Cost-Accountable AWS Cloud Consulting Services

An AWS bill you can explain, on an account that passes review.

Almost nobody calls an AWS consultant because they cannot spell EC2. They call because the bill went up again and nobody can say exactly which decision caused it, or because a customer security questionnaire asked a question about the account that took three days to answer. The technology is rarely the problem. The absence of ownership is.

Cabot's AWS cloud consulting services start by reading your account rather than your architecture diagram, because the two rarely match. We put an owner and a reason against every significant line of spend, separate the security findings that are urgent from the ones that are merely untidy, and deliver the change as infrastructure as code with a rollback for each step. AI runs through that work in defined stages, under review rules we state plainly further down.

Most engagements open with an assessment you can act on whether or not you continue with us, which is the point. Tell us what the account is running and what the question was that sent you looking, and we will come back with what we find and what we would do about it.

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84%

of technical leaders say managing cloud spend is the single biggest cloud challenge their organization faces today.

17%

is how far the average organization exceeds its own cloud budget, before any new workload is added.

90%

of surveyed technology professionals now use AI in their work, which is changing what lands in your cloud account and how fast.

What AWS cloud consulting services actually buy you

AWS cloud consulting services are an engagement in which an outside team assesses, designs and changes how your organization runs on AWS, covering migration and architecture, the cost and governance model that keeps spend explainable, and the security and compliance controls that let the account pass a customer or regulatory review.

The definition matters because the market uses one phrase for three different engagements. Some buyers need a migration. Some have already migrated and need a re-architecture, because a lift and shift that was never followed up produces a data center invoice with a cloud logo on it. Some have neither problem and simply have no governance, so the account grows in ways nobody decided. These need different work, different durations and different money.

So the useful question to ask a prospective partner is not which certifications they hold. Every AWS partner holds certifications. It is what they will tell you when the honest answer is that a workload should be left alone. A consultant paid to recommend change will find change. Everything on this page is our answer to that question.

Why AWS spend and AWS risk both grow quietly

These are the six patterns we find most often when we open an account for the first time. None of them is a technology failure, and none is fixed by a one time cleanup.

Nobody owns the line items. Spend is reviewed in total rather than by owner, so an increase is noticed but never attributed. Without a name against each significant line, the conversation stays about the number instead of about the decision that produced it.

The migration was never finished. Workloads were lifted as they were, the re-architecture was scheduled for later, and later did not arrive. You now pay cloud prices for a design that was drawn for hardware you owned.

Capacity sized for the estimate, not the demand. Instances were provisioned against the original projection and never revisited against what actually runs. Bursty work sits on always on capacity because that was easier at the time and nobody rechecked it.

Security findings arrive as a questionnaire. A customer asks who can access what, and the answer takes days to assemble because it lives across several accounts and nobody maintained it. The control may well exist. The evidence does not.

Keys and access grew without a policy. Encryption keys are scattered across accounts, rotation was never scheduled, and no single person can say who is able to decrypt a given store. This is the question auditors open with.

Every change feels too risky to make. Savings are identified and never taken because nobody is confident about the blast radius. Without rollback and infrastructure as code, doing nothing is the rational choice, and the waste becomes permanent.

Placeholder illustration for cost accountable AWS cloud consulting services

The AWS work we do

Three service lines, and most engagements touch all three because they are the same resources viewed from different angles. Cost work that ignores security creates findings. Security work that ignores cost creates a bill nobody approved.

Two further capabilities run inside engagements rather than as separate lines. Release engineering makes cloud change routine rather than an event, and where that discipline is the actual need it runs through our DevOps practice. And where the software running on the platform is what slows you down rather than the platform itself, that work sits with application modernization instead.

Migration and modernization

Moving workloads to AWS, and more often fixing a move that already happened. A lift and shift that was never followed by a re-architecture is the most common reason an AWS bill looks like a data center invoice with a cloud logo on it. We say which workloads deserve rebuilding and which should be left alone.

Cost and FinOps governance

Tagging, ownership, commitment planning and the rightsizing work that follows. The goal is not a one time cleanup but a bill that stays explainable, where every significant line has a name against it and a reason it is that size this month.

Security and compliance engineering

Identity, network boundaries, encryption key governance and the evidence an auditor asks for. For regulated workloads this includes the business associate addendum, HIPAA eligible service selection, and an answer to who can decrypt protected data that does not require a week of investigation.

What do AWS cloud consulting services cost, and what do they return?

Cost tracks with the size of the account, the number of workloads in scope and whether the engagement ends at a report or continues into the change. An assessment is a fixed piece of work with a fixed price. The change that follows is funded in increments you approve, and the savings are measured against a baseline we agree before starting rather than claimed afterward.

