Cloud & Infrastructure
AWS Consulting & Engineering
The broadest cloud on the market, and the one most likely to be used badly. We help you pick the tenth of it you need, run that tenth properly, and understand the bill before it arrives.
Overview
AWS is the oldest and largest of the public clouds, and both facts show in the product. There are more than two hundred services, several defensible ways to run the same workload, dozens of regions, and a pricing model detailed enough to be a discipline in its own right. That breadth is genuinely valuable once you can navigate it: you can run virtual machines, containers or functions, store objects, blocks or rows, put a queue or an event bus between components, and reach almost anywhere on earth from a single account with a single identity model. It is also the reason so many teams end up lost. Most of AWS is not for you. Knowing which small subset applies to your workload, and then using that subset properly, is the majority of the skill.
It helps to understand the shape of the platform before the services. An AWS account is a hard boundary: it is the unit of billing, the unit of blast radius, and the container for identity. Regions are independent geographic deployments with their own service availability and their own prices, and inside a region there are multiple availability zones, which are physically separate data centres close enough together for synchronous replication. That geometry decides a surprising amount. Whether your database can fail over without data loss, whether traffic between two components crosses a chargeable boundary, whether a bad deployment in a test environment can reach production, and whether a regulator will accept where the data sits, are all answered by account and region layout rather than by which compute service you chose.
The second thing worth understanding early is that AWS services sit on a spectrum from portable to deeply proprietary, and where you land on that spectrum is a choice you make repeatedly rather than once. Running PostgreSQL on RDS is barely a commitment: it is standard PostgreSQL with the backups and patching handled, and you could move it to another provider or a machine in a cupboard with a dump and a restore. Building on DynamoDB, Lambda, Step Functions, EventBridge and IAM together is a much deeper commitment, and a good one in the right circumstances, but you should make it knowing that leaving becomes a project rather than a configuration change. Neither position is wrong. What is wrong is arriving at the deep end by accident because every tutorial pointed that way.
The third thing is cost, and it deserves to be treated as an architectural property rather than a finance problem. AWS bills for compute time, storage volume, requests, and, critically, for data moving in directions people rarely draw on a diagram: out to the internet, between availability zones, through a NAT gateway, across a region boundary. It also bills relentlessly for things that are switched on and idle. An unattached volume, a load balancer with nothing behind it, a NAT gateway serving a subnet nobody uses, a non-production environment running through the night and the weekend. None of these produce an alert. They produce a line item that looks like it has always been there, and the total climbs quietly while everybody assumes the growth is usage.
We design, build and operate production systems on AWS, and we bring an operator’s bias to every decision on it. That means we care what a service costs at three in the morning under load, what happens when an availability zone goes away, who can reach the data and how you would find out if the wrong person did. We are not a reseller and we earn nothing from your consumption, which means we have no reason to talk you into a larger architecture than you need. Quite often the most useful thing we say on an AWS engagement is that a managed database and five well-chosen services will serve you better than the elegant event-driven design somebody sketched on a whiteboard, or that your workload does not belong on AWS at all.
Best for: Teams whose workload has real constraints on control, scale, residency or traffic shape, and who have (or want us to bring), the discipline to run the dominant cloud safely and affordably.
Why teams choose AWS
Breadth, used narrowly
The value of AWS is having the right service available when you need it, not using many of them. We map your workload to the handful that genuinely fit, commonly ECS or EC2, RDS, S3, CloudFront and a queue, and deliberately leave the other one hundred and ninety alone. Fewer moving parts means fewer things to secure, patch, monitor and pay for, and a system your team can hold in their heads.
A cost model before the first invoice
Every design we produce comes with an estimate of what it will cost to run at your expected volume, including the parts people forget: data transfer, NAT gateway processing, log ingestion and retention, and the non-production environments. You should never learn what a service costs by reading the bill, and you should never be surprised by the second month.
