What Problem This Solves

The ML Service Selection Challenge: Organizations face a critical decision when implementing machine learning on AWS: should they use pre-built AI services (Rekognition, Comprehend, Transcribe), build custom models with SageMaker, or develop entirely custom solutions? The wrong choice leads to unnecessary complexity, excessive costs, or models that don’t meet business requirements.

What This Guide Provides: A systematic decision framework for selecting the right ML approach based on:

• Use case requirements and constraints
• Team expertise and resources
• Cost and time-to-market considerations
• Customization needs and data availability
• Compliance and operational requirements

The AWS ML Service Spectrum

AWS offers three tiers of ML services, each suited for different scenarios:

Tier 1: AI Services (Pre-Built, Fully Managed)

Services: Rekognition, Comprehend, Transcribe, Polly, Translate, Textract, Forecast, Personalize

What they are:

• Pre-trained models ready to use via API calls
• No ML expertise required
• Pay-per-use pricing (per image, per character, per minute)
• Instant deployment (no model training)

Example:

import boto3

# Computer vision with Rekognition - zero ML expertise needed
rekognition = boto3.client('rekognition')

response = rekognition.detect_labels(
    Image={'S3Object': {'Bucket': 'my-bucket', 'Name': 'product.jpg'}},
    MaxLabels=10
)

for label in response['Labels']:
    print(f"{label['Name']}: {label['Confidence']:.2f}%")

When to use: Common use cases with standard requirements (sentiment analysis, image classification, translation).

Tier 2: SageMaker (Custom Models, Managed Infrastructure)

Services: SageMaker Training, Inference, Built-in Algorithms, AutoML (Autopilot), Feature Store, Model Registry

What it is:

• Platform for building, training, and deploying custom ML models
• Managed infrastructure (compute, storage, orchestration)
• Bring your own model code or use built-in algorithms
• Requires ML expertise (data science, feature engineering)

Example:

from sagemaker.pytorch import PyTorch

# Custom model training with SageMaker
estimator = PyTorch(
    entry_point='train.py',  # Your custom training code
    role='arn:aws:iam::123456789012:role/SageMakerRole',
    instance_type='ml.p3.2xlarge',
    instance_count=1,
    framework_version='1.12',
    py_version='py38'
)

estimator.fit({'training': 's3://my-bucket/training-data/'})

When to use: Custom use cases requiring domain-specific models, proprietary data, or performance tuning.

Tier 3: DIY (Custom ML, Self-Managed)

Services: EC2, ECS/EKS, Lambda (for inference), S3, custom ML frameworks (TensorFlow, PyTorch)

What it is:

• Complete control over ML infrastructure and code
• You manage everything: training, deployment, scaling, monitoring
• Use open-source frameworks and custom pipelines
• Maximum flexibility, maximum operational burden

Example:

# Train model on EC2 with custom infrastructure
import torch
import torch.nn as nn

# Custom model architecture
model = CustomNeuralNetwork()
optimizer = torch.optim.Adam(model.parameters())

# Manual training loop on EC2 instance
for epoch in range(num_epochs):
    for batch in dataloader:
        optimizer.zero_grad()
        loss = compute_loss(model(batch))
        loss.backward()
        optimizer.step()

# Manual deployment to ECS/EKS
# (You handle: containerization, load balancing, autoscaling, monitoring)

When to use: Extremely specialized requirements, existing ML infrastructure, or cost optimization at massive scale.

Decision Framework: Choosing the Right Tier

Step 1: Use Case Mapping

Start with AI Services if your use case matches these categories:

If your use case appears above: Start with AI Services. Only move to SageMaker if AI Services don’t meet accuracy, latency, or customization requirements.

If your use case is NOT above: Proceed to Step 2.

Step 2: Evaluate Customization Requirements

Ask these questions to determine if SageMaker is needed:

Decision rule: If you answered “SageMaker Required” for 2+ questions, use SageMaker.

Step 3: Assess Team Expertise

Required skills by tier:

Decision rules:

• No ML expertise: Use AI Services exclusively
• Data science team, no MLOps: Use SageMaker
• Full ML engineering team: Consider DIY for specialized use cases, otherwise SageMaker

Step 4: Cost-Benefit Analysis

Total Cost of Ownership (TCO) includes:

• Service/infrastructure costs
• Development time (time-to-market)
• Ongoing maintenance and operations
• Retraining and model updates

Example cost comparison for sentiment analysis use case:

Decision rule: Use the simplest tier that meets requirements. Only move to higher tiers when business value justifies the cost increase.