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See the AWS work we do See where AI sits in the work

How we find where your AWS spend and risk actually sit

The assessment answers four questions in a fixed order, because each one changes what the next is worth doing. We read the account, not the documentation, and the gap between the two is usually the first finding.

What is running that nobody owns

Most accounts carry workloads nobody will claim, kept alive because switching them off feels riskier than paying for them. We find them, name an owner, and establish what actually breaks if each one stops.

Whether the architecture matches the workload

Oversized instances and always on capacity for bursty work are the usual finding. We size against observed demand rather than against the original estimate, which is almost always the number that got provisioned.

Where the data sits and who can decrypt it

Keys scattered across accounts with no rotation policy is normal, and it is the question an auditor opens with. We map the key estate and the access paths before recommending anything else.

What breaks if we change it

Every cost saving is a change, and change is where availability goes. We establish the blast radius and the rollback for each recommendation before it reaches a plan you are asked to approve.

What does AI actually change in cloud consulting work?

The honest version is narrower than the marketing. Cloud work is mostly reading: reading a bill that runs to thousands of line items, reading policy documents to find who can reach what, reading logs to find which of those resources anybody actually uses. That reading is where AI genuinely helps, because the volume is large, the patterns are repetitive, and a wrong answer is cheap to catch because the account itself is the source of truth.

So AI drafts the first pass. It clusters spend into probable owners from tags and naming conventions, flags resources with no traffic, summarizes identity policies into who can reach which data, and drafts the infrastructure as code for a change once a person has decided what the change is. Every one of those outputs is checked against the account before it reaches a recommendation, because an inference about your architecture that nobody verified is worse than no inference at all.

It does not decide what to change, sign off a production change, or judge whether a saving is worth its risk. DORA's 2025 research is useful here: AI adoption correlates with higher throughput where engineering discipline already exists, and with worse stability where it does not. In a production cloud account, worse stability is an outage. Digital.ai's 18th State of Agile Report, published in October 2025, found that only 49 percent of organizations have governance guardrails for AI in place, which is why ours sit in the contract rather than on a slide.

Three rules govern every engagement. We are model neutral, choosing tools per task rather than committing your account to one vendor. A named engineer reviews every AI assisted change before it is applied and is accountable for it exactly as if they had written it. And we do not train models on your code, your configuration or your billing data, under any arrangement. No credential, key material or customer data is ever placed in a model prompt.

The AWS services and tooling we work in

We work in your account with your constraints rather than importing a stack of our own. Where your organization already standardizes on tooling, we use it. The table further down sets out where AI sits at each stage of the work, what stays with a person, and which tools are involved.

What we work in:

Compute and platform

EC2

ECS and Fargate

EKS

Lambda

RDS and Aurora

Security, identity and keys

IAM and Identity Center

KMS

CloudHSM

Secrets Manager

GuardDuty

Security Hub

Governance, cost and delivery

Terraform

CloudFormation

Cost Explorer

Organizations

CloudWatch

GitHub Actions

Where AI sits at each stage of an AWS engagement, and where it does not

Each stage below has an output you can inspect and a person who approves it. Nothing an AI drafts is applied to your account without a named engineer accepting it first.

Engagement stage	What AI does	What stays human	Tools used
Account discovery	Clusters thousands of billing line items into probable owners from tags and naming, and flags resources with no observed traffic.	Confirming each owner with a person, and deciding what an untagged workload actually is before anything is proposed.	Claude, Cost Explorer
Security review	Summarizes identity and key policies into plain statements of who can reach which data through which path.	Judging which findings are urgent, which are cosmetic, and what the business risk of each actually is.	Claude, Security Hub
Design	Drafts target architectures and sizing options against observed demand once a direction has been chosen.	Choosing the direction, accepting the trade-offs, and deciding which workloads to leave alone.	Claude, Well-Architected Tool
Change	Drafts Terraform for an agreed change and the rollback path alongside it.	Review before apply, the maintenance window, and the decision to proceed on a production account.	GitHub Copilot, Terraform
Run and report	Groups CloudWatch alarms into probable root causes and drafts the variance narrative for a monthly bill.	Go or no-go on remediation, and what the variance means for next quarter's plan.	Claude, CloudWatch

How a Cabot engagement differs from a typical AWS consultancy

Four dimensions on which AWS engagements succeed or fail, chosen because they are where they go wrong rather than where they are easy to describe. Each row is a mechanism you can hold us to, not a promise.

