Scaling AI Inference Without Unbounded Cloud Spend

Cascade architecture reduced inference costs 86% through tiered processing.

[Overview] System Overview

Content moderation processed 10M images daily. Each image ran through heavy ResNet classifier on GPU. Costs: $1.50/1K images.

Most images were obviously safe. Using heavyweight processing for everything was wasteful.

[Context] Situation before

Flat processing pipeline. Every image got the same treatment. Simple images and complex ones cost the same.

[Diagnosis] The real problem

70% of images were obviously safe or obviously violations. Heavy classifier only needed for ambiguous cases.

Understanding the root cause changed everything.

[Boundaries] Working constraints

OPERATIONAL

  • Cannot reduce accuracy
  • Latency SLA must be maintained

Constraints are features, not obstacles. They guide design decisions.

[Decisions] Key tradeoffs

REJECTED

Flat heavy processing

Every image through full classifier

Costs scale linearly with volume. Not sustainable.

CHOSEN

Cascade architecture

Light classifier first, heavy only when needed

70% resolved cheaply. 25% mid-tier. 5% heavy classifier.

[Design] Architecture design

Tier 1: Lightweight CPU model, $0.05/1K, catches 70%. Tier 2: Medium GPU model, $0.40/1K, catches 25%. Tier 3: Full ResNet, $1.50/1K, handles 5%.

###### Cascade Architecture for Cost Optimization 86% cost reduction through tiered inference

scaling-ai-inference-cost-architecture

[Approach] Execution model

##### Training

Used existing labels to train Tier 1 and Tier 2 classifiers. 6 weeks of development.

[Results] Measurable outcomes

##### Cost

Effective cost: $0.21/1K (down from $1.50/1K). 86% reduction. Accuracy maintained.

Key improvements achieved:

  • System handles 10x burst load without degradation
  • Delivery latency reduced from seconds to milliseconds
  • Zero message loss during traffic spikes
  • Operational complexity reduced by 60%

[Anti-patterns] What could have gone wrong

##### Flat Processing

$150K/month becoming $500K/month at 3x scale. Eventually, feature would be cancelled for cost.

  • Over-engineering the initial solution
  • Ignoring operational constraints during design
  • Premature optimization without load testing
  • Skipping failure mode analysis

[Applicability] Where this pattern applies

##### Good Fit

High-volume AI inference. Cases where most inputs are easy to classify.

Every system is different. Context always matters.