Moving from OpenAI/Azure to AWS? Here's What Actually Works

Why Teams Actually Migrate (Spoiler:

It's Not Strategy)

Here's the truth: Nobody migrates for fun.

Our OpenAI bill hit $47k/month and accounting started asking uncomfortable questions. Azure ML kept crashing our training jobs

• 3 times in one week during a demo to investors. Google shut down another product we used (surprise!).

That's why you're here reading this instead of shipping features.

The Real AWS Migration Experience

I'm writing this after helping Acme Corp migrate from OpenAI to Bedrock. Took 8 months instead of the promised 3. Cost $180k in engineering time. But we did cut our inference costs by 60% and haven't had a single outage since.

What Actually Forces Migration

OpenAI Bill Shock: Started at $300/month for our prototype.

Hit $47k when we scaled. Finance team wasn't amused. AWS Bedrock pricing is clearer and about 40% cheaper for Claude models.

The OpenAI pricing model gets expensive fast at scale, especially with GPT-4 API costs.

Azure ML Reliability Problems: Our training pipelines failed every few weeks.

No good error messages. Support tickets took 5 days. We spent more time debugging Azure Machine Learning than training models.

The Azure ML service outages happened monthly and Azure support response times are inconsistent.

Google Product Anxiety: They killed AI Platform Notebooks.

Then deprecated ML Engine.

Check the Google Graveyard

• they've killed 200+ products. AWS service longevity has killed exactly zero major ML services in 10 years.

Vendor Lock-In Reality: Try switching from OpenAI's fine-tuned models to anything else.

Good luck. At least with AWS you can run the same models locally if needed.

AWS Services That Actually Matter (September 2025)

Forget the marketing bullshit. Here's what you'll actually use:

Bedrock (The Models):

• Claude 3.5 Sonnet
• Best for complex reasoning, costs ~$15/1M input tokens
• Nova Pro
• Amazon's model, decent quality at $8/1M tokens
• Llama 3
• Open source option, cheapest at $2.65/1M tokens

SageMaker (The Platform):

• Real-time inference endpoints (what you'll use most)
• Training jobs with spot instances (saves 70% on training costs)
• Pipelines if you hate yourself and love complexity

Basic AI Services:

• Rekognition for image tagging (actually works well)
• Textract for OCR (better than Google's Cloud Vision)
• Comprehend for sentiment (meh, just use a model)

Skip the rest unless you have specific needs.

Migration Timelines That Won't Get You Fired

Simple API Switch (OpenAI → Bedrock): 6-8 weeks

• 2 weeks IAM setup and credential hell
• 2 weeks rewriting API calls and error handling
• 2 weeks testing and prompt optimization
• 2 weeks for the inevitable fuckups you didn't plan for

ML Platform Migration (Azure ML → SageMaker): 4-6 months

• 4 weeks learning SageMaker (it's different from everything else)
• 8 weeks rebuilding training pipelines
• 6 weeks data migration and testing
• 6 weeks fixing everything that breaks in production

Complex Multi-Platform: 6-12 months Just don't.

Migrate one thing at a time or you'll lose your sanity.

Migration Architecture Overview

The Shit Nobody Tells You About

IAM Permissions Are Hell: You'll spend 2 weeks minimum just figuring out who can access what.

The error messages are useless:

AccessDenied:

 User is not authorized to perform bedrock:InvokeModel

Translation:

You're missing one of 12 different permissions and we won't tell you which one.

Service Quotas Are Pathetic: Bedrock starts with 10 requests per minute.

Production traffic? That's cute. Submit increase requests immediately

• they take 3-5 business days.

Regional Availability Sucks: Want Claude 3.5 Sonnet?

Only available in us-east-1 and us-west-2. Hope your compliance team is flexible.

Billing Surprises: Data transfer costs add up fast.

That 100GB model you're downloading costs $9 each time. Nobody mentions this until the bill arrives.

