During a major football match, traffic to an online sportsbook can jump 10 to 50 times within minutes. Bets, cashouts, and live odds all hit the system at once. If the platform hesitates, users drop, slips fail, and the brand loses trust.

Teams that build on Kubernetes, whether in EKS or GKE, can absorb these spikes with the right mix of autoscaling, resilient design, and tight cost control. 

Operators that cover league games and live markets, including listings that compare sites like ufabet เว็บตรง need this discipline because demand is unpredictable and short. Below is a practical playbook that DevOps engineers can ship fast and keep clean.

Know Your Peak Numbers

Capacity planning begins with real numbers, not guesses. Map the baseline and peak for each key action: login, browse markets, place bet, confirm, and cash out. 

Watch read to write ratios for hot data such as live odds and user balances. Measure queue depth for risk engines and payments.

From these numbers, derive golden signals. Common targets include p95 request latency under 150 to 250 milliseconds for read paths, and under 500 milliseconds for bet placement. 

Set per service Service Level Objectives and record them. These SLOs will drive scaling rules and quick rollbacks when an update hurts latency.

Build A Simple, Modular Stack

A typical I gaming stack on Kubernetes uses:

• API gateway and global load balancing with regional failover
  
• Session services that work with WebSockets for live markets
  
• Redis for caching hot odds and session tokens
  
• Kafka or Pub/Sub for bet events and settlement streams
  
• Sharded Postgres or MySQL with read replicas for odds reads and balance checks
  
• Risk and pricing services fed by live feeds
  
• Payments and KYC microservices isolated by network policies
  

Keep pods small and single purpose. Set resource requests and limits for each container. Use priority classes so payment and bet placement never starve when casual browsing explodes.

Use IaC From Day One

Write the platform once as code. Terraform is the common base and pairs well with Helm and GitOps. Useful patterns:

• Foundations module: VPC, subnets, NAT, IAM roles, and base secrets. One module per region.
  
• Cluster module: EKS or GKE with version pins, audit logging, network policies, and workload identity. Output kubeconfig to a secure store.
  
• Node group module: separate pools for API, async workers, real time pricing, and batch. Mark worker pools as preemptible or spot where safe.
  
• Add ons module: Cluster Autoscaler or Karpenter, metrics server, ingress controller, external DNS, Prometheus, and Fluent Bit.
  
• App deployments: Helm charts in a Git repo, rolled out by Argo CD. Every change is reviewed, then applied by a bot, not a human shell.
  

Keep module outputs small and readable. Use workspaces per environment. Tag every cloud resource with team, service, and cost center so finance can report on spend the next day.

Scale Up Fast, Scale Down Fast

Scaling is a chain. Each link must be quick and predictable.

• Horizontal Pod Autoscaler: scale on CPU, memory, and custom metrics like request per second or queue length from Prometheus. For bet placement, use pending bet queue depth with a low stabilization window so pods rise within seconds.
  
• Cluster autoscaling: enable Cluster Autoscaler or Karpenter to add nodes when pods are pending. Keep a small warm pool of ready nodes during match windows. On EKS, managed node groups with multiple instance types improve placement. On GKE, use multiple node pools and enable surge upgrades outside match time.
  
• Event driven scale: for feeds and workers, KEDA can scale based on Kafka lag or Pub/Sub backlog. This prevents slow settlement during halftime when loads swing.
  
• Safe surge: set PodDisruptionBudgets so core services keep enough replicas during node drains. Use topology spread constraints to distribute pods across zones.
  
• Ingress scale: tune load balancer target capacity units, connection draining, and health check intervals. Prefer regional load balancers for faster failover.
  

Cut Costs After The Rush

Spikes are short. Money burns when the system stays big after the crowd leaves. Use these policies:

• Fast scale down: use low downscale stabilization for HPA on browsing services. Keep a longer window for stateful or write heavy services to avoid thrash.
  
• Spot and on demand mix: run stateless workers on spot or preemptible nodes with a floor of on demand capacity. Keep critical paths like payments on on demand nodes.
  
• Right size continuously: export Prometheus data to a rightsizing job. Adjust requests so utilization stays near target. Over time, this reduces waste and tightens autoscaling response.
  
• Workload schedules: pause non essential batch jobs during match peaks and resume after traffic normalizes. Tie this to a calendar of sports events.
  

Keep Data Fresh And Safe

Odds and balances cannot lag. Separate hot paths from heavy writes:

• Use Redis with replication for odds and market state. Keep eviction policies clear. Warm caches before kickoff using a scheduled job.
  
• For balances and bets, write to a primary database and fan out events on Kafka. Workers update read models for dashboards and risk views.
  
• Keep idempotency keys for bet placement. If a pod restarts mid request, the system can retry without double charging.
  
• Apply database connection pooling per service. Scale read replicas during peaks, then reduce when queues shrink.
  

Ship Changes Safely

Peaks often land during an active release cycle. Reduce risk:

• Blue green or canary: split traffic at the ingress and watch error rate, latency, and cashout success. Promote only if SLOs hold.
  
• Automated rollback: if p95 latency or error budgets breach for a few minutes, roll back the last tag without debate.
  
• Feature flags: disable expensive features like heavy recommendations during sustained spikes.
  
• Chaos and load tests: run steady drills. Reproduce match day patterns with replayed traffic. Add fault tests for a failed zone, a slow third party, or a noisy neighbor node.
  

Lock Down The Cluster

Betting platforms handle money, identity, and high public interest. Keep a tight posture:

• Use network policies to block east west traffic that is not needed.
  
• Sign images and scan them before deploy. Store secrets in a managed vault.
  
• Rate limit at the edge. Keep separate tiers for anonymous users, logged in users, and admin tools. Tie this to bot detection to handle scraping during events.
  
• Build a DDoS runbook and test it with the provider. Public guidance from CISA on DDoS resilience is a good checklist for edge controls and response playbooks. See their advisory for planning details. https://www.cisa.gov/
  

For container security patterns and hardening, NIST’s guide on application containers is a solid reference to build a baseline for clusters and images. https://csrc.nist.gov/publications/

Match Day Runbook

An hour before kickoff, GitOps applies a schedule that warms extra nodes, scales Redis and the API tier, and bumps read replicas. HPAs watch custom metrics and start from a higher min replica count. 

At kickoff, the ingress opens more backends and the autoscaler adds nodes as queue depth rises. The payment service holds steady on on demand nodes. During halftime, workers drain backlogs and the odds service scales again as live prices shift. 

After the final whistle, scale down policies cut pods and nodes within minutes, then the system goes back to baseline.

A setup like this keeps latency low when it matters and saves cost when it does not. The same patterns work across EKS and GKE, and let teams add regions as the user base grows across markets.

Final Thoughts

Make your platform scale on data, not on hope. Define SLOs, pin them to autoscaling rules, and automate both scale up and scale down. 

Keep critical paths on stable nodes, push stateless work to spot, and release with canaries and fast rollback. With this discipline, peak nights feel ordinary to the system and calm to the team.