Cloud & Infrastructure
AWS
The broadest cloud on the market. We help you use the parts you need and ignore the 200 services you don’t.
Overview
AWS is the largest and oldest of the public clouds, and it shows in both directions. There are more than 200 services, several ways to run the same workload, and a pricing model detailed enough to need its own discipline. That breadth is genuinely useful once you know your way around it — you can run virtual machines, containers or functions; store objects, blocks or rows; and reach almost every region on earth from one account. It is also the main reason teams get lost. Most of AWS is not for you, and knowing which tenth to use is most of the skill.
We design, build and run production systems on AWS, and we bring the operator’s bias to every decision. That means we care about what a service costs at 3am under load, what happens when an availability zone drops, and who can reach your data and how you would know. We are not resellers earning margin on your bill; we would rather put you on a managed database and a handful of well-chosen services than architect something clever you cannot afford to keep running.
AWS suits teams who need real control — bespoke networking, strict data residency, unusual compliance, or workloads that genuinely span regions. It rewards engineering maturity and punishes the absence of it. If you are a small team shipping a straightforward web application, a simpler platform will often get you further faster, and we will tell you so before you commit.
Best for — Teams that need the depth and control of the dominant cloud, and have — or want us to bring — the engineering discipline to run it safely and affordably.
Why teams choose AWS
Breadth without the sprawl
We map your workload to the handful of AWS services that actually fit — often EC2 or ECS, RDS, S3, CloudFront and a queue — and deliberately leave the other 190 alone. Fewer moving parts means fewer things to secure, patch and pay for.
Cost you can predict
Every design we ship comes with a cost model and guardrails: budgets, alarms, tagging and right-sized instances. You should never learn what a service costs from the invoice.
Built to be operated
We build with the on-call engineer in mind — sensible alarms, structured logs, runbooks and infrastructure defined in code so the whole environment can be rebuilt from a repository, not from memory.
Why businesses choose AWS
- You want an operator, not a reseller — advice on what to run and, just as often, what to avoid.
- You need cost governance treated as a first-class part of the architecture, not an afterthought once the bill arrives.
- You value least-privilege IAM and a clear grasp of the shared-responsibility model over a working demo that quietly leaves you exposed.
- You want infrastructure defined in code and handed over, so your team can run it without depending on us forever.
What we build with AWS
The capabilities this technology is genuinely strong at — and what we most often build with it.
Compute: EC2, ECS/EKS, Lambda
We pick the compute model per workload. EC2 for full control or licensed software, ECS on Fargate for containers without cluster management, EKS when you genuinely need Kubernetes, and Lambda for event-driven or spiky work where paying per request beats paying for idle servers.
Storage: S3, EBS, EFS
S3 for durable object storage, static assets and data lakes — with lifecycle rules and the right storage class so you are not paying Standard rates for cold data. EBS for block storage on instances, EFS where multiple hosts need a shared filesystem.
Databases: RDS, Aurora, DynamoDB
RDS or Aurora for PostgreSQL and MySQL when you want relational guarantees and managed backups; DynamoDB when you need single-digit-millisecond key-value access at scale and can model around its access patterns. We are candid about when DynamoDB is the wrong tool.
Networking & delivery: VPC, CloudFront, Route 53
Private-by-default VPCs with public and private subnets, security groups scoped tightly, CloudFront in front of static and dynamic content to cut latency and egress, and Route 53 for DNS and health-checked failover.
Messaging & events: SQS, SNS, EventBridge
SQS to decouple services and absorb load spikes, SNS and EventBridge for fan-out and event-driven architectures. These are the joints that let a system degrade gracefully instead of falling over under pressure.
Identity & observability: IAM, CloudWatch, CloudTrail
IAM roles and policies written to least-privilege — no wildcard admin handed to application code. CloudWatch for metrics, logs and alarms, CloudTrail for an audit trail of who did what, so incidents can actually be investigated.
Use cases
Migrating off a single overloaded server
Moving a monolith from one struggling VM onto AWS with a managed RDS database, an autoscaling group or ECS service behind a load balancer, and S3 plus CloudFront for assets — buying headroom and removing single points of failure without a rewrite.
Event-driven and serverless backends
Lambda, API Gateway, DynamoDB and SQS for workloads with uneven traffic, where paying per request is far cheaper than keeping servers warm, and scaling is handled for you.
Data pipelines and analytics
S3 as the landing zone, with Glue, Athena or a managed processing layer to turn raw events into queryable data — without standing up and babysitting a cluster you only need part of the day.
Regulated or multi-region workloads
Systems with data-residency rules or high-availability targets that need private networking, region pinning and cross-AZ redundancy that a simpler platform cannot express.
