Holiday traffic creates some of the most exciting moments of the year for digital businesses with more transactions and maximum opportunities.
Just last year many retailers saw seasonal traffic jump by over 250% during peak hours, and even a 1-second slowdown was enough to reduce conversions by nearly 10%.
Interestingly, the brands that performed best weren’t the ones with the biggest infrastructure, but were the ones with the smartest optimization.
With the holidays just around the corner, are your cloud optimization strategies ready yet, or are you leaving peak season performance to chance?
This year, cloud strategy isn’t just about scaling up; it’s about scaling wisely. Here are 7 cloud optimization strategies to help you stay ahead of the curve before holiday demand hits.
1. Start with a Holiday-Focused Historical Load Analysis
Before optimizing anything, you need to understand how your systems behaved during past peak seasons. A 6–12 month historical load analysis gives you the clearest view of what “normal” looks like against your holiday surge behavior.
Start by reviewing data across key performance metrics:
- Traffic patterns: Identify days and hours where demand consistently spiked.
- CPU and memory utilization: Understand how quickly resources are saturated during peak moments.
- API call volume: Spot bottlenecks or endpoints that historically struggled under load.
- Sales-event trends: Compare major periods like Thanksgiving, Black Friday, and year-end holidays.
This initial step will help you build a reliable holiday baseline, so you can enable smarter scaling decisions and prevent surprise cost spikes during peak season.
2. Right-Size Your Infrastructure Before the Surge Hits
Right-sizing is one of the highest-impact steps you can take before holiday demand kicks in. Across industries, up to 35% of cloud resources run over-provisioned throughout the year. During peak season, that waste compounds as autoscaling builds on top of whatever you already have allocated.
Right-sizing is about creating a clean baseline so that every unit of holiday scaling is justified, efficient, and aligned with the real workload. You can start by reviewing:
- Underutilized instances (often running at <30% CPU or memory): These are instances running far below their intended capacity. Keeping them active inflates your baseline, causing autoscaling to add even more unnecessary resources during peak hours.
- Over-provisioned services such as oversized API nodes or background workers: They lead to inflated steady-state costs and misaligned Savings Plan/RI coverage during surges.
- Idle dev/test environments that don’t need holiday-level capacity: These environments rarely contribute to peak-season traffic, but they silently consume compute. Turning them off or downgrading them during holiday periods can free up significant capacity and cost.
- Old instance families that cost more and perform worse: Legacy instance types (e.g., older M- or C-series generations) underperform during high-traffic moments and cost more to run. Modern families deliver better performance per dollar and scale more predictably.
3. Align Autoscaling Rules With Actual Demand Patterns
Most autoscaling policies are tuned once and forgotten. But during the holiday season, traffic spikes earlier, lasts longer, and recovers more slowly. If your scaling rules don’t reflect these patterns, your system may either scale reactively or excessively.
Start with a quick audit of your existing rules:
- Review threshold sensitivity (CPU, memory, request rate): During peak periods, CPU breaches can occur within seconds. If thresholds are too high or cooldown periods too long, autoscaling lags behind real demand, causing latency or sudden bursts into On-Demand instances.
- Check scaling speed and step sizes: If your system adds one instance at a time during heavy load, it may always be “catching up.” Prepare for high concurrency moments by increasing step sizes or by implementing target-tracking policies
- Identify services that need predictive or pre-warming logic: Critical paths like checkout, search, or payment APIs often require capacity before the spike arrives. Adding scheduled scaling or predictive warm-ups can reduce cold starts and bottlenecks.
- Validate instance family alignment: Unexpected autoscaling behavior, like scaling into uncommitted families can reduce Savings Plan/RI coverage by 20–40%. Ensure your ASGs or Kubernetes clusters restrict scaling to cost-efficient, committed families.
4. Strengthen Database, Caching, and API Performance Before Traffic Spikes
Databases, caching layers, and internal APIs are often the first components to feel the stress of holiday surges. Just last year, retailers reported how 40–60% of peak-season latency came from bottlenecks in these layers.
Optimizing backend performance before the rush reduces the load on autoscaling systems. It also cuts compute waste, and creates a smoother user experience for peak traffic moments.
