Deploying ML Models to Production: AWS Lambda vs ECS vs EKS

A comprehensive, hands-on guide to choosing the right AWS platform for your ML inference workloads

Introduction

“Which AWS platform should I use to deploy my machine learning model?”

As an AWS Community Builder working with ML teams, I hear this question constantly. The answer is always the same: “It depends.” But on what, exactly? Cost? Performance? Team expertise? Scale?

Instead of giving theoretical advice, I decided to build something concrete: a production-ready sentiment analysis model deployed across all three major AWS container orchestration platforms Lambda, ECS Fargate, and EKS then benchmark them rigorously with real load tests and actual cost calculations.

The results surprised me. Lambda isn’t always cheaper. EKS isn’t always faster. And for most teams, t…

A comprehensive, hands-on guide to choosing the right AWS platform for your ML inference workloads

Introduction

“Which AWS platform should I use to deploy my machine learning model?”

As an AWS Community Builder working with ML teams, I hear this question constantly. The answer is always the same: “It depends.” But on what, exactly? Cost? Performance? Team expertise? Scale?

The results surprised me. Lambda isn’t always cheaper. EKS isn’t always faster. And for most teams, the “obvious” choice might be wrong.

In this post, I’ll share everything I learned building this end-to-end MLOps project, including:

Complete implementation details with code
Real performance benchmarks (10,000+ requests tested)
Actual AWS cost breakdowns at different scales
A decision framework for choosing the right platform
Production deployment best practices

By the end, you’ll have a clear, data-driven understanding of when to use each platform and a complete reference implementation you can deploy yourself.

Full project repository: Available at the end of this post with all code, Infrastructure as Code, and documentation.

The Challenge: Deploying ML Models at Scale

Machine learning models are fundamentally different from traditional web applications. They:

Require significant memory (often 1-10GB for transformer models)
Have cold start penalties (model loading takes seconds)
Need GPU acceleration for some workloads
Consume varying CPU based on input size
Must scale quickly under traffic spikes

These unique requirements mean traditional deployment advice doesn’t always apply. A platform perfect for a REST API might be terrible for ML inference and vice versa.

What We’re Building

I built a complete MLOps pipeline for sentiment analysis using:

Model: DistilBERT (a lightweight BERT variant)

Size: ~250MB
Task: Classify text as POSITIVE or NEGATIVE
Accuracy: 92.3% on IMDb dataset
Inference time: 100-300ms per request

API: FastAPI application with:

Single prediction endpoint (/predict)
Batch prediction endpoint (/predict/batch)
Health checks (/health)
Prometheus metrics (/metrics)

Infrastructure: Everything deployed using:

Terraform for all AWS resources
Docker for containerization
Kubernetes manifests for EKS
Locust for load testing

The beauty of this approach: identical code running on three different platforms, making our comparison truly apples-to-apples.

Architecture Overview

Here’s the high-level architecture that’s consistent across all platforms:

Each platform has its own infrastructure setup, but the application code is identical. As shown in the diagram, all three deployments share common infrastructure (ECR for container images, IAM for permissions, CloudWatch for monitoring) while using different compute platforms.

Implementation Deep Dive

Building the ML Model

I chose DistilBERT because it’s 40% smaller and 60% faster than BERT while retaining 97% of its language understanding. Perfect for production inference.

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from datasets import load_dataset

# Load pre-trained model
model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=2  # Binary: positive/negative
)

# Train on IMDb dataset
dataset = load_dataset("imdb")
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_eval
)

trainer.train()

Training Results:

Accuracy: 92.3%
F1 Score: 0.92
Training time: ~15 minutes on CPU
Model size: 268MB

The model loads in 2-3 seconds and processes individual predictions in 100-150ms on CPU.

Creating the FastAPI Application

FastAPI gives us async support, automatic validation, and built-in API documentation:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

app = FastAPI(title="Sentiment Analysis API")

class PredictionRequest(BaseModel):
text: str

class PredictionResponse(BaseModel):
text: str
sentiment: str
confidence: float
processing_time_ms: float

# Load model on startup
@app.on_event("startup")
async def load_model():
global tokenizer, model
tokenizer = AutoTokenizer.from_pretrained("/opt/ml/model")
model = AutoModelForSequenceClassification.from_pretrained("/opt/ml/model")
model.eval()

@app.post("/predict", response_model=PredictionResponse)
async def predict(request: PredictionRequest):
start_time = time.time()

# Tokenize input
inputs = tokenizer(
request.text,
return_tensors="pt",
truncation=True,
max_length=512
)

# Run inference
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

sentiment = "POSITIVE" if predictions[0][1] > predictions[0][0] else "NEGATIVE"
confidence = max(predictions[0]).item()

return PredictionResponse(
text=request.text,
sentiment=sentiment,
confidence=confidence,
processing_time_ms=(time.time() - start_time) * 1000
)

This gives us:

Automatic request/response validation
Type safety
OpenAPI documentation at /docs
Async request handling for better throughput

Containerization Strategy

I built two Docker images using multi-stage builds:

Standard Image (for ECS/EKS):

# Stage 1: Build dependencies
FROM python:3.11-slim as builder
WORKDIR /build
COPY requirements.txt .
RUN pip install --user -r requirements.txt

# Stage 2: Runtime
FROM python:3.11-slim
COPY --from=builder /root/.local /root/.local
ENV PATH=/root/.local/bin:$PATH

WORKDIR /app
COPY app/ /app/
COPY model/exported_model/ /opt/ml/model/

EXPOSE 8080
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]

Lambda Image:

FROM public.ecr.aws/lambda/python:3.11

COPY requirements.txt ${LAMBDA_TASK_ROOT}/
RUN pip install --no-cache-dir -r ${LAMBDA_TASK_ROOT}/requirements.txt

COPY app/ ${LAMBDA_TASK_ROOT}/
COPY model/exported_model/ /opt/ml/model/

CMD ["main.handler"]

The Lambda image uses AWS’s base image and includes the Mangum adapter to translate API Gateway events to ASGI.

Final image sizes:

Standard: 1.2GB
Lambda: 1.3GB

Platform-Specific Deployments

AWS Lambda: Serverless Inference

Lambda deployment uses container images (not ZIP files) to accommodate our large model:

resource "aws_lambda_function" "ml_inference" {
function_name = "ml-inference"
package_type  = "Image"
image_uri     = "${ecr_repository_url}:lambda-latest"

timeout     = 300  # 5 minutes
memory_size = 3008 # ~3GB (affects CPU too)

environment {
variables = {
DEPLOYMENT_TYPE = "lambda"
MODEL_PATH      = "/opt/ml/model"
}
}
}

I added API Gateway HTTP API for RESTful access:

resource "aws_apigatewayv2_api" "lambda_api" {
name          = "ml-inference-api"
protocol_type = "HTTP"
}

Key Features:

Auto-scaling (up to 1000 concurrent executions)
Pay-per-use pricing
No infrastructure management
15-minute timeout limit

Challenges:

Cold starts (3-5 seconds for model loading)
10GB memory limit
250MB unzipped deployment package limit (container images can be 10GB)

Amazon ECS Fargate: Managed Containers

ECS provides a middle ground between serverless and Kubernetes:

resource "aws_ecs_cluster" "main" {
name = "ml-inference-cluster"

setting {
name  = "containerInsights"
value = "enabled"
}
}

resource "aws_ecs_task_definition" "main" {
family                   = "ml-inference"
network_mode             = "awsvpc"
requires_compatibilities = ["FARGATE"]
cpu                      = "2048"  # 2 vCPU
memory                   = "4096"  # 4GB

container_definitions = jsonencode([{
name  = "ml-inference"
image = "${ecr_repository_url}:latest"

portMappings = [{
containerPort = 8080
protocol      = "tcp"
}]

healthCheck = {
command     = ["CMD-SHELL", "curl -f http://localhost:8080/health || exit 1"]
interval    = 30
timeout     = 5
retries     = 3
}
}])
}

resource "aws_ecs_service" "main" {
name            = "ml-inference-service"
cluster         = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.main.arn
desired_count   = 2
launch_type     = "FARGATE"

load_balancer {
target_group_arn = aws_lb_target_group.main.arn
container_name   = "ml-inference"
container_port   = 8080
}
}

I configured auto-scaling based on CPU utilization:

resource "aws_appautoscaling_policy" "ecs_cpu" {
policy_type = "TargetTrackingScaling"

target_tracking_scaling_policy_configuration {
target_value = 70.0

predefined_metric_specification {
predefined_metric_type = "ECSServiceAverageCPUUtilization"
}
}
}

Key Features:

No cold starts (always-on containers)
Application Load Balancer for traffic distribution
Auto-scaling (1-10 tasks)
VPC networking with private subnets

Challenges:

Always running (costs money even with zero traffic)
Less flexible than Kubernetes
Requires ALB ($16/month base cost)

Amazon EKS: Full Kubernetes

EKS gives us the full power of Kubernetes:

resource "aws_eks_cluster" "main" {
name     = "ml-inference-cluster"
version  = "1.28"
role_arn = aws_iam_role.eks_cluster.arn

vpc_config {
subnet_ids = concat(
aws_subnet.private[*].id,
aws_subnet.public[*].id
)
}
}

resource "aws_eks_node_group" "main" {
cluster_name    = aws_eks_cluster.main.name
node_role_arn   = aws_iam_role.eks_node_group.arn
subnet_ids      = aws_subnet.private[*].id
instance_types  = ["t3.large"]

scaling_config {
desired_size = 2
max_size     = 5
min_size     = 1
}
}

Kubernetes deployment with Horizontal Pod Autoscaler:

apiVersion: apps/v1
kind: Deployment
metadata:
name: ml-inference
spec:
replicas: 2
template:
spec:
containers:
- name: ml-inference
image: <ECR_URL>:latest
resources:
requests:
memory: "2Gi"
cpu: "1000m"
limits:
memory: "4Gi"
cpu: "2000m"
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 60
readinessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 30
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: ml-inference-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: ml-inference
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
averageUtilization: 70

Key Features:

Full Kubernetes API and ecosystem
Advanced deployment strategies (blue/green, canary)
Multi-region/multi-cloud portability
Rich monitoring with Prometheus

Challenges:

Most expensive ($73/month for control plane alone)
Requires Kubernetes expertise
Complex setup and maintenance
Overkill for simple workloads

The Benchmarks

I ran comprehensive load tests using Locust with realistic traffic patterns:

Test Configuration:

Duration: 5 minutes per platform
Concurrent users: 10
Request mix: 70% single predictions, 30% batch (5-10 items)
Sample texts: 20 different movie reviews
Total requests: 10,000+ per platform

Performance Results

Metric	Lambda	ECS Fargate	EKS	Winner
Mean Latency	245ms	156ms	128ms	🏆 EKS
Median Latency	198ms	142ms	115ms	🏆 EKS
P95 Latency	890ms	312ms	267ms	🏆 EKS
P99 Latency	1,450ms	498ms	389ms	🏆 EKS
Cold Start	3-5s	N/A	N/A	🏆 ECS/EKS
Throughput (RPS)	42	98	156	🏆 EKS
Success Rate	99.8%	99.9%	99.9%	-

Key Observations:

EKS dominates on raw performance:

48% lower latency than Lambda
18% lower latency than ECS
3.7x better throughput than Lambda

Lambda’s variability is concerning:

P99 latency is 7.3x higher than median
Cold starts add 3-5 seconds unpredictably
Even with provisioned concurrency, variance is high

ECS provides predictable performance:

Consistent latency (small gap between median and P99)
No cold starts
Good balance for most workloads

Cost Analysis

Here’s where things get interesting. I calculated actual AWS costs at different scales:

Monthly Costs for 10,000 Requests/Day

Platform	Infrastructure	Request Costs	Total	Per 1K Requests
Lambda	$0	$28.50	$28.50	$0.095
ECS	$75.08	$0	$75.08	$0.250
EKS	$194.96	$0	$194.96	$0.650

Lambda Cost Breakdown:

Requests: 300,000/month × $0.20 per 1M = $0.06
Compute: 300,000 × 0.25s × 3GB × $0.0000166667 = $28.44
Total: $28.50/month

ECS Cost Breakdown:

vCPU: 2 × $0.04048/hour × 730 hours = $59.10
Memory: 4GB × $0.004445/hour × 730 hours = $12.98
ALB: $16.00/month (base) + $0.008/LCU
Total: ~$75.08/month

EKS Cost Breakdown:

Control Plane: $0.10/hour × 730 hours = $73.00
Worker Nodes: 2 × t3.large × $0.0832/hour × 730 hours = $121.47
LoadBalancer: ~$0.50/month
Total: ~$194.96/month

Cost at Different Scales

This is where the “it depends” becomes clear:

Daily Requests	Lambda	ECS	EKS	Winner	Reason
1,000	$2.85	$75.08	$194.96	🏆 Lambda	96% cheaper
5,000	$14.25	$75.08	$194.96	🏆 Lambda	81% cheaper
10,000	$28.50	$75.08	$194.96	🏆 Lambda	62% cheaper
25,000	$71.25	$75.08	$194.96	🏆 Lambda	5% cheaper
35,000	$99.75	$75.08	$194.96	🏆 ECS	Crossover
50,000	$142.50	$75.08	$194.96	🏆 ECS	47% cheaper
75,000	$213.75	$88.08	$194.96	🏆 EKS	9% cheaper
85,000	$242.25	$88.08	$194.96	🏆 EKS	Crossover
100,000	$285.00	$105.00	$194.96	🏆 EKS	32% cheaper
250,000	$712.50	$140.00	$210.00	🏆 EKS	70% cheaper
500,000	$1,425.00	$180.00	$225.00	🏆 EKS	84% cheaper

Critical Insights:

Lambda is cheapest until ~35K requests/day - Perfect for startups and low-traffic apps
ECS is the sweet spot for medium traffic (35K-85K requests/day) - Best balance for growing companies
EKS wins at high scale (>85K requests/day) - Enterprise-grade performance and cost
The crossover points matter - Most teams will hit the ECS sweet spot

Real-World Cost Optimization

These are base costs. Here’s how to optimize each:

Lambda Optimization:

Use Compute Savings Plans: -17% ($23.66 vs $28.50)
Switch to ARM64 (Graviton2): -20% ($22.80 vs $28.50)
Right-size memory allocation
Combined savings: ~30%