Detailed Service Selection Patterns

Pattern 1: Start Simple, Upgrade When Needed

Recommended approach: Begin with AI Services, migrate to SageMaker only when required.

Example progression:

Phase 1: Validate with AI Services (Week 1)

# Proof of concept with Comprehend
comprehend = boto3.client('comprehend')

sentiment = comprehend.detect_sentiment(
    Text=customer_review,
    LanguageCode='en'
)

# Validate: Does this meet business requirements?
# Metrics: Accuracy, latency, cost

Phase 2: Evaluate Performance (Weeks 2-4)

• Test with real data at production scale
• Measure accuracy against business requirements
• Calculate actual costs at expected volume

Phase 3: Decide Migration (Week 5)

• If accuracy >90% and cost <$10K/month: Stay with Comprehend
• If accuracy <90% or need custom features: Migrate to SageMaker

Phase 4: Custom Model (if needed) (Months 2-3)

# Migrate to SageMaker with custom model
from sagemaker.huggingface import HuggingFace

estimator = HuggingFace(
    entry_point='train_sentiment.py',
    role=sagemaker_role,
    instance_type='ml.p3.2xlarge',
    transformers_version='4.17',
    pytorch_version='1.10',
    py_version='py38',
    hyperparameters={
        'epochs': 3,
        'train_batch_size': 32,
        'model_name': 'bert-base-uncased'
    }
)

estimator.fit({'train': 's3://my-bucket/sentiment-training-data/'})

Outcome: You invest in SageMaker only after validating that simpler solutions are insufficient.

Pattern 2: Hybrid Approach (AI Services + SageMaker)

Use AI Services for common tasks, SageMaker for specialized models.

Example architecture:

Customer Support System:
├── Transcribe (speech-to-text) ← AI Service
├── Translate (multi-language) ← AI Service
├── Custom Intent Classifier ← SageMaker (domain-specific)
├── Comprehend (PII detection) ← AI Service
└── Polly (voice responses) ← AI Service

Implementation:

def process_customer_call(audio_file, customer_language):
    # Step 1: Transcribe (AI Service)
    transcript = transcribe_audio(audio_file)

    # Step 2: Translate to English if needed (AI Service)
    if customer_language != 'en':
        transcript = translate_text(transcript, customer_language, 'en')

    # Step 3: Detect PII (AI Service)
    redacted_transcript = comprehend.detect_pii_entities(transcript)

    # Step 4: Classify intent (Custom SageMaker Model)
    intent = sagemaker_intent_classifier.predict(redacted_transcript)

    # Step 5: Route to appropriate team based on intent
    route_to_team(intent)

Why this works:

• 80% of tasks (transcription, translation, PII) handled by AI Services (low cost, zero maintenance)
• 20% specialized task (intent classification with company-specific intents) uses SageMaker
• Best of both worlds: simplicity + customization

Pattern 3: SageMaker with Built-in Algorithms

Use SageMaker for custom data, but avoid custom model code with built-in algorithms.

When to use: You need custom training data, but standard algorithms (XGBoost, linear learner, k-means) suffice.

Example - Fraud detection:

from sagemaker import image_uris
from sagemaker.estimator import Estimator

# Use SageMaker's built-in XGBoost (no custom code needed)
container = image_uris.retrieve('xgboost', region='us-east-1', version='1.5-1')

estimator = Estimator(
    image_uri=container,
    role=sagemaker_role,
    instance_count=1,
    instance_type='ml.m5.xlarge',
    volume_size=50,
    max_run=3600,
    output_path='s3://my-bucket/fraud-model/'
)

# Train with your proprietary fraud transaction data
estimator.set_hyperparameters(
    objective='binary:logistic',
    num_round=100,
    max_depth=5,
    eta=0.2
)

estimator.fit({'train': 's3://my-bucket/fraud-training-data/'})

# Deploy for real-time inference
predictor = estimator.deploy(
    initial_instance_count=1,
    instance_type='ml.t2.medium'
)

Benefits:

• ✅ Custom data (your fraud patterns, not generic)
• ✅ No custom code (use battle-tested XGBoost)
• ✅ Managed infrastructure
• ✅ Lower dev cost than full custom models

Pattern 4: SageMaker AutoML (Autopilot)

Let AWS automatically build and tune models for you.

When to use: You have labeled data but lack deep ML expertise.

