What causes Black Friday server crashes — and what you can do to prevent them
Black Friday is one of the most demanding days of the year for online systems. Some say it’s a true stress test for any IT infrastructure.
As millions of shoppers flood websites and AIs scrape the best deals, maintaining server performance becomes not just a technical challenge, but a critical business priority.
A momentary slowdown or unexpected crash can cost thousands in lost revenue, damage customer trust, and even impact long-term brand reputation. This year, one of North America’s largest retailers, Best Buy, suffered a website outage in the midst of Black Friday shopping season last year.
In this guide, we’ll share expert strategies to help your business maintain peak server performance for Black Friday
What is server performance and why it matters more on Black Friday
Server performance refers to how effectively a server can handle and respond to requests. This includes the speed of processing workloads, the ability to manage multiple concurrent users, and the stability of system processes under varying levels of demand.
High-performing servers provide:
- Fast response times, ensuring users experience minimal delay when loading web pages or accessing applications.
- Strong reliability and uptime, reducing the risk of outages that interrupt service delivery.
- Scalable capacity, enabling organisations to grow without system slowdowns or errors.
The impact on user experience is drastic if you’re servers are slow and outdated. Older servers or ones that can’t support a business’s traffic, can cause page timeouts, sluggish applications, or interrupted online transactions.
This can lead to customer frustration, decreased engagement, and lost revenue. Internally, server issues may slow productivity tools, cause delays in data processing, and hinder operational efficiency.
Common causes of poor server performance on Black Friday
Server performance issues rarely appear without warning.
They typically stem from specific underlying causes. The most common include:
1. Insufficient resources
Servers require sufficient CPU, RAM, storage capacity, and network bandwidth. If any one of these becomes bottlenecked, such as a high CPU load or insufficient memory — performance will drop and server’s response time will increase.
Unplanned network traffic spikes caused by heightened traffic during Black Friday sales and AI bots and crawlers adding extra server pressure really highlight resource limitations.
2. Application-level inefficiencies
Even if hardware is up to the job, inefficient software can worsen your server’s performance.
Examples include:
- Unoptimised SQL queries
- Memory leaks
- Excessive logging
- Inefficient code loops
These inefficiencies cause disproportionate resource usage and slow the system over time.
3. Network congestion
High demand on network resources can limit data throughput and increase latency. This issue is especially common in distributed systems or environments that rely heavily on external APIs or remote storage.
4. Outdated hardware
Older servers may not keep up with modern workloads, even when well-maintained. Legacy hardware often lacks the processing power, storage speeds, or efficiency required for newer technologies, meaning websites can be even more susceptible to traffic spikes.
5. Misconfiguration issues
Incorrectly configured operating systems, network settings, or database parameters can cause network issues or prevent hardware from performing as expected. These issues may not present obvious symptoms at first, but over time they create noticeable performance strain.
Want to avoid these issues?
Reach out to our hosting and maintenance experts and never worry again.
Key server performance metrics to monitor
Monitoring your servers is paramount for performance management. The following metrics provide insight into how well your server is functioning:
CPU usage
Indicates the proportion of processing power being used. Sustained high usage suggests that workloads need optimisation or additional processing capacity.
RAM allocation
Measures memory utilisation. When free memory runs low, servers may begin swapping memory to disk, drastically reducing performance. Data read from memory is much faster than if that same data was read from disk.
Disk I/O
Represents how fast the server can read and write data. Slow I/O is a common bottleneck on servers running databases or large file operations — switching to NVMe SSD storage often provides immediate gains.
Network throughput
Reflects the volume of data being transmitted. Low throughput or high error rates can indicate congestion, misconfigured networking, or failing hardware.
Latency and response times
Measure how quickly the server responds to requests. High latency often directly impacts end-user experience and is critical to monitor in real time.
Optimising server resource usage
Optimisation improves efficiency and maintains performance without immediately increasing hardware cost.
Use resources smartly
If your system is running lots of AI tasks, it’s easy for one big job to slow everything else down.