Failure survived rather than hoped over
Multi availability zone deployment, health-checked failover, queues between components so a slow dependency becomes a longer queue instead of a cascade, and backups that have actually been restored in a rehearsal. The difference between an architecture that is theoretically resilient and one that is resilient is testing, and we do the testing.
An environment your team can operate
Infrastructure defined in code and reviewed like code, alarms that page a human only for symptoms a human should act on, structured logs you can search, and runbooks written for somebody who did not build it. The test is whether the environment can be rebuilt from the repository rather than from the memory of one engineer.
Why businesses choose AWS
- You want an operator rather than a reseller. Advice about what to run, and just as often what to avoid, from people whose incentive is to make your bill smaller rather than larger.
- You need cost treated as a first-class architectural property, modelled during design and monitored afterwards, not discovered when the finance team asks a question nobody can answer.
- You want least privilege in IAM and a genuine grasp of the shared responsibility model rather than a working demo that quietly leaves an administrator key in an environment variable.
- You want infrastructure defined in code and handed over properly, so your team can operate the environment without depending on us indefinitely.
- You want somebody willing to tell you that a simpler platform, or a smaller architecture on AWS, is the better answer for where you actually are.
What we build with AWS
The capabilities this technology is genuinely strong at, and what we most often build with it.
Compute: EC2, ECS, Fargate, EKS and Lambda
We choose the compute model per workload rather than picking one and forcing everything into it. EC2 where you need full control of the machine or run licensed software with an awkward agreement. ECS on Fargate for steady containerised web services where you want containers without operating a cluster, which is the right answer far more often than people expect. EKS only where Kubernetes genuinely earns its operational cost. Lambda for event-driven and spiky work where paying per invocation beats paying for idle capacity. Mixing several in one system is normal and correct.
Storage: S3, EBS and EFS
S3 for durable object storage, static assets, backups and data lakes, with lifecycle rules and the right storage class so cold data is not billed at Standard rates for years. EBS for block storage attached to instances, sized and typed to the actual I/O profile rather than the default. EFS where several hosts genuinely need a shared filesystem, which is less often than it is reached for. The recurring waste here is snapshots and unattached volumes that outlive whatever created them.
Databases: RDS, Aurora and DynamoDB
RDS or Aurora for PostgreSQL and MySQL when you want relational guarantees, managed backups, point-in-time recovery and multi availability zone failover without operating any of it. DynamoDB when you need predictable single-digit millisecond key-value access at scale and can genuinely model around its access patterns up front. We are candid about the second one: DynamoDB is superb for the shape it fits and painful when a relational access pattern is forced into it later, and that mistake is expensive to reverse.
Networking and delivery: VPC, CloudFront and Route 53
Private-by-default VPC design with public and private subnets, security groups written narrowly and as code so their purpose is visible in review, and no database or internal service holding a public address it does not need. CloudFront in front of static and dynamic content to cut both latency and egress cost, since cached bytes are cheaper bytes. Route 53 for DNS with health-checked failover. This layer is also where a large share of unexplained cost lives, which is why we design it deliberately rather than accepting the default VPC.
Messaging and events: SQS, SNS and EventBridge
SQS between components so a burst of traffic becomes a longer queue rather than a cascade of timeouts, with dead-letter queues so failures are inspectable instead of silent. SNS and EventBridge for fan-out and event routing where several consumers care about the same thing. These are the joints that let a system degrade gracefully, and adding them early is far cheaper than retrofitting them after the first bad afternoon.
Identity: IAM, Organizations and IAM Identity Center
Roles rather than long-lived access keys, application permissions scoped to what the component actually does, and human access federated to your existing identity provider so joiners and leavers are handled in one place. Multiple accounts under an organisation with service control policies as guardrails, so production is separated from everything else by a boundary rather than by convention. Continuous integration authenticates through OpenID Connect federation instead of holding a permanent key in a secrets store.
