Cloud Cost and AI-Readiness Audit against your real bill
We audit where your AWS or Azure spend leaks, and whether the platform under it can carry AI workloads.
One fixed-scope engagement, run by the team a New York SaaS founder credits with helping cut his AWS costs by 30% or more.
The offer, in full
- Fee
- Fixed scope, agreed before we start
- Duration
- Agreed at scoping
- You receive
- 4 written deliverables
- What we need
- Read access to your cloud billing and architecture
- Typical start
- 2 to 4 weeks from signature
- Ends with
- A working session and the written audit
NDA before any access. Read-only, least-privilege, and agreed with your technical contact before anything starts.
01Track record
Who reads the bill
Engineers who run AWS and Azure for a living read the bill, not a cost tool with a consultant attached to it.
- 120+Projects delivered across 12 countries since 2012
- 100+Senior in-house engineers, six countries
- AWS CertifiedSolutions Architects on the team
- ISO 27001Certified security practice, audited by Quay Audit UK
02Overview
Where the money goes, and whether AI can land here
A cloud cost assessment reads a live AWS or Azure environment against the invoice you actually paid and quantifies where the spend leaks. Ours answers a second question in the same pass: whether the architecture, data and access model could carry AI workloads without the bill running away. Elsewhere the engagement is sold as a FinOps audit. Here it ends in a written report and a plan ranked by saving against effort, because AI compute is expensive and a leaking estate is the worst place to land it.
03The answer
Two audits, one engagement
AWS Cost Explorer and Azure Cost Management report what you spent. A free self-serve assessment costs nothing to run. Neither output is a decision. These three questions produce one: what to stop doing, in what order, and whether the platform can carry what you want to run next.
- Half 01
Where is the spend actually going?
Rightsizing, idle and orphaned resources, storage sitting in a tier it does not need, egress you pay for every night. Quantified against the invoice, not against list prices.
- You get
- A line-item teardown of your real invoice
- Half 02
Could this platform carry AI?
Whether the architecture, the data pipelines and the access model can take an AI workload without runaway cost or a new security gap, and what has to move first.
- You get
- An AI-workload readiness read
- Both
What do we do first?
Findings ranked by saving against effort, so the cheap wins are not stuck behind the expensive ones.
- You get
- A prioritized plan, in order
Take one half or both
If your only question is cost, we run the teardown alone and scope the fee to that. AWS, Azure, or an estate running both.
04Fit
Worth auditing now, or later?
Where the audit pays for itself, and where it does not. The second list names the offer that fits instead.
Audit it if
Good fit- Your cloud bill is growing faster than your usage and nobody can say precisely why.
- AI or data workloads are about to land on infrastructure that was sized for something else.
- The people who set up the account have moved on, and what is running is now partly folklore.
- Finance wants a number and engineering wants headroom, and one document has to answer both.
Wait if
Better elsewhere- The wider question is whether AI belongs in the business at all. Start with the AI Readiness Assessment.
- What is failing is the application rather than the bill. Stabilize it first with Legacy Software Rescue.
- You already know what to change and need people to do it. That is cloud consulting or DevOps, not an audit.
- Your spend is small enough that a week of senior engineering costs more than the savings. Tell us the number on the call and we will say so.
Almost nobody overspends on the thing they think they overspend on. Server sizes get checked first because they are on the first screen of the bill. The money is usually somewhere nobody looks: a copy of the data moving between countries every night, files nothing has opened in two years, a test system someone built at full size and never switched off. You will not see any of that on a diagram. It is in the invoice.
Yaroslav HavrylivSenior Engineering Manager05Deliverables
What the audit hands you
Four documents, yours to keep. The teardown opens with a summary a finance reader can act on, and every finding shows the query or console screen behind it, so it can be checked rather than believed.
- 01
A cost teardown
Where the money goes and where it leaks, against your actual invoice. It opens with a summary a finance reader can act on.
- Format
- Executive summary, then line-item findings
- 02
A prioritized savings plan
Every finding ranked by saving against effort and risk, so the order to work in is already decided.
- Format
- Ranked plan, saving against effort
- 03
An AI-readiness read
Architecture, data pipelines and access model against what an AI workload would actually need from them.
- Format
- Findings log and remediation list
- 04
A FinOps starting point
What to watch, who owns it, and what has to become routine so the savings hold. FinOps calls that phase Operate.
- Format
- Practices, metrics and owners
06Delivery
How the audit runs
Four stages, and the findings start landing in stage two rather than at the end. Nothing is granted before an NDA, and the access we work from is read-only.
- Scope and accessWe agree which accounts, subscriptions and environments are in scope, and whether the AI-readiness half is in. That is what the fee and the timeframe are set against.You getScope and timeframe confirmed in writingFrom youRead access to cloud billing and architecture, and a named owner
- AnalyzeWe map spend against real usage to find the waste, and read architecture, data and security against what an AI workload would demand of them.You getFindings as they land, not at the endFrom youShort interviews with whoever knows how the estate grew
- PrioritizeFindings get ranked by saving against effort, and by what an AI workload needs first, with you in the room for the trade-offs.You getThe order to work in, agreed with youFrom youOne prioritization call, with engineering and finance both present
- Readout and planYou get the written audit and a working session: where the money goes, what to change, and what it takes to run AI here affordably.You getThe written audit and the savings planFrom youA working session with the decision-makers present
07Proof
A client put a number on this one
Savings are easy to claim. These three are on the record.