Using tools like Docker or Kubernetes lets you set limits, so no single task eats all your memory or CPU power. When you can, run AI models on GPUs or move them to dedicated inference services — they’re designed for that type of work and can run much faster. It’s common practice to separate worker tasks from your production environment.
Make use of caching
Caching means saving the results of work you’ve already done, so you don’t repeat it unnecessarily. This is especially helpful for things like:
- API responses that are requested often
- AI model outputs that don’t change
- Embedding or similarity search results
Caching keeps your site feeling fast and reduces pressure on your servers and is one of the easiest ways to gain huge performance gains.
Balance the load
Instead of letting one machine handle all the traffic, spread requests across multiple servers. For AI-heavy applications, it’s often best to send inference requests to machines with GPUs, while letting regular backend tasks run on standard servers.
This keeps everything running smoothly. For web traffic, load balancing can help distribute the requests across multiple servers to help avoid one server getting overloaded.
Cut out unnecessary processes
Sometimes the system is being slowed down by things you don’t even need.
Doing regular cleanups can help identify:
- Background tasks that aren’t in use anymore
- Data pipelines that are running constantly without real value
- Logging or monitoring tools that are collecting far more than necessary, such as MySQL slow query logging
Turning these down or turning them off frees up resources for the work that matters.
Looking to optimise your server performance?
Get in touch with us for tailored solutions.
Maintenance best practices for long-term performance
To make sure that your servers are running at their best, and able to handle the annual Black Friday influx, there are few tips you can bear in mind that can help take the pressure off your systems.
Regular updates and patching
Keep OS, frameworks, libraries, database engines, and AI libraries updated to ensure you’re using the most efficient and secure versions.
Data cleanup and archiving
AI workloads generate large logs, checkpoints, embeddings, and analytics outputs. Implement:
- Automatic log rotation
- Tiered storage for cold data
- Policies to remove unused models and datasets
This keeps storage fast and responsive.
Scheduled stress testing
Simulate peak demand (including high concurrency AI-driven traffic) to verify:
- Load balancer readiness
- Auto-scaling response timing
- Cache effectiveness
This prevents performance surprises in production.
Backup and disaster recovery routines
Ensure backups include:
- Application data
- Configuration files
- AI model weights and embeddings
If an outage occurs, your replacement servers must support the same performance profile, including GPU if required.
When to upgrade or replace server infrastructure
Sometimes, no amount of infrastructure management can help you; your servers simply might not be able to keep pace with the demand.
To know for sure, you might want to keep an eye out for some of the signs that include:
- High CPU/RAM usage continues despite efforts to optimise your existing infrastructure.
- Your system suddenly feels slow when it’s processing AI requests or handling lots of database queries at once.
- If the server is saving or organising a lot of data quickly, the storage can struggle to keep up, which slows everything else down.
All of these factors may suggest the hardware simply cannot meet workload demand.
Cost vs. efficiency considerations
In some cases, holding on to older setups or trying to stretch existing hardware ends up costing more in the long run. For example, it’s important to consider that:
- Using GPUs instead of CPUs for AI tasks can actually lower costs, because they handle inference much more efficiently.
- Letting cloud servers scale automatically during busy periods can be cheaper than paying to keep large on-prem servers running all the time “just in case”.
- Specialised vector databases are often faster and more efficient for AI search and recommendation features than trying to force a traditional database to do the same job.
The key is to look at performance per pound, not just the price tag. A setup that costs a bit more upfront might deliver the same work for far less ongoing time, money, and hassle.
Strengthen your website with Kraam
With increased traffic heading to retail and ecommerce sites on Black Friday, you need to make sure your security, and servers are ready to handle the surge.
Kraam’s comprehensive website development services can help make sure that things run smoothly during the busy period.
Are you unsure that your IT infrastructure can handle the load? Kraam’s specialist hosting and maintain services can help you identify areas that need strengthening and work with you to bolster your website’s health.
Contact us today and speak to a specialist to find out how you can get started.