Observability: CloudWatch, CloudTrail and cost tooling
Metrics, structured logs and alarms tuned so a page means something, distributed tracing where request paths are complex enough to need it, and CloudTrail retained so an incident can be reconstructed rather than guessed at. On the cost side, a tagging standard enforced in code, Cost Explorer and budgets configured to the drivers rather than the total, and anomaly alerting so an unexpected shape in spend is noticed within days rather than at the end of the month.
Infrastructure as code: Terraform, CDK and CloudFormation
We use Terraform or OpenTofu where you want one tool across providers or already have it, and the CDK or CloudFormation where staying native is a genuine advantage. Either way the environment exists because a repository says it should, changes go through review with a plan you read before it is applied, and a console change is treated as drift to reconcile rather than as normal operations. This is what makes an AWS account safe to change.
Use cases
Moving off a single overloaded server
The most common AWS engagement we see. A monolith on one struggling virtual machine, backups nobody has restored, and downtime whenever anything is patched. The move is to a managed RDS database, an autoscaling group or an ECS service behind a load balancer, S3 and CloudFront for assets, and infrastructure code so the whole thing can be rebuilt. It buys headroom and removes single points of failure without a rewrite, which matters because a rewrite is exactly what a team in that position cannot afford.
Event-driven and spiky backends
Workloads where traffic is uneven enough that keeping servers warm is mostly paying for silence. Lambda, API Gateway, SQS and DynamoDB fit this shape well, scale without intervention, and cost close to nothing when idle. The honest counterpart is that the same architecture becomes expensive and awkward for steady high-throughput work, so we make the traffic shape an explicit input to the decision rather than a discovery.
Data pipelines and analytics
S3 as the landing zone with lifecycle rules, and a processing layer that turns raw events into queryable data without standing up and babysitting a cluster you need for two hours a day. This is one of the areas where AWS breadth pays for itself, and also one where costs escalate fastest if storage classes, retention and query patterns are not designed on purpose.
Regulated and residency-constrained systems
Systems where data must demonstrably stay in a particular jurisdiction, where private networking is a requirement rather than a preference, and where an auditor will ask how access is controlled and evidenced. AWS can express all of this precisely, which is often the reason a simpler platform is off the table, and region and account structure become the first decisions rather than an afterthought.
Taking an inherited account in hand
An estate built by somebody who has left, with resources nobody can account for, permissions granted broadly to make something work once, and a bill that grows faster than usage. The work is inventory, then infrastructure code, then careful removal of what is confirmed dead, then tightening access in stages. Deliberately unexciting, and it is what turns an account people are nervous about into one they can change.
Multi-region availability when it is genuinely required
Systems whose availability targets are measured in minutes rather than hours, or where a regulator requires geographic separation. We build this when it is warranted and argue against it when it is not, because multi-region brings continuous replication, hard consistency trade-offs, roughly duplicated infrastructure and a permanent operational burden that most businesses are not actually buying anything with.
When AWS is the right choice
- Right when you need breadth under one roof: managed relational and key-value databases, object storage, queues and event buses, container orchestration and serverless compute, all under one account structure, one identity model and one bill.
- Right when you have real constraints a simpler platform cannot express. Private networking with no public path to your data, data residency pinned to a specific region, dedicated tenancy, VPC peering or a private link to a partner, or a compliance regime that requires you to evidence exactly where things run.
- Right when the workload is genuinely uneven. Traffic that spikes hard and falls away, batch processing that needs a hundred machines for twenty minutes a day, or an event-driven system where paying per request is dramatically cheaper than keeping servers warm for a peak that arrives twice a week.
- Right when you expect to run across multiple availability zones or regions and want mature, well-documented tooling for autoscaling, failover, replication and observability rather than building that machinery yourself.
- Wrong for a small team shipping a conventional web application with no unusual constraints. A platform-as-a-service such as Vercel, Render, Fly.io or a well-run managed container service will get you live sooner, cost less to run at that size, and cost far less to reason about. We will say so.
