Cloud infrastructure cost have surpassed $200 billion globally and have grown 5x in the last decade. For many companies, cloud spending is now the second-largest expense after payroll. However, much of this cost is waste—due to overprovisioning, static resource allocation, and inefficient scaling. In this webinar, we explore why existing cloud infra is inefficient and how to cut infra cost by optimizing resource use and eliminating idle compute waste.

Key Highlights from the Session

1. Cloud Infrastructure is Inefficient

• Overprovisioning for peak demand keeps unused resources running unnecessarily.

• Static scaling leads to waste, as compute instances remain allocated even when idle.

• Limited visibility into underutilized resources makes it hard to rightsize workloads effectively.

2. Optimize Resource Use

• Dynamic scaling and real-time rightsizing ensure compute resources match actual demand.

• Bin-packing consolidates workloads onto fewer machines, reducing overhead.

• Predictive scaling automatically adjusts capacity based on workload trends to prevent overprovisioning.

3. Cut Idle Compute Costs

• Hibernation suspends inactive workloads, preserving state without consuming compute.

• Workload migration moves workloads dynamically, reallocating resources efficiently.

• Ephemeral compute automatically shuts down unused dev/test environments, eliminating waste.

4. Optimize GPU Efficiency

• Multi-tenancy enables multiple workloads to share GPUs, preventing unnecessary allocations.

• Snapshotting and live migration allow workloads to move across GPUs without restart.

• Cold start optimization preloads model weights, reducing startup times by 2–10x.

5. Optimize Production and Pre-Production Workloads

• Production workloads require dynamic scaling, compute pooling, and workload rightsizing to prevent overprovisioning.

• Pre-production environments (staging, dev, test) often run idle, making automated hibernation, resource cleanup, and spot instance usage critical for cost savings.

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