Serverless vs VM Cost Tradeoffs
When you design cloud architectures, one of the most impactful decisions you will make is whether to use a serverless model or rely on traditional virtual machines (VMs). Understanding the pricing mechanics of each approach is essential for cost optimization.
- Pay only for actual usage: charged per execution, duration, and memory allocated.
- No costs when code is not running; no charges for idle capacity.
- Infrastructure management handled by the cloud provider.
- Pay for provisioned resources (CPU, memory, storage) regardless of usage.
- Charged for total uptime, even if resources are underutilized.
- Must provision for peak usage, which can lead to higher costs for sporadic or unpredictable workloads.
Serverless (AWS Lambda) Cost Breakdown
- Requests: 1,000,000/month;
- Duration per request: 0.5 seconds;
- Memory: 512MB;
- Total compute: 1,000,000 x 0.5s = 500,000 seconds;
- GB-seconds: 500,000 seconds x 0.5GB = 250,000 GB-seconds.
AWS Lambda Pricing:
- $0.20 per 1M requests = $0.20;
- $0.00001667 per GB-second;
- Compute cost: 250,000 x $0.00001667 = $4.17.
Total monthly cost: $0.20 + $4.17 = $4.37.
VM (AWS t3.small EC2 Instance) Cost Breakdown
- Instance: t3.small (2 vCPU, 2GB RAM);
- On-demand price: ~$0.023 per hour;
- Monthly uptime: 24 x 30 = 720 hours;
- Monthly cost: 720 x $0.023 = $16.56.
Total monthly cost: $16.56.
When deciding between serverless and VM-based architectures, consider your workload patterns.
- Serverless is most cost-effective for workloads with variable, unpredictable, or low-to-moderate traffic, where you do not need to provision for peak capacity and can benefit from per-request billing;
- It is also ideal for event-driven applications, infrequent batch jobs, and APIs with spiky or seasonal demand;
- VM-based models are better suited for steady, predictable workloads that require consistent performance, long-running processes, or specialized hardware.
Choose serverless for flexibility and cost savings in unpredictable environments, and choose VMs for stability and control in consistent, high-usage scenarios.
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When you design cloud architectures, one of the most impactful decisions you will make is whether to use a serverless model or rely on traditional virtual machines (VMs). Understanding the pricing mechanics of each approach is essential for cost optimization.
- Pay only for actual usage: charged per execution, duration, and memory allocated.
- No costs when code is not running; no charges for idle capacity.
- Infrastructure management handled by the cloud provider.
- Pay for provisioned resources (CPU, memory, storage) regardless of usage.
- Charged for total uptime, even if resources are underutilized.
- Must provision for peak usage, which can lead to higher costs for sporadic or unpredictable workloads.
Serverless (AWS Lambda) Cost Breakdown
- Requests: 1,000,000/month;
- Duration per request: 0.5 seconds;
- Memory: 512MB;
- Total compute: 1,000,000 x 0.5s = 500,000 seconds;
- GB-seconds: 500,000 seconds x 0.5GB = 250,000 GB-seconds.
AWS Lambda Pricing:
- $0.20 per 1M requests = $0.20;
- $0.00001667 per GB-second;
- Compute cost: 250,000 x $0.00001667 = $4.17.
Total monthly cost: $0.20 + $4.17 = $4.37.
VM (AWS t3.small EC2 Instance) Cost Breakdown
- Instance: t3.small (2 vCPU, 2GB RAM);
- On-demand price: ~$0.023 per hour;
- Monthly uptime: 24 x 30 = 720 hours;
- Monthly cost: 720 x $0.023 = $16.56.
Total monthly cost: $16.56.
When deciding between serverless and VM-based architectures, consider your workload patterns.
- Serverless is most cost-effective for workloads with variable, unpredictable, or low-to-moderate traffic, where you do not need to provision for peak capacity and can benefit from per-request billing;
- It is also ideal for event-driven applications, infrequent batch jobs, and APIs with spiky or seasonal demand;
- VM-based models are better suited for steady, predictable workloads that require consistent performance, long-running processes, or specialized hardware.
Choose serverless for flexibility and cost savings in unpredictable environments, and choose VMs for stability and control in consistent, high-usage scenarios.
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