- Wrong when nobody will own cost and security. AWS gives you enough freedom to leave a bucket readable, hand an application wildcard administrator permissions, or triple your bill with one misconfigured autoscaling group. Without a named owner, that freedom is a liability rather than a feature.
- Wrong when the real driver is that a competitor uses it or an investor expects to see it on a slide. That is not an engineering requirement, and the complexity you take on will be paid for by your engineers every week thereafter.
AWS: pros and cons
Strengths
- Unmatched breadth and depth. Almost any workload has a mature, heavily documented AWS service behind it, and the pool of engineers who have used it in anger is the largest of any cloud.
- A global footprint of regions and availability zones that makes genuine high availability and specific data residency achievable without owning hardware or negotiating with a data centre.
- Fine-grained control over networking, identity and compute that simpler platforms deliberately do not offer, which is exactly what regulated and unusual workloads need.
- Deep managed services, particularly RDS and Aurora, S3, SQS and CloudFront, which remove a great deal of undifferentiated operational work when they are used for what they are good at.
- A strong infrastructure-as-code story through CloudFormation, the CDK and Terraform, so an entire environment can be described, reviewed and reproduced rather than assembled by hand.
- Long-lived APIs and a strong record on backward compatibility. AWS rarely takes things away, which matters when you are betting a decade of a business on the platform.
Trade-offs
- Costs run away quietly, and the mechanism is nearly always the same. Data egress to the internet, cross availability zone traffic, NAT gateway processing charges, over-provisioned instances chosen at launch and never revisited, and forgotten resources from a project that ended. None of these announce themselves, and the pricing model is complicated enough to hide all of them.
- Vendor lock-in is real and it is graduated. RDS PostgreSQL barely commits you to anything, but a system built on DynamoDB, Lambda, Step Functions, EventBridge and IAM together is an AWS system. Leaving becomes a rewrite of the integration surface, not a redeploy, and you should choose that knowingly rather than drift into it.
- IAM is powerful and genuinely difficult. Identity-based policies, resource-based policies, permission boundaries, service control policies, assumed roles, trust relationships and condition keys interact in ways that are hard to reason about, and the failure modes are asymmetric. Too tight and something breaks noisily, too loose and nothing happens at all until it matters enormously.
- The learning curve is steep and the service sprawl is real. There are usually three ways to do anything, the documentation describes all three neutrally, and the right choice for your situation is rarely obvious from the docs alone.
- The defaults are neither economical nor especially safe. Default VPC layouts, default retention on logs, default instance families and default permissions are starting points, not recommendations, and AWS will not tighten any of them on your behalf.
- Support is a paid tier, and the useful tiers are not cheap. Without one, a genuine platform-level problem is a forum post and a wait, which is a poor position to be in during an incident.
Designing on AWS
We start from the workload and the constraints, never from a favourite service. The opening questions are always the same. What has to be relational and transactional, and what can tolerate eventual consistency. Where does the data legally have to live. What is the failure you most need to survive, and how long can you be down while surviving it. What does traffic actually look like across a day and across a week. Those answers decide whether compute is EC2, ECS on Fargate, EKS or Lambda, and it is entirely normal for one system to use two or three of them for different parts of its job.
Account and region structure comes before anything is built, because it is the decision that is cheapest now and most expensive later. Production sits in its own account so a mistake in a test environment cannot reach customers, guardrails are applied centrally at the organisation level, and human access is federated with roles rather than issued as individual credentials. Region choice is settled explicitly against residency requirements, latency to your users, and the fact that service availability and pricing both vary by region, which surprises people who assumed the platform is uniform.
Inside the network, everything is private by default. Databases and internal services do not get a public address, subnets are laid out so the traffic paths are deliberate, and security groups are written narrowly and reviewed like code. State is externalised to managed services so compute instances stay genuinely disposable, and queues sit between components so a slow downstream produces a backlog rather than a cascade. This is also where cost design happens, because cross availability zone chatter and NAT gateway processing are architectural choices that arrive as a bill.
We use the Well-Architected Framework as a review checklist rather than a badge. Its five pillars, operational excellence, security, reliability, performance efficiency and cost optimisation, are a useful structure for arguing with your own design before production does it for you. And all of it is defined in code, so the environment is reproducible from a repository, reviewable in a pull request, and never a hand-built configuration that exists only in one person’s memory and a browser history.
Where AWS latency actually comes from
Most AWS performance problems are design problems wearing a performance costume. A chatty service making twenty round trips to a database across an availability zone boundary, an under-provisioned RDS instance with no read replica, a Lambda cold-starting inside a VPC, a query with no index behind an endpoint that is called on every page load. These present as latency and are fixed in the architecture, not by moving to a larger instance, and the larger instance is the expensive way to postpone the diagnosis.
So we measure before we change anything. Real CloudWatch metrics and traces to find where the time is actually going, then the specific fix: CloudFront in front of anything cacheable, which cuts latency and egress cost at once; read replicas or a caching layer where the read pattern justifies them; connection pooling where a serverless compute layer is exhausting database connections; right-sized instances chosen against observed utilisation rather than the shape somebody guessed at launch. Autoscaling is then tuned to the actual traffic curve so you are not paying for peak capacity around the clock, and the scaling metric is validated as one that genuinely tracks load rather than one that merely correlates with it on a good day.
Two AWS-specific effects are worth naming because they catch people repeatedly. The first is that placement matters: components that talk constantly should be close, and traffic crossing an availability zone boundary costs both milliseconds and money. The second is that many managed services have quotas and burst behaviours, from EBS credits to Lambda concurrency to API Gateway throttling, and a system can perform beautifully in testing and then hit a ceiling under real load. We check the limits that apply to your design before you find them the hard way.
And we show the numbers. If a change is supposed to improve latency or throughput, you get a before and after from the metrics rather than a reassuring adjective.
The shared responsibility model, taken seriously
AWS secures the cloud; you secure what you put in it. That division is clear in writing and consistently misunderstood in practice, and the misunderstanding is why most incidents on AWS are unglamorous. They are an over-permissive IAM policy that granted everything because narrowing it was fiddly, a storage bucket made public to unblock a deployment, a long-lived access key committed to a repository, or a management port open to the world on a security group somebody created in a hurry. Almost none of it is a clever attack. We treat those fundamentals as the actual job rather than as the boring part before the interesting work.
IAM is written to least privilege, and we accept that this takes longer. Roles for services rather than static credentials, permissions scoped to what a component genuinely does, permission boundaries and service control policies where an organisation needs a hard ceiling, and no wildcard administrator handed to application code because it was quicker at the time. Human access is federated to your identity provider with multi-factor authentication so leavers are removed in one place. Where an existing estate is already too permissive, we tighten iteratively using actual access data, because a single aggressive pass usually breaks an undocumented dependency and gets reverted wholesale, leaving you worse off than before.
Data is encrypted at rest with KMS and in transit with TLS, with key policies written deliberately rather than inherited. Secrets live in Secrets Manager or Parameter Store and are injected at runtime, never baked into an image or an environment file in a repository. CloudTrail is enabled and retained so an incident can be reconstructed rather than reasoned about from memory. We turn on the protections AWS deliberately leaves off, including account-level public access blocks on S3, and add GuardDuty, Config or Security Hub where the compliance requirement justifies the cost of the noise they generate.
One boundary worth stating plainly: none of this substitutes for an independent assessment or a formal certification, which require a qualified assessor rather than the team that built the environment. What we can give you is a posture that is defensible, evidenced and consistently applied, which is what an auditor or an enterprise customer’s security questionnaire is actually looking for.
Scaling, and the bill that comes with it
Scaling on AWS is mostly straightforward once the architecture cooperates. Stateless compute behind a load balancer scales horizontally through autoscaling groups or Fargate services. Queues absorb spikes so a surge becomes a backlog rather than a set of cascading timeouts. Managed databases scale vertically, add read replicas, or in the case of Aurora Serverless adjust largely on their own. The work is in making the application genuinely stateless, which is usually where the real effort sits: session state in a shared store, uploads in S3 rather than on a local disk, no assumption that consecutive requests reach the same machine.
Multi availability zone is the sensible default for anything that matters. It costs relatively little, it survives the failure mode that actually happens, and it is how RDS failover and load balancer health checks are designed to be used. Multi-region is a different order of commitment: continuous data replication, genuine consistency trade-offs, near-duplicated infrastructure and a permanent operational burden. We recommend it when availability targets are measured in minutes or a regulator requires geographic separation, and we argue against it the rest of the time, because the cost is paid every month and the benefit is exercised almost never.
The part teams underestimate is that scale is a cost event as much as a capacity one. An autoscaling group with no ceiling will happily scale into a bill nobody approved, a retry storm can multiply your own traffic against yourself, and a spike in requests to a per-request service is a spike in spend with no natural brake. So we set autoscaling maximums deliberately, put budgets and anomaly alerts on the drivers rather than the total, and design backpressure into the system so that a bad day degrades gracefully instead of expensively.
There is also a ceiling worth planning around: service quotas. Concurrency limits, instance counts per region, network interfaces, API rate limits. Most are raisable on request, but the request takes time you do not have during an incident, so we identify which quotas your design will approach and get them raised before growth arrives rather than during it.
AWS integrations & ecosystem
The technologies we most often pair with it. Each links to how we work with it.
How we work on AWS
We start with an assessment, and it is real engineering rather than a sales exercise. We inventory what is running, map how it connects, break the cost down by driver, review identity and network posture, and establish what the business actually needs on availability, recovery and compliance. The output is a written picture of the current state and a prioritised plan with reasoning attached, and it stands on its own: quite often the assessment is the most valuable thing we deliver, because it turns a general unease about the account into a list somebody can act on.
For a new build, the same discipline runs forwards. We design the smallest architecture that meets the constraints, produce a cost model for it before anything is created, and get both signed off. For an existing estate, we make it legible first, importing what is there into infrastructure code with state managed properly, and only then start improving. We do not change what we cannot describe, because an environment that has grown organically is holding up a business and the fastest route to an outage is tidying before understanding.
Delivery is incremental and in code from the first day. Infrastructure lands as Terraform, OpenTofu, CDK or CloudFormation, every change goes through review with a plan attached, and alarms, logging and budgets are wired in as we go rather than as a final tidy-up. Each step is small enough to reverse. Cost changes are quantified before and measured after, resilience is agreed against explicit recovery objectives and then rehearsed rather than assumed, and access tightening is staged so it does not take out an undocumented dependency on a Friday afternoon.
We hand over deliberately. Diagrams, decision records, runbooks written for somebody who did not build the system, a walkthrough with your team, and the repository in your hands. If you want us to stay and operate it alongside you, that should be a choice you make freely rather than a dependency we engineered 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
Weighing up AWS?
A short call with engineers who build in it and operate the result. If AWS is the wrong tool for what you are doing, we would rather tell you now than bill you later.
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
AWS compared
Why teams choose us for AWS
We operate what we build
We run production systems on AWS ourselves, which is why our designs are shaped by three in the morning pages and month-end invoices rather than by an exam syllabus. It shows up as an insistence on tested backups, alarms that mean something, and a cost model produced before the architecture is committed to.
Senior engineers only
The person designing your VPC, your IAM policies and your failover is the person who has debugged all three under pressure. No juniors learning on your infrastructure, and no account manager relaying answers from a specialist you never speak to.
No margin on your bill
We charge for engineering time and earn nothing from your consumption. Everything runs in accounts you own, billed to you directly at AWS prices. That is why we can recommend a smaller architecture, a cheaper storage class or a simpler platform without arguing against our own interests.
