Scale without guesswork—choose the right compute model for your growth curve.

Table of Contents

1. Why Compute Model Matters
2. Architecture Comparison
3. Performance & Cost Benchmarks
4. Deployment Workflows
5. Observability & Operations
6. Case Study: SaaS Scaling Scenario
7. Conclusion

Why Compute Model Matters

Your choice impacts:

• Cold Start Latency
• Concurrent Throughput
• Cost per Invocation vs. Reserved Capacity
• Operational Overhead
• Security & Compliance Boundaries

Architecture Comparison

flowchart LR
    subgraph Serverless
      A[API Gateway] --> B[Lambda / Functions]
      B --> C[Managed DB]
    end
    subgraph Containers
      D[Load Balancer] --> E[Kubernetes Cluster]
      E --> F[Service Pods]
      F --> C
    end

Feature	Serverless Functions	Containerized Services
Provisioning	Automatic, event-driven	Manual/auto-scaling groups
Startup Time	Cold start (100–300 ms)	Warm containers (5–50 ms)
Scaling Granularity	Per-request	Pod-level (10–100 reqs/pod)
Billing Model	Pay-per-invocation	Pay-for-provisioned vCPU/RAM
Operational Complexity	Low (managed by provider)	Medium–High (cluster ops)
Vendor Lock-in Risk	High (proprietary runtimes)	Low–Medium (standard images)

Performance & Cost Benchmarks

Scenario	Lambda @ 512 MB	ECS Fargate @ 512 MB	GKE Autopilot @ 512 MB
10 M invocations/month	$1.50	$120	$110
Average p95 latency	120 ms	45 ms	50 ms
Ops overhead (FTE)	0.1	1.0	1.2

Cost Formula

$$\mathrm{Monthly\ Cost} = \mathrm{Invocations} \times \mathrm{MemoryGB} \times \mathrm{Duration\_Sec} \times \mathrm{Rate\_PerGB\_Sec}$$

Deployment Workflows

Serverless CI/CD

# serverless.yml (AWS SAM)
Resources:
  ApiFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: src/
      Handler: handler.main
      Runtime: nodejs18.x
      MemorySize: 512
      Timeout: 10
      Events:
        ApiGateway:
          Type: Api
          Properties:
            Path: /api/{proxy+}
            Method: ANY

• Pipeline: CodeCommit → CodeBuild → SAM Deploy
• Testing: Local invocation via sam local invoke

Container CI/CD

# deployment.yaml (K8s)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: api
  template:
    metadata:
      labels:
        app: api
    spec:
      containers:
      - name: api
        image: registry/company/api:latest
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        ports:
        - containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
  name: api-lb
spec:
  selector:
    app: api
  ports:
  - port: 80
    targetPort: 8080

• Pipeline: GitHub Actions → Docker Build → Push → kubectl apply
• Testing: helm chart dry-run + integration tests in a staging cluster

Observability & Operations

Concern	Serverless	Containers
Logging	Centralized (CloudWatch)	Cluster-wide (EFK stack)
Tracing	X-Ray	OpenTelemetry + Jaeger
Metrics	Provider dashboards + Prometheus	Prometheus + Grafana
Security Patching	Managed by provider	Team-managed image updates

Case Study: SaaS Scaling Scenario

A B2B SaaS platform experienced 5× traffic during product launch:

• Serverless: Cold starts spiked p95 latency to 650 ms under burst; user complaints rose 15%
• Containers: Autoscaled to 20 pods; steady p95 latency ≈ 60 ms; ops load ≈ 0.5 FTE

Switching to containers for core API reduced support tickets by 40% and saved $20k/month at scale.

Conclusion

Choose serverless for unpredictable, low-throughput workloads and minimal ops. Opt for containers when consistent performance, predictable cost, and full control matter. Align your compute model with your SaaS growth stage to avoid costly re-architecture.