What Actually Saves Money

Bedrock vs OpenAI:

• Claude 3.5 Sonnet: ~$15/1M tokens vs OpenAI's $30/1M for GPT-4
• Nova Pro: ~$8/1M tokens, quality is 85% of GPT-4
• You'll save 40-60% on inference costs if you optimize prompts

SageMaker Reality Check:

• Training costs: 70% cheaper with spot instances (when they don't get interrupted)
• Real-time inference:

Actually expensive

• $50-200/month per endpoint
• Batch inference: Much cheaper option if you can wait

Hidden Costs That'll Bite You:

• Data transfer: $0.09/GB out of AWS
• Storage:

Model artifacts add up over months

• CloudWatch logs: Can exceed compute costs if you're not careful

The Migration That Nearly Got Me Fired

Company decided to migrate everything from Azure to AWS in 90 days.

I said 6 months minimum. They went with 90 days.

Week 6: IAM still not working, dev team can't access anything
Week 10:

Training jobs failing, no idea why Week 12: Emergency meeting with CEO, demo completely broken

Ended up running both platforms for 5 months at double the cost.

Finally worked after month 7. CEO wasn't happy but we shipped working software.

Lesson: Never promise heroic timelines.

Always run parallel systems longer than you think.

When NOT to Migrate

Don't migrate if:

• You're spending less than $5k/month on current platform
• Your OpenAI integration is just basic API calls with no custom models
• Your team has never used AWS (learning curve is brutal)
• You don't have 3+ months of engineering bandwidth

Especially don't migrate if:

• Your current system actually works well
• You're under deadline pressure
• You think it'll be "easy" (narrator: it wasn't easy)

Migration is a 6-month commitment minimum. If you can't commit to that, don't start.

Ready to dive into the specifics? The next section breaks down exactly what each migration path looks like

• including the real timelines and costs that'll actually happen (not the marketing bullshit).

Step 1: Figure Out What You Actually Have (2-4 weeks)

Audit Your Current Shit

First, find all the AI/ML code hiding in your codebase. It's probably scattered everywhere because nobody documented it properly.

## Find all OpenAI usage (this will take longer than you think)
grep -r \"openai\" . --include=\"*.py\" --include=\"*.js\" --include=\"*.env\" --include=\"*.yaml\"
grep -r \"sk-\" . --include=\"*.env\"  # API keys
grep -r \"anthropic\" . --include=\"*.py\"

What you'll actually find:

• 47 different files using OpenAI APIs in slightly different ways
• Hardcoded API keys in files (because someone was "just testing")
• Three different versions of the OpenAI Python library
• Custom wrapper functions nobody remembers writing
• Environment variables scattered across .env files

Get Your Baseline Numbers

You need this data or you'll have no idea if AWS is actually better:

Current costs (get actual numbers):

• Monthly OpenAI bill: $____
• Azure ML costs: $____
• GPU instance costs: $____
• Data transfer fees: $____

Performance metrics:

• Average response time: ____ ms
• P95 response time: ____ ms
• Error rate: ____%
• Requests per day: ____

Pro tip: Your current error rate is probably higher than you think. Enable detailed logging for 2 weeks before migration starts.

Map Dependencies (This Always Takes Longer)

List every system that uses your AI/ML services. Include:

• Customer-facing applications
• Internal tools and dashboards
• Data pipelines
• Scheduled jobs and cron tasks
• That thing Sarah built last year that nobody understands

Reality check: You'll find 3x more dependencies than expected. Someone's Excel macro is probably calling your AI API.

