AWS Consulting Company: What They Do and How to Choose One

By Viprasol Tech Team

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AWS has 200+ services. Most applications use fewer than 20. An AWS consulting company's value is knowing which 20 are right for your workload, how to configure them correctly for security and reliability, and how to keep costs from quietly tripling as you scale.

The range of companies calling themselves AWS consultants is wide — from former AWS Solutions Architects doing independent consulting to large integrators who've never built a production system and just resell AWS credits. This guide covers what legitimate AWS consulting includes, the architecture patterns that matter, and how to evaluate providers.

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What AWS Consulting Engagements Cover

Architecture design — mapping your application's requirements to AWS services, designing the network topology (VPC, subnets, routing), and choosing compute/database/cache options. This is the foundation everything else rests on.

Infrastructure as Code (IaC) — implementing the architecture in Terraform or AWS CDK so it's reproducible, version-controlled, and auditable. No manual console configuration in production.

Security and compliance — IAM policies, security groups, encryption configuration, audit logging (CloudTrail, Config), compliance guardrails (AWS Security Hub, GuardDuty).

Cost optimization — right-sizing instances, Reserved Instance or Savings Plans purchasing, identifying unused resources, configuring auto-scaling to avoid over-provisioning.

Migration — moving workloads from on-premise or another cloud to AWS. Application migration, database migration (using AWS DMS), and data migration.

Well-Architected Review — formal assessment of your architecture against AWS's five pillars: Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Optimization.

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The Standard Production AWS Architecture

Most web applications on AWS follow this pattern:

Internet
    ↓
CloudFront (CDN + WAF)
    ↓
Application Load Balancer (ALB) — in public subnets
    ↓
ECS Fargate (application containers) — in private subnets
    ↓
RDS PostgreSQL Multi-AZ — in database subnets (no public access)
ElastiCache Redis — in private subnets
    ↓
S3 (static assets, uploads) — private bucket + presigned URLs
Secrets Manager (credentials, API keys)

Terraform implementation of the core network:

# VPC with public/private/database subnet tiers
module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "~> 5.0"

  name = "${var.environment}-vpc"
  cidr = "10.0.0.0/16"

  azs              = ["us-east-1a", "us-east-1b", "us-east-1c"]
  public_subnets   = ["10.0.1.0/24", "10.0.2.0/24", "10.0.3.0/24"]
  private_subnets  = ["10.0.11.0/24", "10.0.12.0/24", "10.0.13.0/24"]
  database_subnets = ["10.0.21.0/24", "10.0.22.0/24", "10.0.23.0/24"]

  enable_nat_gateway   = true
  single_nat_gateway   = var.environment != "production"  # save cost in non-prod
  enable_dns_hostnames = true
  enable_dns_support   = true

  create_database_subnet_group       = true
  create_database_subnet_route_table = true

  tags = local.common_tags
}

# ECS cluster for application containers
resource "aws_ecs_cluster" "main" {
  name = "${var.environment}-cluster"

  setting {
    name  = "containerInsights"
    value = "enabled"  # CloudWatch Container Insights for monitoring
  }

  tags = local.common_tags
}

# RDS PostgreSQL — Multi-AZ for production, single-AZ for staging
resource "aws_db_instance" "postgres" {
  identifier              = "${var.environment}-postgres"
  engine                  = "postgres"
  engine_version          = "16.3"
  instance_class          = var.db_instance_class
  allocated_storage       = 100
  max_allocated_storage   = 1000  # auto-scaling storage
  storage_type            = "gp3"
  storage_encrypted       = true
  kms_key_id              = aws_kms_key.rds.arn

  db_name  = var.db_name
  username = var.db_username
  manage_master_user_password = true  # Secrets Manager rotation

  multi_az               = var.environment == "production"
  db_subnet_group_name   = module.vpc.database_subnet_group_name
  vpc_security_group_ids = [aws_security_group.rds.id]

  backup_retention_period = var.environment == "production" ? 14 : 1
  deletion_protection     = var.environment == "production"

  performance_insights_enabled = true

  tags = local.common_tags
}

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IAM: The Most Common Security Failure

AWS IAM misconfigurations cause the majority of cloud security incidents. The principles:

Least privilege — every IAM role should have only the permissions it needs for its specific function. Nothing more.

No wildcard actions on sensitive resources:

// Bad: gives the application full S3 access to everything
{
  "Effect": "Allow",
  "Action": "s3:*",
  "Resource": "*"
}

// Good: limited to specific bucket and specific actions
{
  "Effect": "Allow",
  "Action": [
    "s3:GetObject",
    "s3:PutObject",
    "s3:DeleteObject"
  ],
  "Resource": "arn:aws:s3:::my-app-uploads/*"
}

Task roles, not user credentials — ECS tasks get IAM task roles; Lambda functions get execution roles. No long-term access keys stored in application code or environment variables:

# ECS task role — application gets these permissions, nothing else
resource "aws_iam_role" "ecs_task" {
  name = "${var.environment}-api-task-role"

