AWS sized for your workload, not a hypothetical one.
The right compute, data, and networking for the traffic you actually have — ECS Fargate, Lambda, or EC2; RDS or DynamoDB; a VPC that fits your compliance needs. All of it in Terraform, with a deploy pipeline, CloudWatch alarms, and a cost dashboard so spend never surprises you.
Half your AWS bill is for traffic you don't have.
Most AWS setups are sized for a future that never arrived — instances running at 8% CPU, an RDS three sizes too big, autoscaling minimums set high "just in case," and a NAT gateway quietly charging for data nobody measured. It happens because someone copied an architecture meant for a company a hundred times your size. We start the other way around: from your real traffic and access patterns, we choose the compute, data, and networking that fit it today, write the whole thing in Terraform, and put a cost dashboard in front of you. The goal isn't a clever architecture. It's an AWS bill that matches the application you actually run.
Your AWS account shouldn't be a black box.
Each familiar symptom has the same answer — measure first, then size to fit.
- 01
The AWS bill is unpredictable month to month
Tagged cost tracking and budget alarms, so spend is visible instead of a surprise.
- 02
You're over-provisioned and nobody measured
Right-sizing from real CloudWatch usage, with autoscaling that follows demand.
- 03
ECS vs Lambda vs EC2 was never really decided
Compute chosen against your traffic shape — not whatever was copied first.
- 04
Compliance and VPC peering drive the design
A VPC, subnets, and private paths built around your constraints from the start.
- 05
Nobody can fully account for the AWS account
Terraform for the whole stack — reviewable, reproducible, and diff-able.
Everything an AWS app needs to run well.
On AWS, sized for what you actually run — not for a hypothetical Netflix.
Workload analysis & architecture
We measure your real traffic and access patterns, then choose compute and data to match.
Compute, chosen deliberately
ECS Fargate, Lambda, or EC2 — picked for your workload shape, not a default.
Data layer that fits
RDS or DynamoDB chosen for your queries, with S3 and caching where they earn it.
VPC & networking
Subnets, ALB, security groups, and private paths built around your compliance needs.
Terraform & CI/CD
The whole stack as code, plus a pipeline that builds, tests, and ships to AWS.
CloudWatch & cost dashboard
Metrics, alarms, and a tagged cost view so problems and spend are both visible.
From provisioned infrastructure to a bill you can tune.
- Provision01
- Deploy02
- Scale03
- Monitor04
- Optimize05
Terraform provisions the VPC, compute, and data; the pipeline deploys your application; autoscaling handles real demand; CloudWatch watches for trouble; and the cost dashboard tells you where to trim — the same path every time.
ECS, Lambda, or EC2 — for real reasons.
We pick compute against the shape of your workload. Lambda for spiky, event-driven work where idle compute is wasted money; ECS Fargate for steady containers without managing servers; EC2 when you need specific instance types, GPUs, or reserved-capacity economics. The decision is written down, with the trade-off and the cost math behind it.
- Lambda for spiky, event-driven workloads
- ECS Fargate for steady containers
- EC2 / ECS-on-EC2 when the math favours it
- The reasoning and cost trade-off, written down
RDS or DynamoDB, by access pattern.
The database follows your data, not the trend. Relational data and SQL-thinking teams get RDS — Postgres or MySQL — with a migration path they understand. Key-value, high-throughput, predictable-query workloads get DynamoDB. We add S3 for objects and ElastiCache where reads are hot, and we choose for how you actually query, not for a 'scale' you may never reach.
- RDS (Postgres / MySQL) for relational data
- DynamoDB for key-value at high throughput
- S3 for objects, ElastiCache for hot reads
- Chosen for your access patterns, not the trend
A network built around your constraints.
Compliance and peering requirements shape the design from the start. We lay out the VPC, public and private subnets, an ALB, security groups, and VPC endpoints so your application reaches what it needs over private paths — and so the architecture maps cleanly to whatever framework you answer to. Secrets live in Secrets Manager, scoped per environment, not in .env files.
- VPC, subnets, ALB, and security groups
- Private paths, endpoints, and VPC peering
- Designed around compliance from the start
- Secrets in Secrets Manager, scoped per env
One pipeline to ship. One step to undo.
Every deploy to AWS runs the same path — built, tested, and shipped by a CI/CD pipeline, with rollback designed in rather than improvised. Releases are versioned and immutable, so reverting is redeploying the last known-good build. Where it fits, blue-green or canary releases catch a bad deploy on a slice of traffic before it reaches everyone.
- CI/CD pipeline: build, test, ship to AWS
- Versioned, immutable releases
- One-step rollback to last good build
- Blue-green / canary where it fits
Right-sized AWS, built deliberately.
Analyse the workload
We measure your real traffic, access patterns, and current spend — and the constraints compliance and networking put on the design.
Choose the architecture
We pick compute, data, and networking for what you actually run, with the reasoning and cost trade-offs written down.
Codify in Terraform
The VPC, compute, databases, and IAM as code — reviewable, reproducible, and diff-able before anything changes.
Build the pipeline
A CI/CD pipeline that builds, tests, and ships to AWS, with rollback and protected production.
Watch & hand off
CloudWatch alarms, a cost dashboard, runbooks, and an AWS setup your team can own.
The AWS bill stopped matching the app you actually run.
The AWS bill is unpredictable
Spend jumps month to month and nobody can point to what's driving it — NAT, idle RDS, or traffic you never measured.
You're over-provisioned
Instances run at single-digit CPU and the database is sized for a peak that never comes, because no one measured first.
Compliance shapes the choices
VPC peering, private networking, and audit requirements drive the architecture, and the current setup wasn't built for them.
AWS services, used with restraint.
The things teams ask first.
Size AWS for the app you actually run.
Tell us what you run on AWS and where the bill or the architecture goes wrong. We'll analyse the workload, choose the compute, data, and networking that fit it, codify it in Terraform, and build the pipeline and monitoring — sized for your traffic, not a hypothetical one.