Example - Customer churn prediction:

from sagemaker.automl.automl import AutoML

automl = AutoML(
    role=sagemaker_role,
    target_attribute_name='churned',  # Column to predict
    output_path='s3://my-bucket/automl-output/',
    max_candidates=10,  # Try up to 10 different models
    job_objective={'MetricName': 'F1'}  # Optimize for F1 score
)

# Autopilot automatically:
# - Explores data
# - Engineers features
# - Tries multiple algorithms (XGBoost, Linear Learner, Deep Learning)
# - Tunes hyperparameters
# - Selects best model

automl.fit(
    inputs='s3://my-bucket/customer-churn-data.csv',
    wait=False,  # Run asynchronously
    logs=False
)

# Deploy best model
predictor = automl.deploy(
    initial_instance_count=1,
    instance_type='ml.m5.xlarge'
)

Cost: 2-5x more expensive than training a single model, but eliminates weeks of experimentation.

Real-World Decision Scenarios

Scenario 1: E-commerce Product Recommendation

Requirements:

• Recommend products to users based on browsing/purchase history
• 10M users, 100K products
• Real-time recommendations (<200ms latency)

Decision process:

Recommendation: Use Amazon Personalize. Only consider SageMaker if accuracy testing shows Personalize doesn’t meet business KPIs.

Scenario 2: Medical Image Diagnosis (X-ray Analysis)

Requirements:

• Detect pneumonia from chest X-rays
• HIPAA compliance required
• High accuracy needed (>98%)
• Proprietary hospital dataset

Decision process:

Recommendation: Use SageMaker. Medical diagnosis requires custom models trained on domain-specific data, but DIY offers no advantage over SageMaker’s managed infrastructure.

Scenario 3: Customer Support Call Sentiment Analysis

Requirements:

• Analyze sentiment from 10,000 support call transcripts/month
• Identify negative sentiment for manager escalation
• Budget: <$5K/month

Decision process:

Recommendation: Start with Comprehend. If accuracy testing shows <85% accuracy, consider SageMaker with fine-tuned BERT model.

Scenario 4: Real-Time Fraud Detection (Financial Transactions)

Requirements:

• Detect fraudulent transactions in real-time (<50ms latency)
• Proprietary fraud patterns (credit card, account takeover)
• 1M transactions/day
• Accuracy critical (false positives cost revenue)

Decision process:

Recommendation: Use SageMaker. Fraud detection requires custom models, but SageMaker’s managed endpoints meet latency requirements without DIY complexity.

Scenario 5: Document Processing Pipeline (Invoices, Receipts)

Requirements:

• Extract text, tables, and key-value pairs from invoices
• Process 50,000 documents/month
• Integrate with existing workflow automation

Decision process:

Recommendation: Use Textract. Only consider SageMaker if you need extremely specialized document types not handled by Textract.

Cost Optimization Strategies by Tier

AI Services Cost Optimization

1. Batch processing over real-time:

# Instead of real-time per-image processing
for image in images:
    rekognition.detect_labels(Image=image)  # 1 API call per image

# Use batch operations
rekognition.start_label_detection(
    Video={'S3Object': {'Bucket': 'my-bucket', 'Name': 'video.mp4'}}
)
# Process entire video in single job

2. Cache results for frequently analyzed content:

import hashlib

def get_or_analyze_sentiment(text):
    cache_key = hashlib.sha256(text.encode()).hexdigest()

    # Check cache (DynamoDB, Redis, etc.)
    cached = dynamodb.get_item(Key={'text_hash': cache_key})
    if cached:
        return cached['sentiment']

    # Analyze and cache
    sentiment = comprehend.detect_sentiment(Text=text, LanguageCode='en')
    dynamodb.put_item(Item={'text_hash': cache_key, 'sentiment': sentiment})
    return sentiment

3. Pre-filter before expensive operations:

# Don't send every image to Rekognition for moderation
# Use cheaper heuristics first (file size, metadata, content type)

if image_size < 10KB:
    return {'safe': True}  # Too small to contain inappropriate content

# Only analyze suspicious images
response = rekognition.detect_moderation_labels(Image=image)

SageMaker Cost Optimization

1. Use Spot Instances for training (70% savings):

estimator = PyTorch(
    entry_point='train.py',
    instance_type='ml.p3.2xlarge',
    instance_count=4,
    use_spot_instances=True,  # 70% savings
    max_wait=7200,  # Wait up to 2 hours for spot capacity
    checkpoint_s3_uri='s3://my-bucket/checkpoints/'  # Save progress
)

2. Right-size inference endpoints:

# Start with smallest instance that meets latency requirements
predictor = estimator.deploy(
    initial_instance_count=1,
    instance_type='ml.t2.medium'  # $0.065/hour vs ml.p2.xlarge at $1.26/hour
)

# Use autoscaling for variable load
from sagemaker.predictor import Predictor

predictor = Predictor(endpoint_name='my-endpoint')

predictor.update_endpoint(
    initial_instance_count=1,
    instance_type='ml.t2.medium',
    variant_name='AllTraffic',
    endpoint_config_name='my-config'
)

# Configure autoscaling
client = boto3.client('application-autoscaling')
client.register_scalable_target(
    ServiceNamespace='sagemaker',
    ResourceId=f'endpoint/{predictor.endpoint_name}/variant/AllTraffic',
    ScalableDimension='sagemaker:variant:DesiredInstanceCount',
    MinCapacity=1,
    MaxCapacity=5
)

3. Use Serverless Inference for intermittent traffic:

from sagemaker.serverless import ServerlessInferenceConfig

serverless_config = ServerlessInferenceConfig(
    memory_size_in_mb=2048,  # 1GB, 2GB, 4GB, or 6GB
    max_concurrency=10  # Max concurrent invocations
)

predictor = estimator.deploy(
    serverless_inference_config=serverless_config
)
# Pay only when invoked ($0.20 per 1M requests + $0.0000133 per second)

4. Use Asynchronous Inference for batch jobs:

from sagemaker.async_inference import AsyncInferenceConfig

async_config = AsyncInferenceConfig(
    output_path='s3://my-bucket/async-output/',
    max_concurrent_invocations_per_instance=10
)

predictor = estimator.deploy(
    instance_type='ml.m5.xlarge',
    initial_instance_count=1,
    async_inference_config=async_config
)
# Scale to zero when idle, process large batches efficiently

5. Shut down idle resources:

# Delete endpoints when not in use
sagemaker_client = boto3.client('sagemaker')

# List all endpoints
endpoints = sagemaker_client.list_endpoints()

for endpoint in endpoints['Endpoints']:
    # Check last invocation time (via CloudWatch metrics)
    metrics = cloudwatch.get_metric_statistics(
        Namespace='AWS/SageMaker',
        MetricName='ModelLatency',
        Dimensions=[{'Name': 'EndpointName', 'Value': endpoint['EndpointName']}],
        StartTime=datetime.now() - timedelta(days=7),
        EndTime=datetime.now(),
        Period=86400,
        Statistics=['SampleCount']
    )

    # Delete if no invocations in 7 days
    if not metrics['Datapoints']:
        sagemaker_client.delete_endpoint(EndpointName=endpoint['EndpointName'])
        print(f"Deleted idle endpoint: {endpoint['EndpointName']}")

DIY Cost Optimization

1. Use EC2 Spot for training:

• 70-90% savings over on-demand
• Requires checkpointing and fault tolerance

2. Use Lambda for low-volume inference:

import json
import boto3
import torch

# Load model in Lambda (store in /tmp or EFS)
def lambda_handler(event, context):
    model = torch.load('/tmp/model.pth')
    input_data = json.loads(event['body'])

    prediction = model(input_data)

    return {
        'statusCode': 200,
        'body': json.dumps({'prediction': prediction.tolist()})
    }

# Cost: $0.20 per 1M requests (vs $50+/month for always-on endpoint)

3. Use ECS/Fargate with autoscaling:

• Scale to zero during off-hours
• Right-size container resources (CPU/memory)

When to Move Between Tiers

Moving from AI Services → SageMaker

Triggers:

1. Accuracy insufficient: AI Service achieves <85% accuracy on your data
2. Latency too high: Need <100ms p99 latency (AI Services typically 200-500ms)
3. Customization needed: Require domain-specific features or model architecture
4. Cost at scale: AI Service pricing exceeds SageMaker at your volume (usually >10M requests/month)
5. Compliance: Need model interpretability, audit trails, or data residency control

Migration example (Comprehend → SageMaker):

# Phase 1: AI Service baseline
comprehend = boto3.client('comprehend')
sentiment = comprehend.detect_sentiment(Text=text, LanguageCode='en')