ECS Optimization:

Use Fargate Spot: -70% on compute ($34.92 vs $75.08)
Use Savings Plans: -17%
Right-size task definitions
Combined savings: ~65%

EKS Optimization:

Use Spot instances for nodes: -70% ($109.46 vs $194.96)
Use Reserved Instances for baseline: -40%
Implement cluster autoscaler
Combined savings: ~60%

Decision Framework

Based on my testing, here’s when to choose each platform:

Choose AWS Lambda When:

✅ Traffic Characteristics:

Sporadic/unpredictable patterns
Daily traffic spikes with long idle periods
<35K requests/day consistently
Event-driven workloads

✅ Business Requirements:

Zero infrastructure management
Fast time-to-market (deploy in minutes)
Pay only for actual usage
Prototyping or MVP

✅ Technical Constraints:

Model <10GB
Inference time <15 minutes
Can tolerate 3-5s cold starts
Team has limited ops experience

❌ Avoid Lambda When:

Need sub-100ms latency consistently
Model >10GB
High, steady traffic (>50K req/day)
Can’t tolerate cold start delays

Real-World Example: A startup analyzing customer feedback emails receives 500 emails/day in bursts after business hours. Lambda cost: $1.43/day vs ECS $75.08/month. Lambda saves $2,100/year.

Choose Amazon ECS Fargate When:

✅ Traffic Characteristics:

Steady, predictable traffic
35K-85K requests/day
Business hours traffic with some variability
Need consistent performance

✅ Business Requirements:

Balance of control and simplicity
Team familiar with Docker
Moderate budget
Want managed infrastructure

✅ Technical Constraints:

No cold starts acceptable
Need 100-200ms latency
Standard container patterns
No Kubernetes expertise

❌ Avoid ECS When:

Very low traffic (<10K req/day) - waste money
Need advanced orchestration (blue/green, canary)
Multi-region/multi-cloud required
Team already proficient in Kubernetes

Real-World Example: A B2B SaaS analyzing 50K customer support tickets/day during business hours (9am-6pm). ECS with auto-scaling provides predictable costs (~$75/month) and performance. Perfect fit.

Choose Amazon EKS When:

✅ Traffic Characteristics:

High, sustained traffic (>85K req/day)
Need to scale to 1M+ requests/day
Global traffic across regions
Complex microservices architecture

✅ Business Requirements:

Enterprise-grade performance
Multi-region/multi-cloud strategy
Advanced deployment patterns
Strong DevOps/SRE team

✅ Technical Constraints:

Need <100ms latency
Complex service mesh
Advanced monitoring (Prometheus, Grafana)
Team has Kubernetes expertise

❌ Avoid EKS When:

Small team (<5 engineers)
No Kubernetes experience
Simple, single-service deployment
Budget constraints
Rapid prototyping needed

Real-World Example: An enterprise processing 1M+ social media posts/day across US, EU, and APAC regions. EKS provides the performance, scalability, and multi-region capabilities needed. Cost: ~$220/month (optimized with Spot) vs $4,275/month on Lambda.

Deployment Pipeline and CI/CD

One of the key advantages of using containerized deployments is that we can use the same CI/CD pipeline for all three platforms. Here’s our deployment flow:

Pipeline Architecture

Our MLOps pipeline is built using GitHub Actions and consists of five automated stages:

1. Source Control & Triggers

Trigger: Every push to main branch
Code checkout with full history for versioning
Environment setup: Python 3.11, Docker BuildKit, AWS credentials

2. Build Stage (2-3 minutes)

- name: Build Docker Images
run: |
# Build standard image for ECS/EKS
docker build -t ml-inference:latest -f Dockerfile .

# Build Lambda-optimized image
docker build -t ml-inference:lambda-latest -f Dockerfile.lambda .

# Tag with version and commit SHA
docker tag ml-inference:latest $ECR_URI:v${VERSION}
docker tag ml-inference:latest $ECR_URI:${GITHUB_SHA}

Multi-stage builds reduce image size by 60%:

Stage 1: Build dependencies (includes compilers)
Stage 2: Runtime-only (production-ready)

3. Test & Security Stage (2-4 minutes)

- name: Run Tests
run: |
# Unit tests
pytest tests/ --cov=app --cov-report=xml

# Integration tests
pytest tests/integration/ --maxfail=1

# Model validation
python scripts/validate_model.py

Security Scanning:

Trivy: Container vulnerability scanning
Bandit: Python code security analysis
SAST: Static application security testing
Dependency check: Known CVE detection

- name: Security Scan
run: |
trivy image --severity HIGH,CRITICAL ml-inference:latest
bandit -r app/ -f json -o security-report.json

4. Push to ECR (1-2 minutes)

- name: Push to Amazon ECR
run: |
aws ecr get-login-password | docker login --username AWS --password-stdin $ECR_URI

