TL;DR

Scalable AI infrastructure balances compute costs, latency requirements, and reliability. Use managed services where possible, implement smart caching, design for async processing, and always have cost controls in place. Scale horizontally, monitor relentlessly, and optimize continuously.

Why it matters

AI workloads are expensive and unpredictable. A viral feature can 10x your costs overnight. Without scalable infrastructure, you'll either overspend on unused capacity or crash under load. Good infrastructure lets you handle growth without burning money or disappointing users.

Infrastructure decisions

Build vs. buy

Approach When to use Tradeoffs
API services (OpenAI, Anthropic) Getting started, variable load Easy but per-token costs add up
Managed inference (AWS Bedrock, Vertex AI) Enterprise needs, data residency More control, still managed
Self-hosted models High volume, cost optimization Full control but operational burden
Hybrid Mixing workload types Complexity but optimized costs

Cloud provider considerations

AWS:

  • SageMaker for training and hosting
  • Bedrock for managed foundation models
  • Strong GPU instance availability

Google Cloud:

  • Vertex AI for end-to-end ML
  • TPU access for specific workloads
  • Good integration with TensorFlow

Azure:

  • Azure OpenAI Service for GPT models
  • Strong enterprise integration
  • Cognitive Services ecosystem

Scaling strategies

Horizontal scaling

Add more instances to handle load:

Implementation:

  • Containerize inference services
  • Use Kubernetes for orchestration
  • Implement load balancing
  • Design stateless services

Benefits:

  • Linear cost scaling
  • High availability
  • Geographic distribution

Challenges:

  • Model loading time (cold starts)
  • Memory requirements per instance
  • Orchestration complexity

Vertical scaling

Use bigger machines:

When it works:

  • Large model requirements
  • Low-latency single requests
  • Simpler architecture needs

Limitations:

  • Hardware ceilings
  • Single point of failure
  • Expensive unused capacity

Async processing

Decouple requests from processing:

Architecture:

Request ā†’ Queue ā†’ Worker Pool ā†’ Result Store ā†’ Notification
   ā†“                                              ā†“
Immediate acknowledgment              Callback/polling for result

Benefits:

  • Smooth load spikes
  • Better resource utilization
  • Retry handling built-in

Use cases:

  • Batch processing
  • Long-running generations
  • Non-real-time features

Cost optimization

Caching strategies

Don't pay for the same computation twice:

What to cache:

  • Embedding vectors (expensive to compute)
  • Common query results
  • Intermediate chain-of-thought steps
  • Retrieved context chunks

Cache tiers:

L1: In-memory (milliseconds, limited size)
L2: Redis/Memcached (low milliseconds, medium size)
L3: Database (higher latency, large size)

Cache hit strategies:

  • Exact match (simple, limited hits)
  • Semantic similarity (more hits, complexity)
  • Prefix matching (for completion scenarios)

Model selection optimization

Route to appropriate models:

Task complexity Model choice Cost impact
Simple classification Small/fast model 10-100x cheaper
Standard chat Medium model Baseline
Complex reasoning Large model Higher cost, better results
Specialized tasks Fine-tuned models Variable

Batch processing

Combine requests when latency allows:

Benefits:

  • Lower per-request overhead
  • Better GPU utilization
  • Volume discounts from providers

Implementation:

  • Collect requests over time window
  • Process in batches
  • Fan out results
  • Set maximum batch wait time

Reliability patterns

Redundancy

Don't depend on single points of failure:

  • Multiple availability zones
  • Backup model providers
  • Replicated caches
  • Distributed queues

Circuit breakers

Fail fast when dependencies are down:

Normal: Requests flow through
Errors exceed threshold: Circuit opens
Open: Requests fail immediately (or use fallback)
After timeout: Circuit half-opens, tests recovery
Success: Circuit closes, normal operation resumes

Graceful degradation

Maintain partial functionality under stress:

Degradation levels:

  1. Full service: All features available
  2. Limited: Disable expensive features
  3. Cached: Serve cached/stale results
  4. Minimal: Basic functionality only

Monitoring and observability

Key metrics

Performance:

  • Request latency (p50, p95, p99)
  • Throughput (requests/second)
  • Queue depth and wait times
  • Cache hit rates

Reliability:

  • Error rates by type
  • Availability percentage
  • Recovery time
  • Circuit breaker state

Cost:

  • Spend per request
  • Spend per user/feature
  • Compute utilization
  • Waste (unused capacity)

Alerting thresholds

Metric Warning Critical
Latency p95 2x baseline 5x baseline
Error rate >1% >5%
Queue depth >1000 >10000
Daily spend >120% budget >150% budget

Infrastructure checklist

Before launch

  • Load tested at 2-3x expected peak
  • Cost controls and spending alerts configured
  • Fallback strategies implemented
  • Monitoring and dashboards set up
  • Runbooks for common incidents

During operation

  • Daily cost review
  • Weekly capacity planning
  • Monthly optimization review
  • Quarterly architecture review

Common mistakes

Mistake Consequence Prevention
No spending limits Budget explosion Set hard limits, alerts
Over-provisioning Wasted money Right-size, use autoscaling
Under-provisioning Poor user experience Load test, plan for peaks
Single provider dependency Outage vulnerability Multi-provider strategy
No caching Unnecessary costs Cache aggressively

What's next

Build production-ready AI systems: