In the previous subchapter, you learned how to view and control your costs with Cost Explorer and Budgets. Those tools show you the spending, but you have to decide what to optimize. What if AWS gave you specific recommendations on what to change to save and improve? That’s what Trusted Advisor and Compute Optimizer do: two automatic advisors that analyze your account and proactively tell you what to do.
The difference: from “seeing” to “advising”
Cost Explorer shows you the numbers; these tools go a step further and recommend actions:
Cost Explorer → "you spend €450 on servers" (informs you)
Trusted Advisor → "you have 3 almost idle servers,
turn them off and you'll save €200" (advises you)It’s the difference between a dashboard (which shows data) and an advisor (who tells you what to do with it).
Trusted Advisor: your account’s general advisor
AWS Trusted Advisor is like an automatic consultant that reviews your AWS account and gives you recommendations in several categories, not just costs. It analyzes your account and points out opportunities for improvement and issues:
Trusted Advisor reviews and recommends in these areas: 💰 Cost optimization (underutilized resources, savings...) 🔒 Security (insecure configurations) ⚡ Performance (mis-sized resources) 🛡️ Fault tolerance (lack of redundancy, backups...) 📊 Service limits (you’re approaching an AWS limit)
Analogy: Trusted Advisor is like bringing in an expert inspector to review your house who, as they walk through, tells you: “this window doesn’t close properly (security)”, “you have a lightbulb wasting energy (cost)”, “you’re missing a smoke detector here (security/faults)”, “this outlet is overloaded (performance)”. It gives you a clear list of what to fix and why.
Examples of Trusted Advisor cost recommendations
- “You have servers with very low usage → consider turning them off or downsizing.”
- “You have reserved IP addresses not in use → you’re being charged for nothing, release them.”
- “You have disks not attached to any server → they’re still costing you, delete them.”
- “You could save by purchasing reserved capacity for these resources” (we’ll cover this in subchapter 25.4).
Each recommendation usually comes with the estimated savings, so you can prioritize.
Compute Optimizer: the sizing specialist
AWS Compute Optimizer is more specialized: it focuses on recommending the right size for your compute resources (EC2 servers, Lambdas, etc.). It analyzes how you actually use those resources (their CPU, memory, etc., over time, using CloudWatch data) and tells you if they are properly sized, oversized (too big, wasting money), or undersized (too small, running tight).
Compute Optimizer analyzes your server:
"This 'large' server only uses 10% of its capacity.
RECOMMENDATION: switch to a 'medium' one → save 50% without losing performance"Analogy: Compute Optimizer is like an advisor who looks at your electricity bill and your habits and tells you: “you have a 10 kW contract but never go above 4; drop to 5 kW and you’ll pay much less without ever falling short.” It looks at your actual usage and adjusts the size to what you really need.
This size adjustment is so important it has its own name —rightsizing— and we dedicate the next entire subchapter (25.3) to it. Compute Optimizer is the tool that gives you those rightsizing recommendations with data.
Trusted Advisor vs Compute Optimizer
| Trusted Advisor | Compute Optimizer | |
|---|---|---|
| Scope | General: costs, security, performance, faults, limits | Specialized in compute sizing (EC2, Lambda...) |
| Type of advice | Varied (turn off resources, improve security...) | Optimal size based on actual usage |
| Analogy | General house inspector | Contracted power advisor |
They complement each other: Trusted Advisor gives you a broad view of improvements in many areas, and Compute Optimizer dives deep into the optimal sizing of your compute resources.
Real-world example: a company has been on AWS for a year without reviewing anything. They activate Trusted Advisor and get a list: 5 unused IPs (charging for nothing), 8 orphaned disks, 3 almost idle servers, and two security alerts. Then they check Compute Optimizer, which tells them half their servers are oversized and could use smaller sizes. By applying the recommendations, they reduce their bill by 35% without affecting service, and also improve their security. All thanks to automatic advisors they just had to look at.
How it fits into the cost strategy
Cost Explorer / Budgets (25.1) → SEE and CONTROL spending
Trusted Advisor (this) → BROAD RECOMMENDATIONS (costs, security...)
Compute Optimizer (this) → OPTIMAL SIZE RECOMMENDATIONS (rightsizing)
│
▼
Rightsizing (25.3) → apply the size adjustmentFirst you see the spending, then you get recommendations, and then you act (starting with rightsizing in the next subchapter).
What you should remember
- Cost Explorer and Budgets show and control spending; Trusted Advisor and Compute Optimizer go a step further and recommend specific actions. It’s moving from a dashboard to an advisor.
- Trusted Advisor is a general automatic consultant that reviews your account and recommends improvements in costs, security, performance, fault tolerance, and limits (with estimated savings). Like an inspector walking through your house pointing out what to fix.
- Compute Optimizer is specialized in recommending the right size for your compute resources based on their actual usage (detects oversized and undersized). Like a contracted power advisor.
- They complement each other: Trusted Advisor gives a broad view, Compute Optimizer dives deep into sizing (rightsizing).
- Applying their recommendations can greatly reduce your bill without affecting service, and also improve security and reliability.
In the next subchapter, we’ll dive deeper into the most direct savings technique these tools recommend: rightsizing (adjusting the size of resources to what you really need).
Cloud, AWS & Terraform — From Zero to Expert
Chapter 1 · What is cloud computing
- 1.1 The traditional client-server model
- 1.2 Problems the cloud came to solve
- 1.3 On-premise vs cloud vs hybrid
- 1.4 The three service models: IaaS, PaaS, SaaS
- 1.5 The five pillars of cloud (according to NIST)
- 1.6 Real advantages: elasticity, pay-as-you-go, global availability
Chapter 2 · The cloud market and major providers
- 2.1 AWS, Azure and GCP: differences and market share
- 2.2 Why learn AWS first
- 2.3 Concepts that are universal among providers
Chapter 3 · Regions, availability zones and edge
- 3.1 What is an AWS region and how to choose it
- 3.2 Availability Zones: high availability by design
- 3.3 Edge locations and CloudFront
- 3.4 Latency, resilience and data sovereignty
Chapter 4 · Compute: EC2
- 4.1 Instances: types, families and when to choose each
- 4.2 AMIs, key pairs and Security Groups
- 4.3 Instance lifecycle
- 4.4 Elastic IPs and Placement Groups
- 4.5 Savings Plans vs Reserved vs On-Demand vs Spot
Chapter 5 · Storage: S3
- 5.1 Buckets, objects and keys
- 5.2 Storage classes (Standard, IA, Glacier…)
- 5.3 Versioning and object lifecycle
- 5.4 Bucket policies and ACLs
- 5.5 Static website hosting
Chapter 6 · Networking: VPC
- 6.1 What is a VPC and why you need it
- 6.2 Public and private subnets
- 6.3 Internet Gateway and NAT Gateway
- 6.4 Route Tables and Network ACLs
- 6.5 VPC Peering and endpoints
Chapter 7 · Identity and access: IAM
- 7.1 Users, groups, roles and policies
- 7.2 The principle of least privilege
- 7.3 Identity-based vs resource-based policies
- 7.4 MFA and temporary credentials (STS)
- 7.5 IAM security best practices
Chapter 8 · Managed databases
- 8.1 RDS: engines, Multi-AZ and read replicas
- 8.2 Aurora and its advantages over vanilla RDS
- 8.3 DynamoDB: key-value / document model
- 8.4 ElastiCache for in-memory cache
- 8.5 When to use each type of database
Chapter 9 · Why Infrastructure as Code
- 9.1 Problems with manual provisioning
- 9.2 Declarative vs imperative IaC
- 9.3 Terraform vs CloudFormation vs Pulumi vs CDK
- 9.4 The plan → apply → destroy cycle
Chapter 10 · HCL: the Terraform language
- 10.1 Resource, variable, output, locals blocks
- 10.2 Data types: string, number, bool, list, map, object
- 10.3 Expressions, references and built-in functions
- 10.4 Conditionals and loops (count, for_each, for)
Chapter 11 · Providers and state
- 11.1 How the AWS provider works
- 11.2 The terraform.tfstate file and its importance
- 11.3 Local state vs remote state (S3 + DynamoDB)
- 11.4 Essential commands: init, plan, apply, destroy, fmt, validate
Chapter 12 · Your first real infrastructure in Terraform
- 12.1 Create a VPC with subnets from scratch
- 12.2 Launch a public EC2 instance
- 12.3 Associate a Security Group and an Elastic IP
- 12.4 Outputs and references between resources
- 12.5 Team workflow: PR review of plans
Chapter 13 · Load balancing and auto scaling
- 13.1 Application Load Balancer vs Network Load Balancer
- 13.2 Target Groups, listeners and rules
- 13.3 Auto Scaling Groups: policies and metrics
- 13.4 Warm pools and lifecycle hooks
Chapter 14 · Serverless with Lambda
- 14.1 The Lambda execution model
- 14.2 Triggers: API Gateway, S3, DynamoDB Streams, SQS
- 14.3 Dependency management and layers
- 14.4 Cold starts and strategies to reduce them
- 14.5 Limits and anti-patterns
Chapter 15 · Messaging and events
- 15.1 SQS: standard vs FIFO queues, DLQ
- 15.2 SNS: topics, subscriptions, fan-out
- 15.3 EventBridge: event buses and rules
- 15.4 Patterns: pub/sub, decoupling, saga
Chapter 16 · Content delivery and DNS
- 16.1 Route 53: record types and routing policies
- 16.2 CloudFront: distributions, caches and origins
- 16.3 ACM: free SSL/TLS certificates
- 16.4 WAF integrated with CloudFront
Chapter 17 · Containers on AWS