# Push all tags
docker push $ECR_URI:latest
docker push $ECR_URI:lambda-latest
docker push $ECR_URI:v${VERSION}
docker push $ECR_URI:${GITHUB_SHA}

# Enable image scanning
aws ecr start-image-scan --repository-name ml-inference --image-id imageTag=latest

Image Management:

Keep last 10 images (lifecycle policy)
Automatic vulnerability scanning on push
Cross-region replication for DR

5. Parallel Deployment (3-5 minutes)

The pipeline deploys to all three platforms simultaneously:

deploy:
strategy:
matrix:
platform: [lambda, ecs, eks]
steps:
- name: Deploy to ${{ matrix.platform }}
run: ./scripts/deploy-${{ matrix.platform }}.sh

Lambda Deployment:

aws lambda update-function-code \
--function-name ml-inference \
--image-uri $ECR_URI:lambda-latest

aws lambda wait function-updated \
--function-name ml-inference

# Update alias to point to new version
aws lambda update-alias \
--function-name ml-inference \
--name production \
--function-version $NEW_VERSION

ECS Deployment (Rolling Update):

# Create new task definition revision
aws ecs register-task-definition \
--cli-input-json file://task-definition.json

# Update service (rolling deployment)
aws ecs update-service \
--cluster ml-inference-cluster \
--service ml-inference-service \
--task-definition ml-inference:${NEW_REVISION} \
--force-new-deployment

# Wait for stable state
aws ecs wait services-stable \
--cluster ml-inference-cluster \
--services ml-inference-service

EKS Deployment (Kubernetes):

# Update image tag in deployment
kubectl set image deployment/ml-inference \
ml-inference=$ECR_URI:latest \
--record

# Rolling update with zero downtime
kubectl rollout status deployment/ml-inference

# Rollback if health checks fail
if ! kubectl rollout status deployment/ml-inference; then
kubectl rollout undo deployment/ml-inference
exit 1
fi

6. Post-Deployment Verification

Smoke Tests:

def run_smoke_tests(endpoint):
# Health check
response = requests.get(f"{endpoint}/health")
assert response.status_code == 200

# Prediction test
test_payload = {"text": "This movie was fantastic!"}
response = requests.post(f"{endpoint}/predict", json=test_payload)
assert response.status_code == 200
assert response.json()["sentiment"] in ["POSITIVE", "NEGATIVE"]

# Latency check
assert response.elapsed.total_seconds() < 1.0

Monitoring Integration:

- name: Update Deployment Metrics
run: |
aws cloudwatch put-metric-data \
--namespace MLOps/Deployment \
--metric-name DeploymentSuccess \
--value 1 \
--dimensions Platform=${{ matrix.platform }},Version=$VERSION

CI/CD Benefits

This unified approach provides:

✅ Consistency: Same code, same tests, same images across all platforms ✅ Speed: 8-12 minutes from commit to production (all platforms) ✅ Safety: Automated testing, security scanning, gradual rollouts ✅ Visibility: Full deployment history, metrics, and audit logs ✅ Reliability: Automatic rollbacks on failure, zero-downtime deployments

Deployment Metrics

Over the last 90 days:

Deployment frequency: 3-5 per week
Success rate: 98.7%
Mean time to deploy: 9.5 minutes
Failed deployments: Auto-rollback within 2 minutes
Zero production incidents from failed deployments

Shared Infrastructure

All three platforms leverage common AWS infrastructure to ensure consistency, security, and cost efficiency:

1. Network Architecture (VPC)

VPC Configuration:

resource "aws_vpc" "main" {
cidr_block           = "10.0.0.0/16"
enable_dns_hostnames = true
enable_dns_support   = true

tags = {
Name = "ml-inference-vpc"
}
}

Multi-AZ Design for High Availability:

us-east-1a (AZ1):
├─ Public Subnet: 10.0.0.0/24
│  ├─ ALB/NLB
│  └─ NAT Gateway 1
└─ Private Subnet: 10.0.10.0/24
├─ ECS Tasks
├─ EKS Worker Nodes
└─ Lambda ENIs

us-east-1b (AZ2):
├─ Public Subnet: 10.0.1.0/24
│  ├─ ALB/NLB (standby)
│  └─ NAT Gateway 2
└─ Private Subnet: 10.0.11.0/24
├─ ECS Tasks
├─ EKS Worker Nodes
└─ Lambda ENIs

Security Groups:

# ALB Security Group (public-facing)
resource "aws_security_group" "alb" {
ingress {
from_port   = 443
to_port     = 443
protocol    = "tcp"
cidr_blocks = ["0.0.0.0/0"]  # HTTPS from internet
}

egress {
from_port       = 8080
to_port         = 8080
protocol        = "tcp"
security_groups = [aws_security_group.ecs_tasks.id]
}
}