# Accuracy test: 87% (need 92%)

# Phase 2: Migrate to SageMaker with fine-tuned BERT
from sagemaker.huggingface import HuggingFace

estimator = HuggingFace(
    entry_point='train_sentiment.py',
    role=sagemaker_role,
    instance_type='ml.p3.2xlarge',
    transformers_version='4.17',
    pytorch_version='1.10',
    hyperparameters={
        'epochs': 5,
        'model_name': 'bert-base-uncased',
        'learning_rate': 2e-5
    }
)

# Train on your labeled data
estimator.fit({'train': 's3://my-bucket/sentiment-training/'})

# Deploy
predictor = estimator.deploy(
    initial_instance_count=1,
    instance_type='ml.t2.medium'
)

# Accuracy test: 93% ✅

Moving from SageMaker → DIY

Triggers (rare):

1. Extreme scale: Processing >1B predictions/day where SageMaker costs exceed DIY + ops
2. Specialized hardware: Need custom GPUs, TPUs, or edge devices not supported by SageMaker
3. Ultra-low latency: Need <10ms latency with custom optimizations
4. Existing ML platform: Already invested in Kubeflow, MLflow, or custom infrastructure

Warning: DIY rarely makes financial sense unless you have:

• Dedicated ML infrastructure team (5+ engineers)
• Volume exceeding $50K/month in SageMaker costs
• Highly specialized requirements SageMaker cannot meet

Staying in AI Services (When NOT to Move)

Keep using AI Services when:

• Accuracy is “good enough” (>85% for most business cases)
• Cost is <$10K/month
• Latency is acceptable (200-500ms for most applications)
• You lack ML expertise
• Time-to-market is critical (weeks, not months)

Example: Many companies stay on Comprehend sentiment analysis even at 87% accuracy because:

• 92% accuracy from custom models costs $100K+ to achieve
• Business impact of 5% accuracy gain is <$20K/year
• ROI is negative

Common Pitfalls

Pitfall 1: Premature Optimization (Building Custom Models Too Early)

Problem: Teams build custom SageMaker models before testing AI Services.

Example:

# ❌ BAD: Jump straight to SageMaker
estimator = HuggingFace(entry_point='train.py', ...)  # 3 months of work

# ✅ GOOD: Test AI Service first
comprehend = boto3.client('comprehend')
sentiment = comprehend.detect_sentiment(Text=text, LanguageCode='en')

# Test accuracy with labeled data
# Only build custom model if AI Service accuracy insufficient

Solution: Always prototype with AI Services first. Migrate to SageMaker only after validating that simpler solutions fail.

Pitfall 2: Over-Engineering (DIY When SageMaker Suffices)

Problem: Teams build custom ML infrastructure when SageMaker provides same capabilities.

Example:

# ❌ BAD: Custom infrastructure
# - Set up Kubernetes cluster
# - Deploy MLflow
# - Build training pipelines
# - Manage model registry
# - Implement autoscaling
# - Monitor infrastructure
# (6+ months, 5 engineers)

# ✅ GOOD: Use SageMaker
estimator.fit(...)
predictor = estimator.deploy(...)
# (2 weeks, 1 data scientist)

Solution: Only build DIY infrastructure if you have requirements SageMaker cannot meet (which is rare).

Pitfall 3: Not Testing AI Service Accuracy

Problem: Assuming AI Services won’t work without testing on your data.

Solution:

# Test AI Service accuracy systematically
import pandas as pd
from sklearn.metrics import accuracy_score

# Load labeled test data
test_data = pd.read_csv('labeled_reviews.csv')

# Get predictions from AI Service
predictions = []
for text in test_data['review_text']:
    response = comprehend.detect_sentiment(Text=text, LanguageCode='en')
    predictions.append(response['Sentiment'])

# Calculate accuracy
accuracy = accuracy_score(test_data['true_sentiment'], predictions)
print(f"Comprehend accuracy: {accuracy:.2%}")

# Decision: Use Comprehend if accuracy >85%, else SageMaker

Pitfall 4: Ignoring Total Cost of Ownership

Problem: Comparing only service costs, ignoring dev and ops costs.

Correct TCO calculation:

# AI Service TCO
ai_service_tco = (
    service_cost_per_month * 12 +  # $100 × 12 = $1,200
    integration_dev_cost +           # $5,000 (2 weeks)
    ops_cost_per_month * 12          # $0 × 12 = $0 (fully managed)
)
# Total: $6,200 for 12 months

# SageMaker TCO
sagemaker_tco = (
    sagemaker_cost_per_month * 12 +  # $2,000 × 12 = $24,000
    model_dev_cost +                  # $50,000 (3 months)
    ops_cost_per_month * 12           # $5,000 × 12 = $60,000 (retraining, monitoring)
)
# Total: $134,000 for 12 months

# Decision: Use AI Service unless business value gain exceeds $127,800/year

Pitfall 5: Not Leveraging Hybrid Approaches

Problem: Thinking you must use one tier exclusively.