When AWS is the right choice
- You need breadth: managed databases, queues, object storage, container orchestration and serverless compute under one account, one bill and one IAM model.
- You have real compliance, data-residency or networking requirements that a simpler PaaS cannot express — private subnets, VPC peering, dedicated tenancy, region pinning.
- You expect to run at scale or across multiple regions, and want mature tooling for autoscaling, failover and observability rather than building it yourself.
- Wrong for a small team shipping a standard CRUD app with no unusual constraints — a PaaS such as Vercel, Render or Fly.io will be cheaper to run and far cheaper to reason about.
- Wrong when nobody on the team owns cloud cost or security. AWS gives you enough freedom to run up a surprising bill or leave a bucket open; without an owner, that freedom becomes a liability.
AWS: pros and cons
Strengths
- Unmatched breadth and depth — almost any workload has a mature, well-documented AWS service behind it, with a large hiring pool who know it.
- Global footprint of regions and availability zones makes genuine high availability and data residency achievable without owning hardware.
- Fine-grained control over networking, identity and compute that simpler platforms simply cannot offer.
- Deep managed services (RDS, DynamoDB, SQS, EKS) remove a great deal of undifferentiated operational work when used well.
Trade-offs
- Costs can run away quietly. Egress charges, idle NAT gateways, over-provisioned instances and forgotten resources add up, and the pricing model is complex enough to hide them.
- The learning curve is steep and the service sprawl is real. There are usually three ways to do anything and the right one is rarely obvious from the docs.
- Vendor lock-in is genuine. Lean on DynamoDB, Lambda, SQS and IAM and you are committing to AWS; leaving later is a project, not a config change.
- The defaults are not always safe or economical. Getting IAM, VPC and cost governance right is work you cannot skip, and AWS will not do it for you.
Architecture
We start from the workload and the constraints, not from a favourite service. The first questions are always the same: what has to be relational, what can tolerate eventual consistency, where does the data have to live, and what is the failure you most need to survive? The answers decide whether compute is EC2, ECS on Fargate, EKS or Lambda — and we are happy to mix them in one system where that is the honest fit.
Everything sits inside a VPC with private subnets by default; databases and internal services never get a public address they do not need. Security groups and IAM roles are scoped tightly, state is externalised to managed services so instances stay disposable, and queues sit between components so a slow downstream does not take the whole system down. We build against the AWS Well-Architected Framework — the five pillars of operational excellence, security, reliability, performance and cost — as a review checklist, not a marketing badge.
All of it is defined as code. We use CloudFormation or the AWS CDK where staying native is an advantage, and Terraform where you want one tool across clouds or already have it. Either way the environment is reproducible from a repository, reviewed like any other change, and never a hand-built snowflake that only one person understands.
Performance
Most AWS performance problems are design problems wearing a performance costume. A chatty service hitting a database across an availability zone, an under-provisioned RDS instance, a Lambda cold-starting inside a VPC, a missing index — these show up as latency but are fixed in the architecture, not by throwing bigger instances at them.
We put CloudFront in front of content that can be cached to cut both latency and egress cost, right-size compute against real CloudWatch metrics rather than guesswork, and use read replicas or DynamoDB where the access pattern justifies them. Autoscaling is tuned to the actual traffic shape so you are not paying for peak capacity around the clock.
We measure before and after. If a change is meant to improve latency or throughput, we show you the numbers from CloudWatch, not a reassuring adjective.
Security
AWS operates a shared-responsibility model: they secure the cloud, you secure what you put in it. Most breaches on AWS are not clever attacks — they are an over-permissive IAM policy, a public S3 bucket, or a long-lived access key committed to a repository. We treat those basics as the job, not the boring part.
IAM is written to least-privilege: roles for services rather than static keys, no wildcard admin handed to application code, and permissions granted for what a component actually does. Secrets live in Secrets Manager or Parameter Store, data is encrypted at rest with KMS and in transit with TLS, and CloudTrail records who did what so an incident can be reconstructed rather than guessed at.
We also close the gaps AWS deliberately leaves open by default — public access blocks on buckets, security groups that do not allow the world in on port 22, and GuardDuty or Config where the compliance need justifies them. Security here is unglamorous and continuous, and that is exactly why it works.
Scalability
Scaling on AWS is straightforward once the architecture cooperates. Stateless compute behind a load balancer scales horizontally through autoscaling groups or Fargate; queues absorb spikes so a burst of traffic becomes a longer queue rather than a cascade of failures; and managed databases scale up, add read replicas, or in the case of DynamoDB and Aurora Serverless scale largely on their own.
