At Oracle, we focus on optimizing cloud costs with a mix of strategies. Auto-scaling ensures resources match real-time demand, avoiding over-provisioning. For non-critical workloads, preemptible or spot instances are cost-effective options. We also rely on Oracle Cloud Infrastructure’s cost analysis tools to monitor spending, identify inefficiencies, and optimize resource allocation. Combining these with flexible savings models and efficient workload placement helps us scale effectively without overspending.

One technique we’ve found effective is dynamically blending on-demand, reserved, and spot instances based on workload patterns. We start by locking in reserved instances for predictable baseline loads, then layer in spot instances for flexible, non-critical tasks. We also use predictive analytics to anticipate peak times and warm up resources in advance. Additionally, we employ robust tagging and cost governance frameworks to identify wasteful spending—such as idle storage volumes or underutilized VMs—and take swift corrective action. Regular cost reviews and detailed, team-specific reports keep everyone accountable and aware of their spending.

• Implement auto-scaling with proactive and reactive policies based on metrics and schedules • Right-size instances and use spot/reserved instances for cost savings (up to 90%) • Leverage cloud bursting for handling overflow traffic during peak periods • Deploy elastic resource allocation with pay-as-you-go flexibility • Monitor usage patterns and costs continuously using cloud provider tools • Use load balancers and distributed systems for even traffic distribution • Optimize data transfer costs through CDNs and compression • Train teams on cost-aware practices and cloud optimization • Regular performance testing to identify bottlenecks • Consider multi-cloud strategies for vendor flexibility and cost optimization

To optimize costs while scaling cloud infrastructure for peak demand, focus on leveraging elastic scaling to dynamically adjust resources based on real-time needs, avoiding over-provisioning. Use cost-effective services like spot or reserved instances for predictable workloads and auto-scaling for fluctuating demands. Implement monitoring tools to track usage and identify underutilized resources for optimization. Take advantage of multi-cloud or hybrid solutions to compare costs and negotiate better pricing. Employ practices such as right-sizing instances, using storage tiering, and enabling data lifecycle policies to reduce unnecessary expenses.

"Measure it to improve on it". Three factors which are responsible costs in cloud are resources, services and location. Cost analysis and budget alerts from cost management module of cloud will help to measure and optimize cost for future on these three factors. Auto-scaling will ensure to pay only for specific time period of peak demand. Additionally, we can leverage cost optimization by exploring reserved Instances, Spot Pricing and cost reduction for existing licenses.

We leverage spot instance luckily our workload can tolerate and designed for batch processing that can resume where it left off anytime otherwise we can use autoscaling too it will surely work but not cost efficient for our use case

In my experience, effective cloud cost optimization during peak demand involves a strategic combination of techniques. Auto-scaling is crucial for dynamically managing resources, ensuring capacity aligns with real-time usage while avoiding over-provisioning. For non-critical workloads, leveraging spot instances has proven cost-efficient, offering substantial savings without affecting key operations. I also prioritize cost visibility through robust monitoring tools such as AWS Cost Explorer or custom dashboards, enabling proactive management and adherence to budgets. These strategies collectively balance scalability, reliability, and cost-efficiency. I'd be interested to hear about other approaches that have worked effectively.