Solution: Mix AI Services and SageMaker based on task requirements.

# ✅ GOOD: Hybrid approach
def process_support_ticket(ticket_text):
    # AI Service: PII detection
    pii_entities = comprehend.detect_pii_entities(Text=ticket_text, LanguageCode='en')
    redacted_text = redact_pii(ticket_text, pii_entities)

    # SageMaker: Custom intent classification (company-specific)
    intent = sagemaker_intent_predictor.predict(redacted_text)

    # AI Service: Sentiment analysis
    sentiment = comprehend.detect_sentiment(Text=redacted_text, LanguageCode='en')

    return {
        'intent': intent,
        'sentiment': sentiment['Sentiment'],
        'urgency': determine_urgency(intent, sentiment)
    }

Quick Reference Decision Tree

START: Do you need machine learning?
  │
  ├─ Yes → Does your use case match an AI Service?
  │         (vision, language, speech, forecasting, recommendations)
  │         │
  │         ├─ Yes → Test AI Service accuracy on your data
  │         │         │
  │         │         ├─ Accuracy >85% → ✅ USE AI SERVICE
  │         │         │
  │         │         └─ Accuracy <85% → Need custom model
  │         │                            │
  │         │                            ├─ Have ML team? → ✅ USE SAGEMAKER
  │         │                            │
  │         │                            └─ No ML team → ⚠️ HIRE or USE SAGEMAKER AUTOPILOT
  │         │
  │         └─ No → Need custom model
  │                  │
  │                  ├─ Standard algorithm works?
  │                  │  (classification, regression, clustering)
  │                  │  │
  │                  │  ├─ Yes → ✅ USE SAGEMAKER BUILT-IN ALGORITHMS
  │                  │  │
  │                  │  └─ No → ✅ USE SAGEMAKER CUSTOM MODEL
  │                  │
  │                  └─ Extreme requirements?
  │                     (custom hardware, >1B predictions/day, <10ms latency)
  │                     │
  │                     ├─ Yes + have ML infra team → ⚠️ CONSIDER DIY
  │                     │
  │                     └─ No → ✅ USE SAGEMAKER
  │
  └─ No → Don't use ML (use rules/heuristics)

Key Takeaways

Decision Framework:

1. Always start with AI Services for common use cases (vision, language, speech)
2. Move to SageMaker when you need custom models, domain-specific features, or accuracy >95%
3. Consider DIY only when SageMaker cannot meet specialized requirements (extremely rare)
4. Use hybrid approaches combining AI Services (80% of tasks) with SageMaker (20% specialized)

Cost Optimization:

1. Calculate Total Cost of Ownership (TCO), not just service costs (include dev and ops)
2. AI Services typically cost $100-5K/month with near-zero ops burden
3. SageMaker typically costs $1K-10K/month plus dev ($30K-100K) and ops ($5K-20K/month)
4. DIY rarely justified unless ML infrastructure team exists and scale exceeds $50K/month

Testing & Validation:

1. Test AI Service accuracy on your data before building custom models
2. Accuracy threshold: Use AI Services if >85%, consider SageMaker if <85%
3. Validate business ROI before migrating to higher tiers (does 5% accuracy gain justify $100K cost?)

Team & Expertise:

1. No ML expertise: Use AI Services exclusively
2. Data science team: Use SageMaker for custom use cases
3. Full ML engineering team: Use SageMaker (DIY offers minimal advantage)

Service Selection:

1. Use AI Services for: sentiment analysis, image classification, translation, transcription, OCR, forecasting, recommendations
2. Use SageMaker for: fraud detection, medical diagnosis, custom NLP, domain-specific vision, proprietary recommendation engines
3. Use DIY for: extremely specialized requirements SageMaker cannot meet (edge cases only)

Migration Strategy:

1. Progressive migration: Start with AI Services → validate → migrate to SageMaker only if needed
2. Avoid premature optimization: Don’t build custom models before testing simpler solutions
3. Monitor and iterate: Continuously evaluate whether your tier choice still makes sense as requirements evolve

The simplest solution that meets requirements is always the best choice. Move to higher tiers only when business value clearly justifies the increased complexity and cost.