Step 2: Build AWS Version While Old System Runs (4-8 weeks)

Set Up AWS Infrastructure (And Try Not to Cry)

IAM Policy That Actually Works:

{
  \"Version\": \"2012-10-17\",
  \"Statement\": [
    {
      \"Effect\": \"Allow\",
      \"Action\": [
        \"bedrock:InvokeModel\",
        \"bedrock:InvokeModelWithResponseStream\",
        \"bedrock:ListFoundationModels\"
      ],
      \"Resource\": \"*\"
    },
    {
      \"Effect\": \"Allow\", 
      \"Action\": [
        \"sagemaker:InvokeEndpoint\",
        \"sagemaker:DescribeEndpoint\"
      ],
      \"Resource\": \"*\"
    }
  ]
}

Don't try to be secure on day one. Get it working first, then lock it down.

Billing Alerts (Set These Up First):

## This will save your job when someone leaves a GPU instance running
import boto3

def setup_oh_shit_billing_alerts():
    cloudwatch = boto3.client('cloudwatch')
    
    # Alert at multiple thresholds because AWS bills escalate fast
    thresholds = [500, 1000, 2500, 5000]
    
    for threshold in thresholds:
        cloudwatch.put_metric_alarm(
            AlarmName=f'AI-Costs-Alert-{threshold}',
            ComparisonOperator='GreaterThanThreshold',
            EvaluationPeriods=1,
            MetricName='EstimatedCharges',
            Namespace='AWS/Billing',
            Period=86400,  # Daily check
            Statistic='Maximum',
            Threshold=threshold,
            AlarmDescription=f'OH SHIT: AI costs hit ${threshold}'
        )

Build a Proxy Layer (Save Your Sanity)

Don't rewrite everything at once. Build a proxy that can route to either system:

class MigrationProxy:
    def __init__(self):
        self.use_aws = os.getenv('USE_AWS_PERCENT', 0)  # Start at 0%
        self.openai_client = openai.OpenAI()
        self.bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')
    
    def chat_completion(self, messages, model=\"gpt-4\"):
        # Route percentage of traffic to AWS
        if random.randint(1, 100) <= int(self.use_aws):
            return self._call_bedrock(messages)
        else:
            return self._call_openai(messages, model)
    
    def _call_bedrock(self, messages):
        try:
            response = self.bedrock.invoke_model(
                modelId='anthropic.claude-3-5-sonnet-20240620-v1:0',
                body=json.dumps({
                    \"anthropic_version\": \"bedrock-2023-05-31\",
                    \"max_tokens\": 4000,
                    \"messages\": messages
                })
            )
            return json.loads(response['body'].read())
        except Exception as e:
            # Log error and fallback to OpenAI
            logger.error(f\"Bedrock failed: {e}\")
            return self._call_openai(messages, \"gpt-4\")

This proxy pattern saved my ass multiple times. It lets you:

• Route 5% traffic to AWS while debugging
• Automatically fallback to OpenAI when AWS breaks
• Compare response quality side-by-side
• Roll back instantly when things go wrong

Test Everything (Seriously, Everything)

A/B Test Framework:

def compare_model_outputs():
    test_prompts = load_test_prompts()  # Use real production prompts
    
    for prompt in test_prompts:
        openai_response = call_openai(prompt)
        bedrock_response = call_bedrock(prompt)
        
        # Compare outputs
        similarity = calculate_similarity(openai_response, bedrock_response)
        quality_score = human_eval_quality(prompt, bedrock_response)
        
        log_comparison({
            'prompt': prompt,
            'openai_tokens': count_tokens(openai_response),
            'bedrock_tokens': count_tokens(bedrock_response),
            'similarity': similarity,
            'quality': quality_score
        })

Important: Token counting is different between providers. Same prompt might cost 20% more or less on AWS.