# ECS/EKS Security Group (private)
resource "aws_security_group" "compute" {
ingress {
from_port       = 8080
to_port         = 8080
protocol        = "tcp"
security_groups = [aws_security_group.alb.id]  # Only from ALB
}

egress {
from_port   = 443
to_port     = 443
protocol    = "tcp"
cidr_blocks = ["0.0.0.0/0"]  # HTTPS to AWS services
}
}

Network Costs:

NAT Gateway: $0.045/hour × 2 × 730 hours = $65.70/month
Data Transfer: ~$0.09/GB (first 10TB)
VPC endpoints for S3/ECR: Free (saves data transfer costs)

2. Container Registry (ECR)

Repository Configuration:

resource "aws_ecr_repository" "ml_inference" {
name                 = "ml-inference-comparison"
image_tag_mutability = "MUTABLE"

image_scanning_configuration {
scan_on_push = true
}

encryption_configuration {
encryption_type = "AES256"
}
}

Lifecycle Policy (Cost Optimization):

{
"rules": [
{
"rulePriority": 1,
"description": "Keep last 10 images",
"selection": {
"tagStatus": "any",
"countType": "imageCountMoreThan",
"countNumber": 10
},
"action": {
"type": "expire"
}
}
]
}

Multi-Platform Image Support:

# Build multi-architecture images
docker buildx build \
--platform linux/amd64,linux/arm64 \
-t $ECR_URI:latest \
--push .

Repository Features:

Automatic scanning: Trivy integration for CVE detection
Immutable tags: Production images are immutable
Cross-region replication: DR to us-west-2
Lifecycle management: Auto-delete old images

ECR Costs:

Storage: $0.10/GB/month
Average usage: 3GB (10 images × 300MB)
Monthly cost: ~$0.30

3. IAM Roles & Policies

Principle of Least Privilege:

Each platform gets its own IAM role with minimal permissions:

Lambda Execution Role:

{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"ecr:GetAuthorizationToken",
"ecr:BatchGetImage",
"ecr:GetDownloadUrlForLayer"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "arn:aws:logs:*:*:log-group:/aws/lambda/ml-inference*"
},
{
"Effect": "Allow",
"Action": [
"cloudwatch:PutMetricData"
],
"Resource": "*",
"Condition": {
"StringEquals": {
"cloudwatch:namespace": "MLInference"
}
}
}
]
}

ECS Task Role:

{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"ecr:GetAuthorizationToken",
"ecr:BatchCheckLayerAvailability",
"ecr:GetDownloadUrlForLayer",
"ecr:BatchGetImage"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "arn:aws:logs:*:*:log-group:/ecs/ml-inference*"
}
]
}

EKS Node Role (IRSA - IAM Roles for Service Accounts):

apiVersion: v1
kind: ServiceAccount
metadata:
name: ml-inference-sa
annotations:
eks.amazonaws.com/role-arn: arn:aws:iam::ACCOUNT_ID:role/ml-inference-eks-pod-role

Secrets Management:

resource "aws_secretsmanager_secret" "model_config" {
name = "ml-inference/model-config"

rotation_rules {
automatically_after_days = 30
}
}

4. Monitoring & Logging (CloudWatch)

Centralized Logging:

CloudWatch Log Groups:
├─ /aws/lambda/ml-inference          (Lambda logs)
├─ /ecs/ml-inference                 (ECS logs)
├─ /aws/eks/ml-inference-cluster     (EKS control plane)
└─ /aws/containerinsights/ml-inference (EKS pod logs)

Log Retention & Costs:

resource "aws_cloudwatch_log_group" "lambda" {
name              = "/aws/lambda/ml-inference"
retention_in_days = 30  # Balance cost vs. debugging needs

tags = {
Platform = "Lambda"
}
}

Custom Metrics:

# Application sends custom metrics
cloudwatch.put_metric_data(
Namespace='MLInference',
MetricData=[
{
'MetricName': 'ModelInferenceTime',
'Value': inference_duration_ms,
'Unit': 'Milliseconds',
'Dimensions': [
{'Name': 'Platform', 'Value': 'lambda'},
{'Name': 'ModelVersion', 'Value': 'v1.2.0'}
],
'Timestamp': datetime.utcnow()
}
]
)

CloudWatch Dashboard:

{
"widgets": [
{
"type": "metric",
"properties": {
"metrics": [
[ "MLInference", "InferenceLatency", { "stat": "Average" } ],
[ ".", ".", { "stat": "p99" } ]
],
"period": 300,
"stat": "Average",
"region": "us-east-1",
"title": "Inference Latency"
}
},
{
"type": "metric",
"properties": {
"metrics": [
[ "AWS/Lambda", "Invocations", { "label": "Lambda" } ],
[ "AWS/ECS", "TaskCount", { "label": "ECS" } ],
[ "ContainerInsights", "pod_number_of_running_pods", { "label": "EKS" } ]
],
"title": "Active Compute Units"
}
}
]
}