Honest about lock-in and about AWS itself
We will tell you which services commit you to AWS and how deeply, and we will tell you when a platform-as-a-service or a couple of well-run machines would serve you better than any of this. We would rather lose the work than architect something you cannot afford to keep running.
Typical timeline
- 01
Assessment and design
One to two weeks reviewing the workload, constraints and any existing estate, then producing an architecture and a cost model you sign off before anything is built. For an existing account this is also where the inventory and dependency map happen.
- 02
Foundations
Account structure, organisation guardrails, VPC and network layout, federated identity and IAM roles, budgets, alarms and the CI pipeline, all defined in code. The unglamorous groundwork everything else depends on, and the part most teams rush and later unpick.
- 03
Build or migrate
The workload built incrementally, or moved in stages where it is a migration, with observability and alarms wired in from the start rather than added afterwards. Cutover is sequenced so each step is reversible, and data migration gets the most planning because it carries the most risk.
- 04
Harden, tune and hand over
Access tightened in stages using real usage data, backups restored in a rehearsal, failover triggered on purpose at least once, autoscaling and instance sizing tuned against observed load, then documentation, runbooks and the repository handed to your team.
How pricing works
- A paid assessment first, as a fixed-scope piece of work: inventory, dependency map, cost breakdown by driver, identity and network review, resilience gap analysis, and a prioritised roadmap with the expected effect of each item. It is a standalone deliverable and deliberately the cheapest way to find out whether the rest of the work is worth doing.
- Fixed-scope engagements for a defined outcome, such as a migration off ageing infrastructure, a greenfield account and landing-zone build, bringing an existing estate under infrastructure code, or a cost-reduction programme. Quoted once the assessment has established what is actually there, because pricing AWS work without that is guesswork with a number attached.
- A monthly senior engagement where the platform keeps developing alongside your product, with continuity from the people who built it. This suits organisations that need ongoing cloud engineering capacity but not a permanent hire, and it is the arrangement most likely to stop an environment degrading again once it is right.
- Your AWS bill is yours, and this is worth being concrete about. The accounts are in your name, AWS invoices you directly at AWS prices, and there is no reseller margin, no markup and no consumption-based commission anywhere in our arrangement. Third-party tooling and licences are bought on your accounts too. We model the expected running cost during design and monitor it afterwards, so the bill is a forecast rather than a monthly surprise. It also means our incentive is unambiguous: we earn nothing from your spend going up, so when we recommend a smaller architecture we are not arguing against ourselves.
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, and anybody promising otherwise before looking at your workload is guessing. AWS is cheaper when demand is variable, when you would otherwise buy hardware for a peak that occurs rarely, and when managed services remove operational work you are currently paying people to do. It is more expensive when a steady, entirely predictable workload is lifted across unchanged, when data transfer is heavy, or when nobody governs the account and idle resources accumulate. We model the running cost during design, set budgets and alarms, and will tell you plainly if your workload would cost less on a simpler platform or on dedicated hardware.
Should a small team use AWS or a simpler platform?
If you are shipping a conventional web application with no unusual compliance, networking or scale requirements, a platform-as-a-service such as Vercel, Render or Fly.io will usually get you live faster and cost less to run and to reason about. That remains true for longer than most people expect. AWS earns its complexity when you need real control: private networking with no public path to data, a specific data residency position, deep managed services, genuinely uneven traffic, or multi-region availability. We are entirely happy to steer you to the simpler option, and we say so often enough that it is worth stating up front.
How do you stop the bill getting out of control?
By treating cost as part of the architecture rather than as a finance problem discovered later. First attribution: a tagging standard enforced in code so spend can be broken down by product, environment and team, because a bill you cannot decompose is a bill you cannot act on. Then the drivers in order of size: right-sizing against observed utilisation rather than the shape guessed at launch, non-production environments that switch off outside working hours, storage lifecycle rules so cold data leaves hot tiers, removal of idle NAT gateways, orphaned volumes and load balancers with nothing behind them, log retention chosen deliberately, and network design that stops traffic crossing chargeable boundaries unnecessarily. Commitment discounts come last, once the architecture is stable, because committing to a year of an oversized instance is an expensive way to feel efficient.