  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [{
      Effect    = "Allow"
      Principal = { Service = "ecs-tasks.amazonaws.com" }
      Action    = "sts:AssumeRole"
    }]
  })
}

resource "aws_iam_role_policy" "ecs_task_policy" {
  role = aws_iam_role.ecs_task.id

  policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Effect   = "Allow"
        Action   = ["s3:GetObject", "s3:PutObject"]
        Resource = "${aws_s3_bucket.uploads.arn}/*"
      },
      {
        Effect   = "Allow"
        Action   = ["secretsmanager:GetSecretValue"]
        Resource = [
          aws_secretsmanager_secret.db.arn,
          aws_secretsmanager_secret.redis.arn,
        ]
      },
      {
        Effect   = "Allow"
        Action   = ["ses:SendEmail"]
        Resource = "*"
        Condition = {
          StringEquals = { "ses:FromAddress" = "noreply@yourdomain.com" }
        }
      }
    ]
  })
}

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Auto-Scaling: Getting It Right

Auto-scaling is one of the most incorrectly configured AWS features. Common mistakes: scaling on CPU alone (latency spikes before CPU catches up), aggressive scale-in (terminating instances too quickly), no scale-in protection during deployments.

ECS Service Auto Scaling with multiple metrics:

resource "aws_appautoscaling_target" "ecs" {
  max_capacity       = 50
  min_capacity       = 2
  resource_id        = "service/${aws_ecs_cluster.main.name}/${aws_ecs_service.api.name}"
  scalable_dimension = "ecs:service:DesiredCount"
  service_namespace  = "ecs"
}

# Scale on CPU utilization
resource "aws_appautoscaling_policy" "cpu" {
  name               = "cpu-scaling"
  policy_type        = "TargetTrackingScaling"
  resource_id        = aws_appautoscaling_target.ecs.resource_id
  scalable_dimension = aws_appautoscaling_target.ecs.scalable_dimension
  service_namespace  = aws_appautoscaling_target.ecs.service_namespace

  target_tracking_scaling_policy_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ECSServiceAverageCPUUtilization"
    }
    target_value       = 60.0   # scale at 60% CPU — buffer before saturation
    scale_in_cooldown  = 300    # don't scale in within 5 minutes of scaling out
    scale_out_cooldown = 60     # scale out quickly
  }
}

# Scale on ALB request count per target (more responsive than CPU)
resource "aws_appautoscaling_policy" "requests" {
  name               = "request-scaling"
  policy_type        = "TargetTrackingScaling"
  resource_id        = aws_appautoscaling_target.ecs.resource_id
  scalable_dimension = aws_appautoscaling_target.ecs.scalable_dimension
  service_namespace  = aws_appautoscaling_target.ecs.service_namespace

  target_tracking_scaling_policy_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ALBRequestCountPerTarget"
      resource_label         = "${aws_alb.main.arn_suffix}/${aws_alb_target_group.api.arn_suffix}"
    }
    target_value = 1000  # 1000 requests per container per minute
  }
}

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Cost Optimization: Where the Real Savings Are

AWS bills can grow quietly. The highest-leverage cost optimization actions:

Reserved Instances / Savings Plans — commit to 1 or 3-year usage in exchange for 30–72% discount. For stable workloads (production databases, always-on services), this is the single highest-return optimization. A $2,000/month RDS instance costs $700–$900/month on a 3-year reservation.

RDS right-sizing — most RDS instances are overprovisioned. Performance Insights shows actual CPU and memory usage. Downsize after watching 2–4 weeks of metrics.

S3 Intelligent Tiering — automatically moves objects between storage tiers based on access patterns. Objects not accessed for 30 days move to cheaper tiers. No retrieval fee for Intelligent Tiering.

Unused resources — AWS Cost Explorer identifies resources with no usage. Common culprits: old EBS snapshots, unattached EBS volumes, idle load balancers, forgotten NAT Gateways ($32/month each).

Data transfer costs — often overlooked. Cross-AZ data transfer costs $0.01/GB. For high-throughput internal services, this adds up. VPC endpoints eliminate data transfer costs for S3 and DynamoDB.

A typical engagement finds 20–40% cost reduction in production AWS accounts without touching architecture.

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Choosing an AWS Consulting Company

AWS Partner status — AWS Partner Network (APN) tiers (Select, Advanced, Premier) indicate some level of vetting. AWS Certified Solutions Architect or DevOps Engineer certifications on the team indicate technical credibility. Neither is sufficient alone — verify through technical assessment.

Terraform or CDK fluency — ask to see IaC code from a previous project. Manual console configuration is not professional AWS work in 2026.

The security question — ask: "How do you approach IAM for a new ECS application?" A good answer covers task roles, least privilege, no long-term keys, and Secrets Manager for credentials. A bad answer involves IAM users with programmatic access keys stored as environment variables.

The cost question — ask: "How would you right-size our infrastructure and what's a typical savings range?" A consultant who can't discuss Reserved Instances, Savings Plans, and the main cost drivers hasn't done cost optimization work.

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Cost Ranges for AWS Consulting

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Working With Viprasol

Our cloud and infrastructure services cover AWS architecture design, Terraform implementation, ECS and EKS deployments, security configuration, and cost optimization. We use Terraform for all infrastructure, IAM task roles for all ECS services, and Secrets Manager for credentials.

Need an AWS consulting company? Viprasol Tech architects and manages AWS infrastructure for startups and enterprises. Contact us.

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See also: DevOps Consulting Company · Cloud Migration Services · Kubernetes Development

Sources: AWS Well-Architected Framework · Terraform AWS Provider · AWS Cost Optimization Pillar