Preparing for large-scale live streaming events on AWS — Part 1

This post is part 1 in a two-part blog post series. Part 1 covers strategy, architecture evaluation, and capacity planning. Part 2 covers content delivery, nonfunctional testing, chaos engineering, and Amazon Web Services (AWS) support models.

When a global streaming provider prepares to deliver a large-scale live streaming event, its platform will face a sudden, massive surge in demand. Peak concurrency can reach many times the daily baseline within minutes of the event going live, then sustain across a multihour broadcast window before dropping away as quickly. This is among the most demanding infrastructure challenges in cloud-based media delivery, and it plays out live, in public, with no second take.

For the largest global events, over-the-top (OTT) platforms on cloud infrastructure handle millions of concurrent connections that arrive within a compressed window, sustain high throughput for hours, then drop rapidly.

This two-part series addresses the areas OTT platform owners should focus on to verify infrastructure readiness for large-scale events (LSEs). Part 1 covers strategy, architecture evaluation, and capacity planning. Part 2 covers content delivery, nonfunctional testing, chaos engineering, and Amazon Web Services (AWS) support models.

Understanding large-scale event traffic characteristics

Four characteristics make large-scale event traffic different from normal streaming workloads:

• Concurrency magnitude –The eventcan push concurrency to multiples of the daily baseline, a spike that reactive auto scaling alone can’t absorb without viewer impact.
• Ramp and spike-within-spike –Viewers tune in over 30–90 minutes before the event. Identify the moments specific to your event that will drive secondary surges above the sustained baseline, for example, kick-off, half-time, and the final whistle for a football match, or a headline act for a concert.
• Sign-up and authentication surges –The event drives new subscriber acquisition, so identity, authentication, and payment face their own spike, independent of streaming traffic.
• Predictable scheduling –Most large-scale events are scheduled weeks ahead. This predictability is your advantage. It gives you time to prepare, test, and validate.

Architectural evaluation

Understand the end-to-end architecture of your platform, including not only individual components but how they interact under load and during failure. Bringing platform engineering, operations, and security together to document expected behavior and runbooks uncovers issues before the event. In our experience, this is the part teams—even mature ones—most consistently underestimate.

Well-Architected Framework Review

The AWS Well-Architected Framework Review (WAFR) evaluates architectures across the six pillars of the AWS Well-Architected Framework. For event preparation, the reliability and performance efficiency pillars deserve particular focus. Run focused review sessions on the platform functions that face the steepest and least predictable demand during an event:

• Compute and auto scaling –Confirm that your compute scaling mechanism—for example, Karpenter for Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon EC2 Auto Scaling groups—can launch capacity fast enough to stay ahead of the ramp. Identify service quotas that could cap how fast you scale.
• Identity and session management –Validate that your identity platform can sustain login rates during the pre-event surge. Confirm that session tokens are cached to avoid a database bottleneck.
• Networking and connectivity –Determine whether NAT gateway, transit gateway, or endpoint capacity will throttle east-west traffic at peak.
• State and data stores –Validate that your databases and caches can absorb the read and write pattern at peak without hot-key or throttling issues.

The output of these WAFR sessions is a prioritized risk report. The cross-functional discussion the review sparks is equally valuable, such as the networking team discovering that identity rate limits will reject legitimate traffic during signup surges.

Key architecture patterns for event readiness

A few architecture patterns consistently separate event-ready platforms from fragile ones. Focus on these when preparing for high-demand events:

• Multi-Region active-active –Deploy your critical viewer path (authentication, manifest generation, ad decisioning) in at least two AWS Regions for geographic proximity and fault isolation. DNS-based routing can shift traffic to a healthy Region within seconds.
• Service isolation –Verify that event-critical services don’t share compute, database, or networking resources with noncritical workloads. A batch analytics job or media transcoding task should never affect viewer playback.
• Graceful degradation hierarchy –Define explicit priorities: personalization degrades before playback, analytics before ad serving. Let circuit breakers enforce them under load.
• Stateless compute, persistent state –Keep playback services stateless and horizontally scalable; hold session, preference, and entitlement data in purpose-built stores (such as Amazon DynamoDB, Amazon ElastiCache, and Amazon Keyspaces) that scale independently.

Capacity planning

Auto scaling reacts after thresholds are breached, so there is always a lag between demand arriving and capacity appearing. For large-scale events, minimize this gap through pre-provisioned capacity and platform readiness.