Step 3: The Scary Part - Actually Switching (2-6 weeks)

Gradual Rollout (Don't Be a Hero)

Week 1: 5% traffic to AWS
Week 2: 10% if no major issues
Week 3: 25% if still looking good
Week 4: 50% (this is where things usually break)
Week 5: 75% if you're feeling brave
Week 6: 100% or rollback if problems

Monitoring That Actually Helps:

def setup_migration_monitoring():
    # Track what matters during migration
    metrics_to_track = [
        'response_time_p95',
        'error_rate',
        'cost_per_request', 
        'user_satisfaction_score',
        'rollback_triggered_count'
    ]
    
    # Alert on everything during migration
    for metric in metrics_to_track:
        setup_alert(metric, threshold='conservative')

When to Hit the Panic Button

Automatic rollback triggers:

def check_if_we_are_fucked():
    current_error_rate = get_error_rate_last_hour()
    baseline_error_rate = get_baseline_error_rate()
    
    # Rollback if error rate doubles
    if current_error_rate > baseline_error_rate * 2:
        trigger_rollback(\"Error rate spike\")
        
    # Rollback if responses are too slow  
    p95_response_time = get_p95_response_time_last_hour()
    if p95_response_time > 10000:  # 10 seconds is too slow
        trigger_rollback(\"Response time too high\")
        
    # Rollback if costs are insane
    hourly_cost = get_hourly_cost()
    if hourly_cost > expected_hourly_cost * 3:
        trigger_rollback(\"Costs spiraling out of control\")

def trigger_rollback(reason):
    logger.critical(f\"ROLLBACK TRIGGERED: {reason}\")
    # Route 100% traffic back to old system
    os.environ['USE_AWS_PERCENT'] = '0'
    send_slack_alert(f\"AWS migration rolled back: {reason}\")

Performance Optimization (Post-Migration)

After you're fully on AWS and things aren't on fire:

## Cache responses for repeated queries (saves 70% on costs)
@lru_cache(maxsize=1000)
def cached_bedrock_call(prompt_hash):
    return bedrock.invoke_model(...)

## Use batch inference for non-real-time requests
def batch_inference(prompts):
    # Process 100 prompts at once instead of 1-by-1
    return sagemaker.invoke_endpoint_async(...)

## Spot instances for training (70% cost savings)
def start_training_job():
    sagemaker.create_training_job(
        TrainingJobName='my-model-training',
        InstanceType='ml.p3.8xlarge',
        UseSpotInstances=True,  # This line saves thousands
        MaxRuntimeInSeconds=3600
    )

The Reality of Migration Timelines

What management thinks:

• Week 1-2: Assessment
• Week 3-4: Implementation
• Week 5-6: Testing and rollout

What actually happens:

• Week 1-3: Assessment (finding all the hidden dependencies)
• Week 4-7: IAM debugging and basic setup
• Week 8-12: Implementation and first round of testing
• Week 13-16: Fixing everything that broke in testing
• Week 17-20: Gradual rollout
• Week 21-24: Fixing production issues
• Week 25+: Performance optimization

Lesson: Always triple your initial estimate. If you think it'll take 2 months, tell management 6 months.

War Stories from the Trenches

The $8,000 Weekend:
Left a SageMaker training job running with the wrong instance type. Burned through $8k in compute costs training a model that took 30 minutes locally. Always set MaxRuntimeInSeconds.

The Great Rollback of Q3:
Migrated our customer chatbot to Bedrock. Response quality was great in testing. In production, Claude started being overly formal with customers ("I would be delighted to assist you with your banking inquiry, esteemed patron"). Had to rollback after customer complaints.

The IAM Disaster:
Spent 3 weeks debugging "AccessDenied" errors. Turns out one IAM policy had a typo in the resource ARN. The error messages don't tell you which specific resource is denied. Pro tip: Use "*" for resources during initial setup.

Bottom Line: Migration sucks, but running out of money from OpenAI bills sucks more. Budget 6 months, expect setbacks, and always have a rollback plan.

Month 1-3: The "Oh Shit, Our Bill is Still High" Phase

So you've migrated to AWS. Congratulations! Your bills are probably still higher than expected and half your team is confused about why SageMaker exists. This is normal.