Dimension	Typical AWS consultancy	Cabot
How savings are reported	Claimed against a projection, with the baseline chosen after the work rather than before it.	Measured against a baseline agreed in writing before any change, so the number is checkable rather than persuasive.
What happens to workloads that are fine	Re-architected anyway, because the engagement is priced on the volume of change.	Left alone, and we say so in the report. Some savings cost more in risk than they return.
Who can decrypt your data	Rarely asked, and the answer takes days to assemble when a customer or auditor asks first.	Mapped during assessment and delivered as a document naming each key, what it protects and who holds decrypt.
What you have when it ends	A slide deck, and a dependency on the consultant to make the next change safely.	Infrastructure as code, runbooks and the governance model in your repository, so your team keeps it.

The organizations we do AWS work for

We work with three kinds of organization on AWS. Each line below says what we have learned about that market, not that we sell into it. Where the landing zone is settled and the work becomes moving workloads onto it, that runs through our cloud enablement team, with release and regression coverage from our QA and testing practice.

Healthcare and regulated products

AWS publishes which services are HIPAA eligible and signs a business associate addendum. What it does not do is configure your account correctly or produce your evidence. That boundary is where most regulated cloud programmes get into trouble, and it is where the work actually sits.

SaaS and product companies

Cost per customer matters more than total spend, and nobody can calculate it because the tagging was never designed for it. Multi tenancy decisions made early determine whether that number is knowable at all later.

Enterprise IT moving off the data center

The hard part is rarely the technology. It is that the operating model, the change process and the funding cycle were all designed for capital expenditure on hardware, and none of the three survives contact with consumption based billing unchanged.

Industries we already understand

volunteer_activism

Healthcare

shopping_cart

Ecommerce

paid

Fintech

apartment

Travel and Tourism

Security, and the encryption key question auditors open with

Security practice leads the work rather than following it. That means least privilege identity rather than broad roles that were convenient during a migration, network boundaries that are documented and enforced, infrastructure defined as code so that a control is a file rather than a memory, and findings separated into what is urgent and what is untidy so the urgent work is not delayed by the cosmetic.

Encryption key governance deserves its own paragraph because it is the area we most often find unmanaged, and it is where a review starts. In a typical account, keys have accumulated across several accounts and regions, rotation was never scheduled, aliases do not describe what they protect, and no single person can answer who is able to decrypt a given data store. We map the key estate first: which keys are customer managed and which are AWS managed, what each one actually protects, who holds decrypt permission through which policy path, and what happens operationally if a key is scheduled for deletion. From there we set a rotation policy, separate the keys that protect regulated data from those that do not, and where an enterprise customer requires you to bring your own key we build that in rather than retrofit it under contract pressure. The output is a document naming who can decrypt what, which is the thing you were asked for.

Regulatory obligations are scoped to your market. For healthcare workloads that means selecting from AWS HIPAA eligible services, a business associate addendum in place, and evidence produced as the work happens rather than assembled before an audit. Our compliance practice covers the wider programme. The boundary worth stating plainly is that AWS secures the cloud and you remain responsible for what you run in it, and most regulated cloud trouble lives exactly on that line.

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Customer managed encryption keys

Key rotation policy

Least privilege IAM

HIPAA eligible services only

Well-Architected review

Infrastructure as code

How an AWS engagement actually runs

Three stages, each ending in a decision you make rather than a document you receive. The first stage stands on its own, and you can stop there with something useful in hand.

Assessment and baseline

We read the account rather than the architecture diagram, because the two rarely match. You end this stage with an inventory that has owners against it, a ranked list of what is costing you and why, and the security findings separated into what is urgent and what is merely untidy. You decide what gets acted on.

Change in funded increments

Work lands as infrastructure as code with a rollback for each change, in increments you approve. Cost work and security work run together rather than as separate programmes, because they touch the same resources and doing them apart means touching production twice.

Run, measure and hand over

Savings are measured against the baseline rather than claimed, and the governance that keeps them is handed to your team with the runbooks. The test of this work is whether the bill is still explainable six months after we leave.

Who does the work and what each person owns

Accountability is explicit from the first day. A cloud architect owns the target design and answers for it. An engineer owns the infrastructure as code and the rollback path for every change that reaches your account. A security engineer owns the findings and their evidence. A delivery lead owns sequencing and keeps your stakeholders informed between checkpoints. Every decision has a name against it and that person is reachable.

Our depth shows in four specific places. We are strong at cost work on accounts where the tagging was never designed, which is most of them. We are strong at regulated workloads, where service selection and evidence matter as much as architecture. We are strong at finishing migrations that stalled after the lift, because the second half is where the return was supposed to come from. And we are strong at handing the result over so your team keeps it, which is the part that decides whether the bill is still explainable a year later. We do not claim equal depth in everything, and we will say when a specialist fits better.