Alarms:

resource "aws_cloudwatch_metric_alarm" "high_error_rate" {
alarm_name          = "ml-inference-high-error-rate"
comparison_operator = "GreaterThanThreshold"
evaluation_periods  = 2
metric_name         = "5XXError"
namespace           = "AWS/ApiGateway"
period              = 60
statistic           = "Sum"
threshold           = 10
alarm_description   = "This metric monitors API error rate"
alarm_actions       = [aws_sns_topic.alerts.arn]
}

CloudWatch Costs:

Logs ingestion: $0.50/GB
Logs storage: $0.03/GB/month
Custom metrics: $0.30/metric/month
Average monthly: ~$15-20

5. Cost Summary - Shared Infrastructure

Component	Monthly Cost	Notes
VPC	$0	No charge for VPC itself
NAT Gateways (2)	$65.70	$0.045/hour × 2 × 730 hours
Data Transfer	~$5-10	Variable based on traffic
ECR	$0.30	3GB storage
CloudWatch Logs	$10-15	30-day retention
CloudWatch Metrics	$5	~15 custom metrics
Secrets Manager	$0.40	$0.40/secret/month
Total	~$86-96	Shared across all platforms

Cost Optimization Tips:

Use VPC Endpoints for S3/ECR (saves data transfer costs)
Compress CloudWatch Logs (reduces storage by 70%)
Archive old logs to S3 (10× cheaper: $0.023/GB vs $0.03/GB)
Use CloudWatch Logs Insights instead of exporting to S3
Enable ECR lifecycle policies (auto-delete unused images)

Infrastructure as Code

All shared infrastructure is defined in Terraform:

terraform/
├── modules/
│   ├── vpc/                 # VPC, subnets, NAT gateways
│   ├── ecr/                 # Container registry
│   ├── iam/                 # Roles and policies
│   └── monitoring/          # CloudWatch, alarms
├── environments/
│   ├── dev/
│   ├── staging/
│   └── production/
└── main.tf

Benefits of Shared Infrastructure:

✅ Cost Reduction: Single VPC, NAT gateways, and ECR for all platforms ✅ Consistency: Same networking, security, and monitoring everywhere ✅ Simplified Management: One IaC codebase, centralized changes ✅ Security: Centralized IAM policies, unified audit logs ✅ Reliability: Multi-AZ design, automatic failover

Production Best Practices

Security Hardening

Lambda:

# Use IAM for authorization
resource "aws_lambda_function_url" "ml_inference" {
authorization_type = "AWS_IAM"  # Not NONE!
}

# Scan container images
resource "aws_ecr_repository" "ml_inference" {
image_scanning_configuration {
scan_on_push = true
}
}

# Use Secrets Manager
environment {
variables = {
DB_SECRET_ARN = aws_secretsmanager_secret.db.arn
}
}

ECS/EKS:

Use private subnets for compute
Security groups with minimal inbound rules
IAM roles for task/pod authentication
Enable encryption at rest and in transit

Monitoring and Observability

All three platforms integrate with CloudWatch, but I added custom metrics:

import boto3

cloudwatch = boto3.client('cloudwatch')

def log_prediction(latency_ms, sentiment):
cloudwatch.put_metric_data(
Namespace='MLInference',
MetricData=[
{
'MetricName': 'InferenceLatency',
'Value': latency_ms,
'Unit': 'Milliseconds',
'Dimensions': [
{'Name': 'Sentiment', 'Value': sentiment},
{'Name': 'Platform', 'Value': os.getenv('DEPLOYMENT_TYPE')}
]
}
]
)

For EKS, I added Prometheus metrics:

from prometheus_client import Counter, Histogram

PREDICTION_LATENCY = Histogram(
'prediction_latency_seconds',
'Prediction latency in seconds'
)

PREDICTION_COUNT = Counter(
'predictions_total',
'Total predictions',
['sentiment']
)

@app.post("/predict")
async def predict(request: PredictionRequest):
with PREDICTION_LATENCY.time():
result = model_analyzer.predict(request.text)
PREDICTION_COUNT.labels(sentiment=result['sentiment']).inc()
return result

Auto-Scaling Configuration

Lambda: Works out of the box, but configure reserved concurrency for production:

resource "aws_lambda_function" "ml_inference" {
reserved_concurrent_executions = 100
}

ECS: Target tracking auto-scaling:

resource "aws_appautoscaling_policy" "ecs_cpu" {
target_tracking_scaling_policy_configuration {
target_value = 70.0
scale_in_cooldown  = 300  # 5 min
scale_out_cooldown = 60   # 1 min
}
}

EKS: Horizontal Pod Autoscaler with custom metrics:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
averageUtilization: 70
- type: Resource
resource:
name: memory
target:
averageUtilization: 80
behavior:
scaleUp:
stabilizationWindowSeconds: 0
policies:
- type: Percent
value: 100
periodSeconds: 30

Lessons Learned

1. Lambda Cold Starts Are Real (And Painful)

Even with 3GB memory and optimized container images, Lambda cold starts took 3-5 seconds. That’s unacceptable for user-facing applications. Provisioned concurrency helps but adds significant cost.

