Operational Efficiency and Optimization for GenAI Applications — AWS AIP-C01 Domain 4

mindmap
  root((Domain 4<br/>Operational<br/>Efficiency))
    **Task 4.1 — Cost Optimization**
      4.1.1 Token Efficiency
        Token Estimation & Tracking
        Context Window Optimization
        Response Size Controls
        Prompt Compression
        Context Pruning
      4.1.2 Model Selection
        Cost-Capability Tradeoff
        Tiered FM Usage
        Price-to-Performance Ratio
        Inference Cost Balancing
      4.1.3 High-Performance FM
        Batching Strategies
        Capacity Planning
        Utilization Monitoring
        Auto-Scaling
        Provisioned Throughput
      4.1.4 Intelligent Caching
        Semantic Caching
        Result Fingerprinting
        Edge Caching
        Deterministic Hashing
        Prompt Caching
    **Task 4.2 — Performance Optimization**
      4.2.1 Responsive AI Systems
        Pre-Computation
        Latency-Optimized Models
        Parallel Requests
        Response Streaming
        Performance Benchmarking
      4.2.2 Retrieval Performance
        Index Optimization
        Query Preprocessing
        Hybrid Search
        Custom Scoring
      4.2.3 FM Throughput
        Token Processing Optimization
        Batch Inference
        Concurrent Invocation
      4.2.4 FM Performance
        Parameter Configurations
        A/B Testing
        Temperature & Top-k/Top-p
      4.2.5 Resource Allocation
        Capacity Planning
        Utilization Monitoring
        Auto-Scaling for GenAI
      4.2.6 System Performance
        API Call Profiling
        Vector DB Query Optimization
        LLM Inference Latency Reduction
        Service Communication Patterns

graph TB
    subgraph "Incoming Request"
        USER[Customer Query]
        APIGW[API Gateway<br/>WebSocket]
    end

    subgraph "Task 4.1 — Cost Optimization Layer"
        style CACHE_CHECK fill:#2ecc71,color:#fff
        style TIER fill:#2ecc71,color:#fff
        style COMPRESS fill:#2ecc71,color:#fff
        style BATCH fill:#2ecc71,color:#fff
        CACHE_CHECK["4.1.4 Cache Check<br/>Semantic + Edge + Prompt"]
        TIER["4.1.2 Model Router<br/>Haiku vs Sonnet"]
        COMPRESS["4.1.1 Token Optimizer<br/>Compress + Prune + Limit"]
        BATCH["4.1.3 Throughput Manager<br/>Batch + Scale + Provision"]
    end

    subgraph "Task 4.2 — Performance Optimization Layer"
        style STREAM fill:#3498db,color:#fff
        style RAG fill:#3498db,color:#fff
        style THROUGHPUT fill:#3498db,color:#fff
        style TUNE fill:#3498db,color:#fff
        style RESOURCE fill:#3498db,color:#fff
        style PROFILE fill:#3498db,color:#fff
        STREAM["4.2.1 Response Streaming<br/>Pre-compute + Parallel"]
        RAG["4.2.2 Retrieval Optimizer<br/>Hybrid Search + Index"]
        THROUGHPUT["4.2.3 Throughput Pipeline<br/>Concurrent Invocations"]
        TUNE["4.2.4 Parameter Tuner<br/>Temp/TopK/TopP + A/B"]
        RESOURCE["4.2.5 Resource Allocator<br/>Capacity + Auto-Scale"]
        PROFILE["4.2.6 System Profiler<br/>API + Vector DB + Latency"]
    end

    subgraph "FM Layer"
        BEDROCK[Amazon Bedrock<br/>Claude 3 Sonnet/Haiku]
        OPENSEARCH[OpenSearch Serverless<br/>Vector Store]
        DYNAMO[DynamoDB<br/>Sessions/Products]
    end

    USER --> APIGW --> CACHE_CHECK
    CACHE_CHECK -->|miss| TIER
    CACHE_CHECK -->|hit| APIGW
    TIER --> COMPRESS --> BATCH
    BATCH --> THROUGHPUT --> BEDROCK
    TIER --> RAG --> OPENSEARCH
    BEDROCK --> TUNE
    TUNE --> STREAM --> APIGW
    RESOURCE --> BATCH & THROUGHPUT
    PROFILE --> RAG & THROUGHPUT & STREAM

    style BEDROCK fill:#ff9900,color:#000
    style OPENSEARCH fill:#ff9900,color:#000
    style DYNAMO fill:#ff9900,color:#000

pie title MangaAssist Monthly Cost Before vs After Optimization
    "Eliminated by Caching (4.1.4)" : 20
    "Eliminated by Template Routing (4.1.1)" : 15
    "Reduced by Model Tiering (4.1.2)" : 18
    "Reduced by Prompt Compression (4.1.1)" : 12
    "Reduced by Batching (4.1.3)" : 8
    "Remaining Optimized Spend" : 27

gantt
    title Optimized Request Latency Budget (p95 Target: < 1500ms)
    dateFormat X
    axisFormat %L ms

    section Edge
    TLS + Routing           :0, 25
    Auth + Rate Limit       :25, 45

    section Cost Optimization
    Cache Check (4.1.4)     :45, 55
    Model Selection (4.1.2) :55, 62
    Prompt Compress (4.1.1) :62, 70

    section Retrieval
    Query Preprocess (4.2.2):70, 85
    Hybrid Search (4.2.2)   :85, 220

    section Intelligence
    LLM First Token (4.2.1) :220, 550
    LLM Streaming (4.2.3)   :550, 1200

    section Delivery
    Guardrails              :1200, 1240
    Format + Stream (4.2.1) :1240, 1300

graph LR
    USER[Customer] --> APIGW[API Gateway<br/>WebSocket]
    APIGW --> ECS[ECS Fargate<br/>Orchestrator]
    ECS --> CACHE[ElastiCache Redis<br/>Semantic Cache]
    ECS --> BEDROCK[Bedrock Claude 3<br/>Sonnet/Haiku]
    ECS --> OPENSEARCH[OpenSearch<br/>Serverless<br/>Vector Store]
    ECS --> DYNAMO[DynamoDB<br/>Sessions/Products]
    ECS --> GUARD[Bedrock<br/>Guardrails]
    BEDROCK --> ECS
    OPENSEARCH --> ECS
    ECS --> APIGW --> USER

    style BEDROCK fill:#ff9900,color:#000
    style OPENSEARCH fill:#ff9900,color:#000
    style ECS fill:#ff9900,color:#000
    style DYNAMO fill:#ff9900,color:#000
    style CACHE fill:#2ecc71,color:#000