On working arrangements, a distributed team commits to a written overlap window with yours, and changes to a production account are scheduled with you rather than around you. Decisions are recorded in the repository alongside the code that implements them, so the reasoning survives a rotation. When the work needs more hands, you can add engineers to the team already running rather than starting a second engagement from nothing.

Why technology leaders choose Cabot for cost-accountable AWS cloud consulting

Savings are easy to promise and hard to keep. These are the six reasons buyers give when they explain why they moved their AWS work to Cabot.

Your AWS bill becomes explainable

Every significant line gets an owner and a reason it is that size. Savings matter, but a bill you can explain to a CFO is what stops the same problem returning next year.

Regulated workloads are the normal case

HIPAA eligible service selection, key governance and audit evidence are routine here rather than a specialism we bolt on. Our compliance practice covers the wider programme.

We say when the answer is do nothing

Some workloads should not be re-architected, and some savings cost more in risk than they return. An adviser whose fee depends on finding work will find work. We will tell you when the current design is fine.

AI in delivery with the review rules written down

We use AI in defined stages of the work and say exactly where it does not touch anything, with a named engineer accountable before any change reaches your account. We do not train models on your code or your configuration.

The handover is the deliverable

Runbooks, infrastructure as code and the governance model go to your team, because an engagement that leaves you dependent on the consultant has moved the problem rather than solved it.

Delivery discipline around the change

Cloud change is release engineering, so it runs with the same pipeline rigor as product work. Where that discipline is what you actually need, it sits with our DevOps practice.

Where to go next, depending on what is in your way

AWS consulting is the platform layer. Pick the card below that matches the problem actually in front of you.

The platform is fine and the application is the problem

If the infrastructure holds and the software on top is what slows you down, the work is modernization. Start at application modernization.

Releases are the bottleneck, not the infrastructure

If the account is sound and shipping is what hurts, the constraint is the pipeline and the change process. Start at DevOps in healthcare.

You are building a clinical product on top of this

If the cloud work exists to support a patient or clinician facing product, scope both together. Start at healthcare application development.

Our Clients

Questions technology leaders ask before starting

What are AWS cloud consulting services?
They are an engagement in which an outside team assesses, designs and changes how your organization runs on AWS. In practice that covers three things that are often confused: migrating or re-architecting workloads, putting a governance model around cost so spend stays explainable, and building the security and compliance controls that let the account pass a customer or regulatory review. Most organizations need more of one than the other two.
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How much do AWS cloud consulting services cost?
An assessment is a fixed scope with a fixed price, sized by how many accounts and workloads are in it. The change work that follows is funded in increments you approve, so there is a real stopping point. You can model a range with our cost calculator. Be wary of any engagement priced as a share of savings, because it creates an incentive to find change whether or not change is warranted.
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Why does our AWS bill keep going up?
Usually three causes together: resources nobody owns that stay running because switching them off feels risky, capacity sized against the original estimate rather than observed demand, and a migration that lifted workloads without the re-architecture that was supposed to follow. None of these is visible in a total. They are visible when every significant line has an owner against it.
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Can AWS be HIPAA compliant?
AWS publishes a list of HIPAA eligible services and will sign a business associate addendum, which covers their side. It does not make your account compliant. Configuration, access control, key governance, logging and evidence remain yours. Almost all regulated cloud trouble sits exactly on that boundary, which is why we scope it explicitly rather than assuming it.
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Who can decrypt our data in AWS, and how do we prove it?
That answer lives in your key policies, grants and IAM paths combined, which is why it usually takes days to assemble under pressure. We map the key estate during assessment: which keys are customer managed, what each protects, who holds decrypt permission through which path, and what breaks if a key is scheduled for deletion. The output is a document you can hand to an auditor rather than a query someone has to run.
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Do we need AWS KMS or CloudHSM?
For most healthcare and enterprise workloads, KMS with customer managed keys and a rotation policy is sufficient, and it is far cheaper to operate. CloudHSM earns its cost when you have a specific requirement for single tenant hardware or exclusive key custody, which is usually driven by a contract or a regulator rather than by the architecture. We will tell you which case you are in rather than defaulting to the more expensive one.
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How long does an AWS assessment take?
Typically two to four weeks depending on how many accounts are in scope and how quickly read access is granted, which is more often the constraint than the analysis. You get the inventory, the ranked findings and the recommendation at the end of it, and it is written to be useful whether or not you continue with us.
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Do you use AI in this work, and does it touch our account?
AI drafts the reading heavy parts: clustering spend, summarizing policies, drafting infrastructure as code once a person has decided the change. It never applies anything. A named engineer reviews every change before it reaches your account. We do not train models on your code, configuration or billing data, and no credentials or key material are ever placed in a prompt.