and is looking for alternatives using AI advice, which has me worried about accuracy. I'm looking for a solid, methodical approach to gather data and estimate these costs more effectively before making a final recommendation. Any insight on best practices for capacity planning and cost estimation would be greatly appreciated!
4 Answers
You might want to try using the Azure Pricing Calculator. It’s a great tool for getting a clearer picture of your expected monthly expenses based on the services you plan to use. Make sure to break down the services and their respective costs to avoid any surprises later!
It's essential to know what specific Azure services you're using and their current utilization before estimating costs. Check your service tiers, sizes, and user numbers. Sometimes just scaling vertically can handle additional users without needing to go all out with infrastructure. Have you considered doing some load testing on the system? It can really help you gauge required resources and potential costs for those 1000 users.
Your manager's push for AI-generated suggestions can be tricky. Remember, cost efficiency often comes with understanding your actual usage patterns. Load tests will give you insight into how many users can comfortably run on your current setup. Keep in mind that you can scale up your resources later based on testing results. It’s better to start with room to grow than to face performance issues down the line!
For sure! Clear service level agreements on performance and uptime are essential, especially if you’re handling sensitive info. Maybe see if you can get input directly from your developers on what they think the application needs.
If I were in your shoes, I would avoid using B-series VMs for a significant client. Over-provisioning is often wiser. Consider starting with more robust VMs to ensure stability and minimize the risk of downtimes when scaling up. Diagnostics and logging are key for future optimizations, but keep track of your costs with tools like Application Insights to avoid surprises!
Totally agree! Starting small and scaling smartly is the way to go. You'll want to ensure you pick the right database tier too, especially for something as sensitive as HR data.