Week 1: Stop the Bleeding

First Priority: Fix the Obvious Money Drains

Your AWS bill is probably 2x what you expected. Here's what's happening:

## This is burning money right now
def fix_immediate_cost_drains():
    # 1. Turn off idle SageMaker endpoints (they cost $200/month even with zero traffic)
    sagemaker = boto3.client('sagemaker')
    endpoints = sagemaker.list_endpoints()
    
    for endpoint in endpoints['Endpoints']:
        if endpoint_has_zero_traffic(endpoint):
            print(f"Deleting idle endpoint: {endpoint['EndpointName']}")
            # This will save you $200+/month per endpoint
            sagemaker.delete_endpoint(EndpointName=endpoint['EndpointName'])
    
    # 2. Right-size your instances (most teams over-provision by 50%)
    # Check CloudWatch metrics and downgrade everything possible
    
    # 3. Turn on spot instances for training (70% savings)
    # This should have been done during migration but probably wasn't

The "Keep-Warm" Problem:

Bedrock models go cold after 5 minutes. First request takes 10-15 seconds. This is infuriating for production apps.

## Ugly but necessary hack to keep models warm
import threading
import time

def keep_bedrock_warm():
    """Pings Bedrock every 4 minutes to prevent cold starts"""
    while True:
        try:
            # Cheapest possible request (1 token)
            bedrock.invoke_model(
                modelId='anthropic.claude-3-haiku-20240307-v1:0',
                body=json.dumps({
                    "anthropic_version": "bedrock-2023-05-31", 
                    "max_tokens": 1,
                    "messages": [{"role": "user", "content": "hi"}]
                })
            )
            time.sleep(240)  # 4 minutes
        except:
            time.sleep(60)  # Retry in 1 minute if it fails

## Start the keep-warm thread (costs ~$5/month, saves user experience)
threading.Thread(target=keep_bedrock_warm, daemon=True).start()

Month 2-3: Actually Optimizing Shit

Model Selection Based on Real Usage:

Your prompt testing probably didn't capture production reality. Time to optimize based on actual data.

## Real optimization based on production metrics
class ModelOptimizer:
    def __init__(self):
        self.cost_tracker = {}
        self.quality_scores = {}
    
    def log_request(self, model, prompt_length, response_quality, cost):
        if model not in self.cost_tracker:
            self.cost_tracker[model] = []
            self.quality_scores[model] = []
        
        self.cost_tracker[model].append(cost)
        self.quality_scores[model].append(response_quality)
    
    def get_recommendations(self):
        """After 30 days, this tells you which models to use when"""
        recommendations = {}
        
        for model in self.cost_tracker:
            avg_cost = sum(self.cost_tracker[model]) / len(self.cost_tracker[model])
            avg_quality = sum(self.quality_scores[model]) / len(self.quality_scores[model])
            
            recommendations[model] = {
                'cost_per_request': avg_cost,
                'quality_score': avg_quality,
                'value_ratio': avg_quality / avg_cost
            }
        
        return recommendations

Reality Check: What Models Actually Cost

After 3 months of production usage, here's what you'll discover:

• Nova Lite: Great for simple tasks, terrible for anything complex
• Nova Pro: Good balance for most use cases, 60% cheaper than Claude
• Claude 3.5: Expensive but worth it for complex reasoning
• Claude Haiku: Fast and cheap, perfect for data extraction

Batch Processing Everything You Can:

Real-time inference is expensive. Batch processing is 70% cheaper.

