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Capacity Planning: Why That Ticket Site Let You Down During Peak Demand

Author: Mike Galvin Posted In: Cloud, Custom Development
Apr 232025
A man in a red shirt sits in front of a laptop, holding a credit card to his forehead and a phone in his other hand, appearing frustrated as he struggles with capacity planning.

Every April, tickets go on sale for the summer season at my favorite local music venue. I always mark the date on my calendar and make sure I’m online the moment sales go live. I join the virtual queue, wait eagerly — and then comes the inevitable frustration.

This year, once I finally made it to the ticketing site, I had 20 minutes to complete my purchase. But the system was so overwhelmed that I couldn’t even reach the payment page in time. I was kicked out, had to start over, and lost my ideal seats in the process.

I don’t know exactly how their system is deployed, but if it's hosted in the cloud, there’s really no excuse for being unprepared for such a predictable traffic spike. With the right planning, any cloud-based application can handle both peak and off-peak demand, ensuring availability without unnecessary overspending.

Why Capacity Planning Matters

Capacity planning is all about understanding your current usage and forecasting what you’ll need in the future. For cloud-based systems, this includes:

  • CPU – Processing power

  • RAM – Short-term memory

  • Network bandwidth – How much data can be transmitted

  • Storage – Disk and database space

Because forecasts are never perfect, capacity planning is an iterative process: forecast, monitor, and adjust.

Learn from Historical Data

In the case of the ticketing platform, they likely have years of historical data showing:

  • Number of concurrent users

  • Request rates

  • Session durations

  • CPU and memory usage per request

  • Database reads and writes per second

For new apps without historical data, business projections can help guide your estimates. From there, you can simulate demand using load testing tools like JMeter, Locust, or Grafana k6 to observe how your system holds up.

The Cloud Makes Scaling Easier

Once you understand your expected load, cloud platforms offer a range of tools and services to help you meet demand:

  • Choose the right instance types for your app

  • Monitor usage with dashboards, logs, and alerts

  • Use OS-level tools to spot performance bottlenecks

When you identify resource constraints—like CPU maxing out or memory depletion—you have two options:

  • Scale vertically: Upgrade to a bigger instance

  • Scale horizontally: Add more instances to handle requests in parallel

The goal is simple: monitor, identify limitations, and scale—without breaking the bank.

Automate Your Response to Demand

One of the best parts of cloud infrastructure is the ability to automate:

  • Auto-scaling: Dynamically add or remove capacity based on real-time demand

  • Scheduled scaling: Increase resources in advance of known spikes

For our ticketing vendor, an ideal setup might look like this:

  • Maintain a baseline capacity for regular operations

  • Schedule additional resources for the first 24 hours after ticket sales go live

  • Enable auto-scaling as a safety net if demand exceeds expectations

Final Thoughts: Best Practices for Capacity Planning

Here are a few key takeaways to help you prepare for high demand without overcommitting resources:

  • Design an efficient architecture that scales smoothly

  • Use pricing calculators and planning tools from your cloud provider

  • Leverage AI tools (like ChatGPT!) to build capacity models or check your math

  • Consult with experts, or consider commercial capacity planning tools

  • Automate infrastructure using Infrastructure as Code (IaC)

In the end, a little planning can save your users from frustrating experiences—and save your business from overspending.

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