Mitigation:

Keep functions warm with scheduled pings
Use provisioned concurrency for critical paths
Accept cold starts for async/batch workloads

2. ECS is Underrated

Most teams jump from Lambda to EKS, skipping ECS entirely. That’s a mistake. ECS Fargate provides 80% of Kubernetes benefits with 20% of the complexity. For most teams, it’s the Goldilocks solution.

3. Right-Sizing Matters More Than Platform

A poorly configured EKS cluster can cost more than a well-optimized Lambda setup. I saved:

30% on Lambda by right-sizing memory
65% on ECS using Fargate Spot
60% on EKS using Spot instances

Focus on optimization before switching platforms.

4. Benchmark With Real Traffic

My synthetic tests showed Lambda performing better than production. Real traffic has:

Variable input lengths (affecting inference time)
Burst patterns (causing cold starts)
Geographic distribution (network latency)

Always test with production-like data.

5. Total Cost of Ownership Includes People

EKS might be cheaper at scale, but requires Kubernetes expertise. If you need to hire a DevOps engineer ($120K/year), Lambda suddenly looks very affordable.

Factor in:

Team expertise and training
Operational overhead
On-call burden
Time to market

Conclusion

So, which platform should you choose?

For most teams starting out: ECS Fargate

It provides:

No cold starts (predictable performance)
Reasonable cost at medium scale
Familiar Docker patterns
Managed infrastructure
Path to EKS if you outgrow it

Start with Lambda if you have:

Truly unpredictable traffic
<35K requests/day
Strong cost constraints
No ops team

Upgrade to EKS when you hit:

>85K requests/day sustained
Need for advanced orchestration
Multi-region requirements
Team with K8s expertise

The beauty of this architecture: you can migrate between platforms with minimal code changes. Start simple, scale as needed.

Get the Complete Project

I’ve open-sourced everything:

Repository includes:

✅ Complete model training code
✅ FastAPI application
✅ Dockerfiles for all platforms
✅ Terraform modules (Lambda, ECS, EKS)
✅ Kubernetes manifests
✅ Load testing suite
✅ Deployment scripts
✅ Comprehensive documentation

Quick start:

git clone https://github.com/rifkhan107/mlops-inference-comparison.git
cd mlops-inference-comparison
make quickstart  # Deploys everything in 60 minutes

Learn more:

What’s Next?

In future posts, I’ll explore:

GPU-accelerated inference on EKS with NVIDIA GPUs
Multi-model serving with SageMaker Multi-Model Endpoints
A/B testing ML models in production
Feature stores for real-time inference

Let’s Connect

I’m passionate about MLOps and cloud-native architectures. Let’s discuss your ML deployment challenges:

Twitter: rifkhan107
LinkedIn: rifkhan107
GitHub: rifkhan107
AWS Community Builders: rifkhan

Questions about the project? Open an issue on GitHub or reach out on Twitter.

Found this helpful? ⭐ Star the repository and share with your team!

Introduction

Introduction

The Challenge: Deploying ML Models at Scale

What We’re Building

Architecture Overview

Implementation Deep Dive

Building the ML Model

Creating the FastAPI Application

Containerization Strategy

Platform-Specific Deployments

AWS Lambda: Serverless Inference

Amazon ECS Fargate: Managed Containers

Amazon EKS: Full Kubernetes

The Benchmarks

Performance Results

Cost Analysis

Monthly Costs for 10,000 Requests/Day

Cost at Different Scales

Real-World Cost Optimization

Decision Framework

Choose AWS Lambda When:

Choose Amazon ECS Fargate When:

Choose Amazon EKS When:

Deployment Pipeline and CI/CD

Pipeline Architecture

1. Source Control & Triggers

2. Build Stage (2-3 minutes)

3. Test & Security Stage (2-4 minutes)

4. Push to ECR (1-2 minutes)

5. Parallel Deployment (3-5 minutes)

6. Post-Deployment Verification

CI/CD Benefits

Deployment Metrics

Shared Infrastructure

1. Network Architecture (VPC)

2. Container Registry (ECR)

3. IAM Roles & Policies

4. Monitoring & Logging (CloudWatch)

5. Cost Summary - Shared Infrastructure

Infrastructure as Code

Production Best Practices

Security Hardening

Monitoring and Observability

Auto-Scaling Configuration

Lessons Learned

1. Lambda Cold Starts Are Real (And Painful)

2. ECS is Underrated

3. Right-Sizing Matters More Than Platform

4. Benchmark With Real Traffic

5. Total Cost of Ownership Includes People

Conclusion

Get the Complete Project

What’s Next?

Let’s Connect

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