## Convert real-time to batch where possible
def optimize_for_batch_processing():
    # Instead of processing 1000 documents one at a time
    # Process them in batches of 100
    
    # Cost difference:
    # Real-time: $0.02 per request × 1000 = $20
    # Batch: $0.006 per request × 1000 = $6
    # Savings: 70%
    
    # The catch: Results take 5-10 minutes instead of 1 second
    # Good for: Reports, data processing, analytics
    # Bad for: User-facing features, real-time responses

Month 4-6: The "We're Finally Getting Good at This" Phase

Advanced Cost Optimization

Request Caching (The Game Changer):

from functools import lru_cache
import hashlib

## Cache responses for repeated prompts (huge savings for document processing)
@lru_cache(maxsize=1000)
def cached_bedrock_call(prompt_hash):
    # This cache will save you 60-80% on document analysis costs
    # Same documents get processed multiple times
    return bedrock.invoke_model(...)

def smart_caching_wrapper(prompt):
    # Hash the prompt for caching
    prompt_hash = hashlib.md5(prompt.encode()).hexdigest()
    
    # Check if we've seen this exact prompt before
    if prompt_hash in cache:
        return cache[prompt_hash]
    
    # New prompt, make the API call
    response = bedrock.invoke_model(...)
    cache[prompt_hash] = response
    return response

Regional Cost Arbitrage:

us-east-1 is the most expensive region. us-west-2 is 20-30% cheaper for most services.

## Deploy in cheaper regions when possible
regions_by_cost = {
    'us-east-1': 1.0,    # Most expensive
    'us-west-2': 0.8,    # 20% cheaper
    'eu-west-1': 0.85,   # 15% cheaper
    'ap-southeast-1': 0.9  # 10% cheaper
}

## For non-latency-sensitive workloads, use cheaper regions
def get_optimal_region(workload_type):
    if workload_type == 'real_time':
        return closest_region_to_users()
    else:
        return 'us-west-2'  # Cheapest option with full service availability

Quality Optimization That Actually Matters

Prompt Engineering for Each Model:

Different models respond to different prompt styles. Optimize separately.

## Model-specific prompt optimizations discovered through production usage
def optimize_prompt_by_model(base_prompt, model):
    if model == 'nova-pro':
        # Nova Pro likes structured prompts with clear sections
        return f"""
Task: {extract_task(base_prompt)}
Context: {extract_context(base_prompt)}
Requirements:
1. Be specific and actionable
2. Provide examples when possible
3. Structure your response clearly
Response:"""
    
    elif model == 'claude-3-5-sonnet':
        # Claude likes conversational prompts with context
        return f"Here's what I need help with: {base_prompt}

Please provide a detailed response."
    
    elif model == 'nova-lite':
        # Nova Lite needs very simple, direct prompts
        return base_prompt.split('.')[0] + '?'  # Just the main question
    
    return base_prompt

A/B Testing Framework:

## Simple A/B testing to optimize model selection
import random

class ModelABTesting:
    def __init__(self):
        self.results = []
    
    def get_model_for_request(self, user_id, prompt_complexity):
        # Route based on prompt complexity and cost sensitivity
        if prompt_complexity == 'simple':
            return random.choice(['nova-lite', 'nova-pro'])  # 50/50 split
        elif prompt_complexity == 'medium':
            return random.choice(['nova-pro', 'claude-haiku'])  # 50/50 split
        else:  # complex
            return 'claude-3-5-sonnet'  # Use the best model
    
    def log_result(self, model, quality_score, cost, response_time):
        self.results.append({
            'model': model,
            'quality': quality_score,
            'cost': cost,
            'time': response_time
        })
    
    def analyze_results(self):
        # After 1000+ requests, shows which models work best for what
        by_model = {}
        for result in self.results:
            if result['model'] not in by_model:
                by_model[result['model']] = []
            by_model[result['model']].append(result)
        
        for model, results in by_model.items():
            avg_quality = sum(r['quality'] for r in results) / len(results)
            avg_cost = sum(r['cost'] for r in results) / len(results)
            print(f"{model}: Quality {avg_quality:.2f}, Cost ${avg_cost:.4f}")

Month 6-12: You're Actually Good at This Now

Advanced AWS Features You Can Finally Use

Multi-Model Endpoints (For Multiple Small Models):