Serverless, containers or virtual machines?
It genuinely depends on the workload, and mixing them in one system is normal rather than indecisive. Lambda suits event-driven or spiky traffic where paying per invocation beats paying for idle capacity, and becomes expensive and constraining for steady high-throughput work. Containers on ECS with Fargate suit most steady web services and give you the container model without operating a cluster, which is the right answer far more often than the industry conversation suggests. EKS is for when you genuinely need Kubernetes, which is a real and separate decision. EC2 makes sense for full machine control, licensed software or workloads with awkward requirements. We choose per workload against traffic shape, state and team capability rather than by fashion.
Does building on AWS lock us in, and how badly?
To a degree, yes, and the honest framing is that lock-in is graduated rather than binary. Running PostgreSQL on RDS commits you to very little: it is standard PostgreSQL and moving it is a dump and a restore. Object storage is close behind, since the API is widely emulated. The deep end is a system built on DynamoDB, Lambda, Step Functions, EventBridge and IAM together, where leaving means rewriting the integration surface rather than redeploying. That is often the right choice, and we will tell you when it is. Where portability matters we bias towards open standards: PostgreSQL over a proprietary store, containers over provider-specific runtimes, Terraform over CloudFormation. The point is that you decide where on that spectrum to sit, rather than arriving at the deep end because every tutorial pointed there.
Why is our AWS bill full of charges we did not design?
Because AWS bills for movement and for idleness, and neither appears on an architecture diagram. The usual suspects, roughly in order: data transfer out to the internet, traffic crossing availability zone boundaries between chatty components, NAT gateway processing charges on every byte from a private subnet, load balancers with no healthy targets, unattached EBS volumes and snapshots left behind by a migration, non-production environments running through nights and weekends, and CloudWatch log ingestion and retention that nobody ever chose. Individually each looks small. Collectively they are frequently a substantial share of an ungoverned bill, and they are the first place we look.
Do we need multi-region, or is multi-AZ enough?
For most organisations multi availability zone is enough, and the way to settle it is to agree two numbers with the people who own the service: how much downtime is genuinely tolerable, and how much data loss is acceptable. If the honest answers are a couple of hours and close to none, a well-designed multi-AZ deployment with tested backups and a rehearsed rebuild meets that comfortably and at a fraction of the cost. Multi-region active-active brings continuous replication, hard consistency trade-offs, roughly duplicated infrastructure and a permanent operational burden, and it is justified when downtime is measured in minutes or a regulator requires geographic separation. Writing the objectives down first usually ends the argument, because most designs proposed on instinct sit several tiers above what the business actually needs.
We have inherited an AWS account nobody understands. Where do you start?
With an inventory rather than a redesign. We map what is running, trace the dependencies, break down what each part costs, and review who and what can reach the data. Then we bring the existing resources under infrastructure code, importing rather than rebuilding underneath you, so the account stops being tribal knowledge and becomes something reviewable. Only after that do we start improving: removing what is confirmed dead, tightening permissions in stages using real access data, and addressing cost drivers in order of size. It is deliberately conservative, because the fastest way to cause an outage in an account nobody can describe is to start tidying it.
Can you work alongside our existing team?
Yes, and that is usually the better arrangement. We are typically brought in for a specific capability the team does not have in-house, network and IAM design, a migration, a cost programme, or resilience work, and the aim is for your engineers to be able to operate the result. That means changes go through review your team can read, decisions are written down with the reasoning, and the handover includes a walkthrough rather than a document drop. We would rather leave you self-sufficient than turn ourselves into a dependency.
Building on AWS?
Tell us what you are building and where it is stuck. A senior engineer reads it and gives you an honest read on whether AWS is the right fit for the problem, or what we would reach for instead.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.