- 17.1 Docker: quick review of key concepts
- 17.2 ECR: private image registry
- 17.3 ECS: task definitions, services, Fargate vs EC2
- 17.4 EKS: when Kubernetes and when not
Chapter 18 · Modules: reuse and composition
- 18.1 Anatomy of a Terraform module
- 18.2 Input variables, outputs and dependencies
- 18.3 Local modules vs Terraform Registry modules
- 18.4 Module versioning with Git tags
- 18.5 Design of generic vs domain-specific modules
Chapter 19 · Workspaces and environment management
- 19.1 Terraform workspaces: use cases and limitations
- 19.2 Directory strategy per environment (dev/stg/prod)
- 19.3 Terragrunt: DRY for environment configurations
- 19.4 Environment variables and .tfvars files
Chapter 20 · Remote backends and locking
- 20.1 Configure S3 + DynamoDB as backend
- 20.2 State locking: avoiding team corruption
- 20.3 State migration between backends
- 20.4 terraform import: bring existing resources into state
Chapter 21 · Infrastructure testing
- 21.1 Terraform validate and fmt in CI
- 21.2 Checkov and tfsec: static security analysis
- 21.3 Terratest: integration tests in Go
- 21.4 Contract testing between modules
Chapter 22 · Terraform in CI/CD
- 22.1 Basic pipeline: lint → plan → apply in GitHub Actions
- 22.2 Atlantis: GitOps for Terraform
- 22.3 Terraform Cloud / HCP Terraform
- 22.4 Drift detection and automatic reconciliation
Chapter 23 · Defense in depth
- 23.1 AWS Organizations and Service Control Policies
- 23.2 AWS Config: continuous compliance
- 23.3 GuardDuty: threat detection
- 23.4 Security Hub: centralized view
- 23.5 KMS: key management and rotation
- 23.6 Secrets Manager vs Parameter Store
Chapter 24 · Observability: logs, metrics and traces
- 24.1 CloudWatch Logs, metrics and alarms
- 24.2 CloudWatch Dashboards and Contributor Insights
- 24.3 X-Ray: distributed tracing
- 24.4 OpenTelemetry on AWS
- 24.5 Managed Grafana and Managed Prometheus
Chapter 25 · Cost optimization
- 25.1 AWS Cost Explorer and budgets with alerts
- 25.2 Trusted Advisor and Compute Optimizer
- 25.3 Rightsizing: how to detect overprovisioning
- 25.4 Savings Plans vs Reserved Instances: strategic decision
- 25.5 FinOps: culture and processes to control spending
Chapter 26 · High availability and disaster recovery
- 26.1 RTO and RPO: defining objectives
- 26.2 Strategies: backup/restore, pilot light, warm standby, multi-site
- 26.3 Route 53 health checks and automatic failover
- 26.4 AWS Backup: centralized backup policy
Chapter 27 · AWS Well-Architected Framework
- 27.1 The six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, sustainability
- 27.2 Well-Architected Tool: formal reviews
- 27.3 How to apply the framework in design decisions
Chapter 28 · Serverless architectures at scale
- 28.1 Event-driven architecture with Lambda + EventBridge
- 28.2 Saga pattern for distributed transactions
- 28.3 Step Functions: orchestration of complex workflows
- 28.4 Lambda@Edge and CloudFront Functions
Chapter 29 · Data platforms on AWS
- 29.1 Data Lake with S3, Glue and Athena
- 29.2 Kinesis Data Streams and Firehose for streaming
- 29.3 Redshift: data warehousing at scale
- 29.4 Lake Formation: data governance
Chapter 30 · Multi-account and landing zones
- 30.1 Why separate workloads into different accounts
- 30.2 AWS Control Tower and Account Factory
- 30.3 Centralized log and security management
- 30.4 Terraform at multi-account scale with shared modules
Chapter 31 · Platform Engineering and Internal Developer Platform
- 31.1 Golden paths and abstractions over Terraform
- 31.2 AWS Service Catalog
- 31.3 Backstage as a developer portal
- 31.4 Terraform modules as internal product
Chapter 32 · Relevant AWS certifications
- 32.1 Cloud Practitioner: is it worth it?
- 32.2 Solutions Architect Associate → Professional
- 32.3 DevOps Engineer Professional
- 32.4 Specialty: Security, Database, Networking
- 32.5 HashiCorp Terraform Associate
Chapter 33 · Projects to consolidate what you've learned
- 33.1 Project 1: serverless blog (S3 + CloudFront + Lambda + DynamoDB)
- 33.2 Project 2: REST API with ECS Fargate + RDS + ALB
- 33.3 Project 3: data platform with Glue + Athena + Redshift
- 33.4 Project 4: multi-account landing zone with Terraform and Control Tower