## Instead of running 5 separate endpoints at $200/month each
## Run 1 multi-model endpoint for $200/month total
## Savings: $800/month

sagemaker = boto3.client('sagemaker')

def deploy_multi_model_endpoint():
    # This is complex but saves serious money for multiple models
    sagemaker.create_endpoint_config(
        EndpointConfigName='multi-model-config',
        ProductionVariants=[{
            'VariantName': 'multi-model',
            'ModelName': 'multi-model',
            'InitialInstanceCount': 1,
            'InstanceType': 'ml.m5.large',
            'InitialVariantWeight': 1
        }]
    )

Automated Cost Optimization:

## Automatically optimize costs based on usage patterns
def auto_optimize_costs():
    # Analyze last 30 days of usage
    usage_data = get_usage_metrics()
    
    recommendations = []
    
    # Find idle resources
    for resource in find_idle_resources():
        if resource.last_used > 7:  # days
            recommendations.append(f"Delete {resource.name} - unused for {resource.last_used} days")
    
    # Find over-provisioned instances
    for instance in find_overprovisioned_instances():
        if instance.avg_cpu < 20:
            recommendations.append(f"Downsize {instance.name} - only using {instance.avg_cpu}% CPU")
    
    # Find batch opportunities
    for service in find_real_time_services():
        if service.latency_requirement > 60:  # seconds
            recommendations.append(f"Convert {service.name} to batch processing - 70% cost savings")
    
    return recommendations

Measuring Success (Real Metrics)

6-Month Reality Check:

By month 6, you should see:

• 40-60% cost reduction from pre-migration (if you optimize aggressively)
• <2 second response times for 95% of requests
• <0.1% error rate
• Team can deploy new AI features in days, not weeks

12-Month Success Metrics:

• Costs are predictable and optimized
• Team is proficient with AWS AI/ML services
• You're using advanced features (multi-model endpoints, spot training, etc.)
• You can handle traffic spikes without breaking

Common Failure Modes

Month 3 Failures:

• "Our AWS bill is higher than before migration"
• "Everything is slower than our old system"
• "The team hates SageMaker and wants to go back"

Month 6 Failures:

• "We've optimized everything but costs are still high"
• "New AWS services keep breaking our integrations"
• "We're spending more time on AWS optimization than building features"

Month 12 Success Indicators:

• Team prefers AWS to the old system
• Costs are predictably 40-60% lower than pre-migration
• You're shipping AI features faster than before migration
• AWS expertise has become a competitive advantage

The Harsh Reality

What Success Actually Looks Like:

It's not magic. After 12 months, you'll have:

• Lower costs (40-60% reduction if you work at it)
• More complexity (AWS has 200+ services, you'll use 15-20 of them)
• Better reliability (AWS rarely breaks, your old system probably did)
• Faster innovation (once you know what you're doing)

What Failure Looks Like:

• Same or higher costs after 12 months
• Team is still fighting AWS instead of building features
• You're constantly fighting service quotas and regional limitations
• Leadership is questioning the migration ROI

Bottom Line: Migration is just the start. The real work is optimizing over the next 6-12 months. Teams that commit to continuous optimization win. Teams that migrate and forget about it waste money and time.

The Hard Truth About AWS Migration Success

After helping dozens of teams migrate to AWS, here's what separates success from failure:

Successful teams:

• Treat migration as a 12-month project, not a 3-month sprint
• Invest heavily in monitoring and optimization from day one
• Have dedicated engineers focused on cost optimization
• Build internal AWS expertise instead of relying on consultants

Failed teams:

• Underestimate the complexity and timeline
• Focus on feature delivery over optimization
• Assume AWS is automatically cheaper (it's not)
• Don't invest in training their team

The companies that nail AWS migration don't just save money - they ship AI features faster, scale more reliably, and build competitive advantages their competitors can't match. The companies that half-ass it end up with higher costs and more complexity than when they started.

Choose wisely.