The harder part is scaling across availability zones and regions without kidding yourself. Multi-AZ is the sensible default for anything that matters and costs little; multi-region is genuine engineering — data replication, failover, and cost roughly doubling — and we will only recommend it when your availability targets actually require it.
Scale is also a cost event, not just a capacity one. We make sure a traffic spike cannot quietly become a bill spike by setting autoscaling ceilings, budgets and alarms, so growth stays something you can afford as well as serve.
AWS integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We begin with a review of what you have and what you actually need, then design the smallest architecture that meets it — including a cost model, so you can see the running bill before we build. We would rather talk you out of complexity than sell it to you.
Delivery is incremental and defined in code from day one. Infrastructure lands as CloudFormation, CDK or Terraform, changes go through review, and every environment can be rebuilt from the repository. We wire up alarms, logging and budgets as we go, not as a final tidy-up, so the system is operable the moment it is live.
We hand over deliberately. Runbooks, architecture notes and access are yours, and we make sure your team can run the thing without us. If you want us to stay on and operate it, that is a choice you make freely — not a dependency we engineer in.
The service behind it
Delivered throughCloud EngineeringWhat we build with AWS
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use AWS in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Cloud & Infrastructure
Related terms
Why teams choose us for AWS
We operate what we build
We run production systems on AWS ourselves, so our designs are shaped by 3am pages and month-end bills, not by a certification exam. That bias shows up in every decision we make on your behalf.
Senior engineers only
The person designing your VPC and IAM is the person who has debugged them in anger. No juniors learning on your infrastructure, no account manager relaying a specialist you never meet.
Honest about lock-in and cost
We tell you when a managed service commits you to AWS, and when a simpler platform would serve you better. We would rather lose the work than architect something you cannot afford to keep running.
Typical timeline
- 01
Review & design
We assess the workload, constraints and existing setup, then produce an architecture and a cost model you sign off before anything is built.
- 02
Foundations
Accounts, VPC, IAM, budgets and CI defined in code. The unglamorous groundwork that everything else depends on and that most teams rush.
- 03
Build & migrate
We build the workload incrementally, moving traffic in stages where it is a migration, with observability and alarms wired in from the start.
- 04
Handover & operate
Runbooks, documentation and access handed to your team — plus an optional retainer if you want us to keep operating it alongside you.
How pricing works
- We charge for our engineering time, not a margin on your AWS bill — our incentive is to make your cloud spend smaller, not larger.
- Your AWS costs are billed directly by AWS on usage. We give you a cost model up front and set budgets and alarms so there are no surprises on the invoice.
- Typical engagements are a fixed-scope build or migration, or an ongoing retainer for teams who want senior support operating what we have built.
Hire AWS engineers
Need AWS capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire AWS engineersCommon questions
Will AWS be cheaper than what we run now?
Not automatically — AWS is cheaper only when it is designed and governed well. Left unchecked, egress charges, idle resources and over-provisioning make it more expensive than a simpler platform. We build with a cost model, budgets and alarms so the bill is predictable, and we will tell you honestly if your workload would cost less somewhere else.
Should a small team use AWS or a simpler PaaS?
If you are shipping a standard web application with no unusual compliance, networking or scale requirements, a PaaS such as Vercel, Render or Fly.io will usually get you live faster and cost less to run and reason about. AWS earns its complexity when you need real control — private networking, data residency, deep managed services or genuine multi-region. We are happy to steer you to the simpler option when it is the right one.
How do you stop AWS bills getting out of control?
Cost governance is part of the architecture, not an afterthought. We tag resources, set AWS Budgets and CloudWatch alarms, right-size compute against real usage, cap autoscaling, remove idle NAT gateways and forgotten resources, and use the appropriate S3 storage classes. You should learn what something costs from a dashboard, never from the invoice.
Serverless, containers or virtual machines — which should we use?
It depends on the workload. Lambda suits event-driven or spiky traffic where paying per request beats paying for idle servers. Containers on ECS Fargate suit steady web services without the overhead of managing a cluster, and EKS when you genuinely need Kubernetes. EC2 makes sense for full control or licensed software. We often mix all three in one system, choosing per workload rather than by fashion.
Does building on AWS lock us in?
To a degree, yes, and we are upfront about it. Lean on DynamoDB, Lambda, SQS and IAM and you are committing to AWS; leaving later is a project rather than a config change. Where portability matters we favour open standards — PostgreSQL over a proprietary store, containers over provider-specific runtimes, Terraform over CloudFormation — so the decision to stay is yours to make, not one quietly made for you.
Building on AWS?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.