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ADR-016: GCP vs AWS Performance Parity — Cost vs Performance Trade-off

  • Status: Accepted
  • Date: 2026-03-18
  • Authors: Duque Ortega Mutis
  • Related: ADR-015 (async inference), ADR-013 (parity policy)

TL;DR: BankChurn shows 2-3× higher latency on GCP under load due to e2-medium shared CPU vs AWS t3.medium burstable credits. Accepted the difference as a documented cost vs performance trade-off ($24/mo vs $145/mo for parity). Both clouds meet the <500ms idle SLA with 0% errors under 100 concurrent users.

Context

After implementing async inference (ADR-015), load testing revealed significant performance differences between GCP and AWS for BankChurn:

Environment Idle p50 Load p50 (50 users) Stress p50 (100 users)
GCP (GKE) 200ms 3100ms 8200ms
AWS (EKS) 110ms 120ms 130ms

NLPInsight and ChicagoTaxi show comparable performance on both clouds (78-100ms).

Root Cause Analysis

BankChurn is CPU-bound — StackingClassifier (5 base learners + meta-learner) consumes ~100ms of pure CPU time per prediction.

Infrastructure differences:

Factor GCP (current) AWS (current) Impact on BankChurn
Instance type e2-medium t3.medium High
vCPU allocation 2 shared (burstable) 2 burstable (better credits) High
CPU frequency AMD EPYC Rome 2.2 GHz Intel Xeon Platinum 2.5-3.1 GHz Critical
Network NGINX Ingress (L7) NLB (L4) Moderate
Node count 1 (all 6 pods) 1-2 (auto-scaled) Moderate

Why NLPInsight/ChicagoTaxi don't improve on AWS: - NLPInsight: I/O-bound (tokenizer), not CPU-saturated - ChicagoTaxi: Lightweight RandomForest (~5ms CPU), not CPU-saturated

Decision

Accept the performance difference as a documented cost vs performance trade-off.

Do NOT upgrade GCP instance types to match AWS performance.

Rationale

  1. BankChurn 200ms idle latency is acceptable for a bank churn prediction tool (analyst-facing, not real-time payments)
  2. 0% error rate under 100 concurrent users on both clouds — production-ready
  3. Cost efficiency: e2-medium at $24/mo vs n2-highcpu-2 at $51/mo (+$27/mo for marginal latency improvement)
  4. Portfolio value: Demonstrating multi-cloud trade-off analysis is more valuable than spending $100+/mo to hide infrastructure differences

Performance Targets Met

Service SLA GCP p50 AWS p50 Status
BankChurn < 500ms idle 200ms 110ms ✅ Both pass
NLPInsight < 200ms idle 78ms 100ms ✅ Both pass
ChicagoTaxi < 200ms idle 100ms 120ms ✅ Both pass
All services < 1% errors under load 0-0.02% 0% ✅ Both pass

Consequences

Positive

  • Cost-effective: $24/mo GCP vs potential $145/mo for c2-standard-4
  • Educational: Portfolio demonstrates understanding of cloud trade-offs, not just "make everything fast"
  • Realistic: Production systems balance cost vs performance, not maximize performance at any cost
  • Documented: ADR explains the difference instead of hiding it

Negative

  • GCP BankChurn latency is 2-3x higher than AWS under concurrent load
  • May raise questions from recruiters unfamiliar with cloud instance differences

Mitigation

  • Document the difference in docs/MULTI_CLOUD_COMPARISON.md with root cause analysis
  • Include this ADR in portfolio navigation
  • Prepare interview talking points: "I chose to optimize for cost efficiency while meeting SLAs rather than over-provisioning for vanity metrics"

Alternatives Considered

1. Upgrade GCP to n2-highcpu-2 (2 dedicated vCPU, 2.8 GHz)

  • Cost: +$27/mo
  • Expected improvement: 200ms → ~120-150ms
  • Rejected: Marginal improvement, doubles infrastructure cost

2. Upgrade GCP to c2-standard-4 (4 dedicated vCPU, 3.8 GHz)

  • Cost: +$121/mo
  • Expected improvement: 200ms → <100ms (would beat AWS)
  • Rejected: 6x cost increase for a portfolio project, unsustainable

3. Increase min_node_count to 2 (distribute load)

  • Cost: +$24/mo
  • Expected improvement: 200ms → ~150ms (moderate)
  • Rejected: Still doesn't achieve parity, adds complexity

4. Node affinity for BankChurn on dedicated c2 node pool

  • Cost: +$145/mo for dedicated node
  • Expected improvement: Best performance, but highest cost
  • Rejected: Over-engineering for a 3-service portfolio

Implementation

No code changes required. Document the performance difference in: - docs/MULTI_CLOUD_COMPARISON.md — add root cause analysis - docs/load-test-results.md — already includes GCP vs AWS comparison - Portfolio README — mention cost-optimized infrastructure

Interview Talking Points

"BankChurn shows 2-3x higher latency on GCP under load because I'm using cost-optimized e2-medium instances ($24/mo) versus AWS t3.medium. The difference is CPU frequency and allocation — BankChurn's StackingClassifier is CPU-bound, while NLPInsight and ChicagoTaxi aren't, so they perform identically on both clouds.

I could upgrade to c2-standard-4 for <100ms latency, but that's $145/mo — 6x the cost — for a portfolio project. Instead, I documented the trade-off in ADR-016. In production, I'd make this decision based on actual SLAs and business requirements, not vanity metrics. Both clouds meet the <500ms SLA with 0% errors under 100 concurrent users."

References