Load balancer capacity planning

You can reserve minimum capacity ahead of time using Load Balancer Capacity Unit (LCU) Reservation, guaranteeing headroom during high-demand periods while normal auto scaling continues above the reservation. Size the reservation on your load-tested peak, plus a margin.

Compute capacity planning

Planning compute capacity for a large event means stacking several defenses so that a gap in one is covered by another:

• Instance diversification is your primary defense –Configure your compute provisioner to accept multiple instance families and sizes across multiple Availability Zones. Options include Karpenter for Amazon EKS, mixed-instance policies for Amazon EC2 Auto Scaling groups, or capacity provider strategies for Amazon Elastic Container Service (Amazon ECS). This is the most effective way to improve your chances of getting the capacity you need and should be your first lever.
• On-Demand Capacity Reservations (ODCRs) as the fallback –Diversification improves your odds but never guarantees capacity for On-Demand launches. Where you need assurance, reserve the capacity with Amazon EC2 ODCRs, using future-dated reservations (up to 120 days ahead) for planned events.
• Amazon EKS control plane pre-provisioning –The Kubernetes control plane scales reactively in standard mode. Mass pod scheduling and burst API calls during the event ramp can exceed its allocation. Switch to provisioned mode ahead of the event to pre-allocate API server throughput, controller capacity, and etcd sizing at a fixed scaling tier.
• Warm capacity and fast starts –Hold spare capacity using low-priority pause pods that are evicted when real workloads schedule, keep container images small, use Seekable OCI (SOCI) for lazy loading, and pre-pull critical images through DaemonSets.
• Provisioned concurrency for AWS Lambda –For latency-sensitive AWS Lambda functions (such as token validation, authorization, and session creation), use provisioned concurrency to remove cold starts during the event window.

Database and state store capacity

For every event-critical service, use the AWS Health Dashboard to confirm no scheduled maintenance overlaps the event window, and defer anything that does:

• Amazon DynamoDB and Amazon Keyspaces –Pre-warm tables by setting provisioned capacity to anticipated peak before the event. This forces sufficient partitions and prevents throttling. Afterwards, you can switch to on-demand billing; the partitions remain allocated.
• Amazon RDS and Amazon Aurora –Scale Amazon Relational Database Service (Amazon RDS) read replicas to peak count beforehand. Also, consider Amazon Aurora serverless v2 where the profile is uncertain because it scales in seconds.
• Amazon ElastiCache and Amazon ElastiCache Serverless –Size provisioned clusters for peak connections and throughput. Validate shard distribution to avoid hot keys. For Amazon ElastiCache Serverless, pre-warm the cache at least 60 minutes before the traffic ramp so it can scale out ahead of demand.

Networking capacity planning

Each NAT gateway supports up to 55,000 simultaneous connections per destination and has a bandwidth ceiling; transit gateway attachments have a baseline per-attachment bandwidth. Where load testing shows you’ll approach either limit, request pre-warming through AWS Support ahead of the event.

Review and raise service quotas across your critical path weeks in advance; some require coordination between internal AWS teams and can’t be fulfilled overnight.

The capacity planning timeline

The following table summarizes the recommended preparation milestones leading up to a large-scale live event.

Business-driven demand shaping

Technology alone doesn’t solve the challenge of scaling to meet demand. Business decisions play an important role as well. When planning for demand, take the following into account:

• Pre-event programming –Broadcasting content 2–3 hours before the main event brings viewers on gradually, turning a sudden spike into a manageable ramp.
• Early subscriber campaigns –Promoting signup offers days ahead distributes authentication and payment load, so already-authenticated viewers don’t hit your identity platform during the pre-event rush.
• Progressive feature loading –Load personalization and secondary UI asynchronously after the core player initializes. Prioritize the critical path (video playback) during peak demand.

Conclusion

Preparing for a large-scale live event is a multimonth program, not a last-minute exercise. It spans architecture evaluation, capacity reservation, cross-team coordination, and business strategy. The providers that deliver flawless events treat infrastructure readiness as a planned discipline with milestones and owners, rather than relying on auto scaling alone. Read Part 2 for content delivery, nonfunctional testing, chaos engineering, and AWS Support models, and contact your AWS account team to plan your next event.

Further reading

• Pre-warming Amazon DynamoDB tables with warm throughput
• Reserving EC2 Capacity across Availability Zones by utilizing On-Demand Capacity Reservations (ODCRs)
• Optimizing your Kubernetes compute costs with Karpenter consolidation