business resources
How Businesses Can Prepare Applications for High-Traffic Events
09 Sept 2026
For many online businesses, a small number of high-traffic days can have an outsized impact on annual sales. Black Friday, product launches, ticket releases and major marketing campaigns have something in common: the date may be known in advance, but the level of demand is much harder to predict.
Preparing for that surge takes more than adding servers a week before the event. Traffic distribution, failover and application security all need to keep working when demand is at its highest, without forcing engineering teams to make critical changes under pressure.
For IT and e-commerce teams, the work starts well before the traffic arrives.
Peak Traffic Exposes More Than Capacity Problems
Systems that perform perfectly well under normal conditions can behave very differently when thousands of users arrive within a short period.
Several problems tend to become more visible during these events:
- Sudden increases in demand: a sale opening, ticket release or major campaign can send large numbers of users to an application within a very short window.
- Less room for failure: an outage on an ordinary day is disruptive; during a four-hour flash sale, the commercial impact can be immediate.
- Legitimate and malicious traffic arrive together: attackers and automated traffic do not disappear during busy periods. They compete for many of the same resources as real customers.
- More teams are involved: marketing, IT and customer service need to know when campaigns are going live and what to expect.
Adobe Analytics reported that U.S. online spending reached $11.8 billion on Black Friday 2025 and $14.25 billion on Cyber Monday. For retailers operating at that scale, even a short interruption during peak hours can affect a significant volume of transactions.
Capacity is not the only concern. A saturated database, an expired SSL/TLS certificate or a single load balancer node can become a weak point for the entire service. Systems also need to distinguish legitimate users from automated or malicious traffic, while failover mechanisms need to react without waiting for someone to spot the problem manually.
This is also why campaign planning cannot remain solely with marketing. If IT learns about a major promotion only days before launch, there may be little time left to test the infrastructure properly.
Prepare the Infrastructure Before Demand Arrives
Test Beyond the Traffic You Expect
Testing only the forecast load tells you whether the system can handle the forecast. It does not tell you how much headroom remains when demand exceeds it.
Load tests should therefore push beyond expected traffic until the first meaningful constraints appear.
- Test above the expected peak rather than stopping at the average forecast.
- Test the full transaction path — browsing, cart, checkout and payment — instead of concentrating only on the homepage.
- Run tests close enough to the event to include recent infrastructure changes and third-party dependencies.
Testing an isolated service can leave important bottlenecks undiscovered. A storefront may load quickly while the database, payment flow or another dependency struggles once many customers attempt to complete transactions at the same time.
Distribute Traffic Based on What Is Actually Happening
More servers do not automatically mean more usable capacity. Traffic still has to reach the right backend at the right time.
An Application Delivery Controller can combine load balancing with health checks, persistence and Layer 7 traffic rules, giving teams more control over how available capacity is used.
- Choose load balancing methods that match the traffic pattern, such as round robin for relatively uniform requests or least-connections and least-response-time when workloads vary.
- Use weighted distribution when some servers or locations have more capacity than others.
- Review the configuration after major events rather than assuming last year's traffic pattern will repeat itself.
Simply adding servers while leaving ineffective distribution rules in place can move the bottleneck rather than remove it.
Automate Health Checks and Failover
During a traffic spike, waiting for someone to notice that a component has failed wastes valuable time.
Health checks and failover should be configured to detect problems and redirect traffic automatically.
- Monitor the service or endpoint itself, not only whether the server is powered on.
- Configure automatic failover and, where required, session persistence so active user sessions are handled correctly.
- Test the failover process before the event rather than assuming the backup will work when needed.
Failover is only useful if the team knows how the system will actually behave when a backend becomes unavailable.
Protect Capacity When Everyone Wants a Piece of It
Keep Malicious Traffic From Competing With Customers
High demand does not come exclusively from customers.
Kinsta's analysis of Black Friday and Cyber Monday 2024 found that blocked requests increased by 42.8% on Black Friday and 44% on Cyber Monday. Its bot-detection challenges increased by 29.4% and 88% respectively.
That matters during a peak event because malicious and automated requests can consume resources that should be serving legitimate users.
- Apply per-source rate limits at the appropriate service or route so a single source cannot consume excessive capacity.
- Review WAF rules before the event and make sure the policies protecting critical applications are current.
- Watch for unusual request patterns while the event is running rather than relying only on post-event analysis.
Performance and security cannot be treated independently during a traffic surge. Both affect how much usable capacity remains for genuine customers.
A managed platform such as SkudoCloud can bring traffic delivery and application security controls into the same service, which can simplify how these policies are managed during high-demand periods.
Remove the Failures That Have Nothing to Do With Traffic
Not every peak-event outage is caused by excessive demand.
An expired certificate or a poorly timed configuration change can make weeks of capacity planning irrelevant.
- Check SSL/TLS certificate expiration dates at least two weeks before the event.
- Automate certificate renewal where possible rather than relying entirely on calendar reminders.
- Avoid non-essential configuration changes during the 48 hours before a major event.
The aim is straightforward: once the event begins, the team should not be solving routine problems that could have been removed beforehand.
During the Event, Visibility Matters as Much as Capacity
Give the Team One Live View of What Is Happening
When a high-traffic event is underway, teams need to detect degradation before customer complaints become the monitoring system.
- Use a centralized dashboard for traffic, error rates and response times.
- Configure alerts at early warning thresholds rather than waiting for a complete outage.
- Decide in advance who monitors the service and who has the authority to make changes.
Clear ownership matters particularly when several teams are involved. An alert is of little use if nobody knows who is expected to respond.
Have a Rollback Plan Before the Event Starts
Not every change made during an incident improves the situation.
Teams should know in advance how to reverse a change quickly if it introduces a new problem.
- Document rollback procedures for changes made close to the event.
- Review what happened within 48 hours, while the sequence of events is still clear.
- Record what failed, what worked and what needs to change before the next peak.
That review turns each high-traffic event into useful input for the next one, rather than forcing the team to start from scratch every season.
Seasonal traffic spikes are unusual in one important respect: businesses usually know when they are coming. That gives IT teams the chance to test beyond normal demand, identify bottlenecks, verify failover, review application security and improve visibility before the pressure arrives.
The goal is not to predict exactly how many users will show up. It is to know how the application behaves when demand exceeds expectations and to make sure the failures that can be prevented have already been dealt with.