Here are a few targeted optimizations:
- Audit database query performance and slow paths: Even small inefficiencies, like unindexed fields or unoptimized joins can cause cascading slowdowns during peak hours.
- Increase caching coverage for predictable holiday traffic: Holiday traffic often includes repeatable patterns like popular products, categories, search terms, etc. Improving cache hit rates reduces database load and keeps APIs responsive. Many teams see 20–40% lower backend latency simply by tuning cache TTLs or adding layer-2 caching.
- Review API rate limits and concurrency capacity: API gateways and microservices often hit concurrency ceilings before compute limits. Increasing throughput capacity or optimizing payload sizes helps avoid bottlenecks under sudden bursts.
- Pre-warm critical backend services: Search services, recommendation engines, payment processors, and inventory checkers often struggle with cold starts. Pre-warming these ahead of major campaigns ensures consistent response times even during sudden 2–3× traffic surges.
5. Use Spot and Mixed Instance Policies for Non-Critical Workloads
Not every workload needs the reliability (or the price tag) of On-Demand instances. You can rather use spot instances and mixed instance groups to run scalable workloads at a 60–90% lower cost, without impacting customer-facing systems.
Here’s how you can use them effectively:
- Move non-critical or flexible workloads to Spot instances: Batch jobs, catalog updates, data pipelines, or ML retraining tasks don’t require guaranteed uptime. Running them on Spot can reduce compute costs by 60–90%, freeing premium capacity for customer-facing traffic.
- Use mixed-instance Auto Scaling Groups for better availability: By allowing multiple instance families and sizes, your autoscaling group can pull from whichever capacity pool is most available. It also lowers the risk of Spot interruptions during peak hours.
- Implement safe interruption handling: A simple checkpointing mechanism or queue-based architecture ensures workloads resume smoothly if a Spot instance is reclaimed.
- Set budgets and priorities for different workload tiers: Keep checkout, search, login, and payments on On-Demand or committed capacity. Shift auxiliary workloads, like sync jobs, feeds, indexing, and analytics to Spot to reduce compute waste without compromising experience.
6. Refresh Your Commitments Before the Peak Season
Outdated commitments (Savings Plans or Reserved Instances) during holiday traffic often fail to cover the burst capacity your workloads will need.
A quick pre-season commitment audit can prevent that.
- Review your existing Savings Plans and RI coverage: Check whether your current commitments match the instance families and sizes your autoscaling groups actually use today. Even a small drift, like moving from C5 to C7g can reduce coverage significantly.
- Identify coverage gaps for expected holiday workloads: If your holiday baseline forecasts a 2–3× spike in compute, your commitments should reflect that. Short-term 1-year Savings Plans or flexible Compute Savings Plans can help cover seasonal bursts.
- Avoid accidental scaling into uncommitted families: During high load, ASGs or Kubernetes clusters may choose instance types that fall outside your commitment portfolio. Restricting or prioritizing the right families improves coverage and reduces On-Demand spillover.
- Rebalance outdated or underutilized commitments: If certain RIs or Savings Plans consistently go unused, rebalancing or exchanging them can improve efficiency before the surge hits.
7. Monitor Cost and Performance in Real Time During Peak Windows
Even the best optimization work can lose impact if teams aren’t watching the right signals during the busiest hours of the season. Instead of weekly dashboards, go for live visibility across both cost and performance metrics.
- Track autoscaling behavior as it happens: Unexpected scaling events often point to backend stress, inefficient queries, or capacity misalignment. Early detection can help you prevent these slowdowns.
- Monitor discount coverage and On-Demand spillover: If a part of your workload suddenly starts running on uncommitted instances, your costs can spike quickly. Setting real-time alerts will help you respond before the spend snowballs.
- Watch out for API latency, error rates, and queue depth: During peak hours, even a small increase in backend latency can translate to cart drop-offs or failed transactions. Ensure your responses are quick to keep customer experiences smooth.
Observe cost burn rate by the hour: Holiday surges can shift resource consumption patterns dramatically. Knowing your spend trajectory in real time will keep budgets under control and avoid next-day surprises.