- The figure belongs to the client, not to usPatrick W., Founder and President of a New York B2B SaaS company, says on Clutch that unicrew helped "reduce our AWS costs by 30% or more". We took over web development for a platform that had been live over a decade and streamlined how it used AWS and Heroku.30%+Lower AWS costs, in the client's own words on Clutch
- What we usually find, before we have seen yoursInfrastructure optimization work tends to land at immediate savings of 20% to 40% for us. Your number depends on how the estate grew: a decade-old SaaS platform and a healthcare platform we stabilized and scaled do not leak in the same places. The audit replaces the range with a figure specific to your bill.20% to 40%Immediate savings, in our delivery experience
- The AI half comes from operating AI, not from reading about itunicrew builds and runs Snaplore and Talkmetry on cloud infrastructure we pay for ourselves, so the readiness questions in this audit are ones we have already answered with our own money.2AI products we build, run and sell on cloud infrastructure
08Case studies
Cloud work with the receipts
An AWS and Heroku bill cut on a live B2B SaaS platform, a migration with DevOps support behind it, and a home-health platform scaled while it stayed up.
See all case studies- SaaSAWS costs down 30%+ for a US B2B SaaS platformunicrew became the programming team for a B2B SaaS platform that had run for over a decade, streamlined how it used AWS and Heroku, and sped up rollouts.30%+Lower AWS costs
SaaSCloud Migration and DevOps Support for a B2B PlatformAzure migration and ongoing DevOps for a B2B CRM platform serving manufacturers and wholesale distributors.- HealthcareStabilizing and Scaling a Home Health Monitoring PlatformA U.S. consumer health tech company needed system stability and an experienced partner. unicrew stabilized the platform, optimized infrastructure costs, and delivered new features, becoming a long-term technology partner.
09Client voices
Clients on the cloud, platform and reliability work
Artelogic has enabled us to develop and roll out products significantly faster than our previous service provider. The team has great knowledge of AWS and Heroku, which has helped us streamline and reduce costs. Artelogic has helped us reduce our AWS costs by 30% or more.
Up until a year and a half ago, I handled all the development and support of our application myself and I needed help. I brought in Artelogic, and it was one of the best business decisions I’ve ever made.
We’re one of unicrew’s smaller clients, but they’re always incredibly quick to respond. When we have issues, they treat them as if they’re extremely important. We feel like we have a partner, and that’s been incredible. That’s why we’ll keep them on for as long as we can.
The Artelogic team finished the project on time and within budget. Their work was of excellent quality, making the cost justified. I was impressed when I learned that despite having a small team, they could cater to our needs and provide a comprehensive solution. Artelogic has been delivering excellent value for our investment.
Artelogic anticipates our needs and meets them before we even identify them. My team lead wants to keep both of Artelogic’s developers on the project until the product’s release. We have a backlog of 40 big projects, so I only see Artelogic’s involvement increasing.
Book the Cloud Cost and AI-Readiness Audit
Tell us whether you are on AWS or Azure, roughly what your cloud spend is, and what prompted the question. We will confirm the scope, the timeframe and which halves you need.
What happens after you contact us
- We reply within one business dayA senior engineer reads what you send, not a bot.
- A short scoping callWhich accounts are in scope, and whether the AI half is in or out.
- Scope and fee, in writingWhat the audit covers, what it costs, and the week we can start.
- NDA, then read-only accessSigned before anything is granted, and billing access is read-only and least-privilege.
10Questions
Questions about the audit
The ones that decide it, answered before you spend a call on them.
Four written documents. The report comes in two halves; the plan says what to do first.
- a cost teardown of where your AWS or Azure spend leaks, against the invoice
- an AI-readiness read on whether the architecture, data and access can carry AI workloads
- a prioritized plan ranking both sets of findings by saving against effort
- a FinOps starting point: what to watch and who owns it
Executing it is a separate engagement: our cloud cost optimization services, or your team from the same document.
Those tools report what you spent. The audit says what to stop doing, and whether the platform can carry what you want to run next.
They are the right first stop and we read from them, along with any third-party cost platform you already run. What none of them read is the architecture behind the number: why the transfer charge exists, whether the storage still has a reader, and what an AI workload would do to both.
Every figure comes from your own billing data at your current usage, not from a benchmark or a model of a company like yours.
Two things move it afterwards: your usage changes, and some findings need engineering time before they pay back, which is why the plan ranks by saving against effort. Where a number rests on an assumption we cannot verify from read-only access, the report says so beside it rather than folding it into a total.
Nobody can put a number on an environment they have not seen; replacing that guess with your own figure is what the audit is for.
Two things we can say. A New York SaaS client says on Clutch that we helped reduce their AWS costs by 30% or more, which is one client's result and not a forecast for yours. And infrastructure optimization work tends to find immediate savings of 20% to 40%, in our delivery experience.
One fixed scope and one number, agreed in writing before anything starts.
What sets it is the size of the estate: how many accounts and subscriptions are in scope, and whether the AI-readiness half is in. A single-account cloud cost assessment and a multi-account estate with an AI read beside it are different pieces of work, so the number comes on the scoping call.
The timeframe is fixed at scoping, once we know how many accounts and how much infrastructure are in scope, and it is in writing before you commit.
We would rather quote a longer window up front than a short one we revise halfway through. Most engagements start within two to four weeks.
Read access to the billing and the architecture: cost and usage reports, the console or portal, and whatever infrastructure-as-code and documentation exists.
NDA before any access. For the assessments and audits we work from read-only, least-privilege access, agreed with your technical contact before anything starts. Read-only is literal: nothing in your accounts is changed, resized or switched off by us.
No. If your only question is cost, we run the teardown alone and scope the fee to that.
Where an AI initiative prompted the review, the readiness half tells you whether the platform can carry it and what running it there would do to the bill. Clients weighing AI usually take both: AI compute is expensive enough that cutting the existing waste comes first.
No. The analysis is ours; what we need from you is access and a few hours of attention.
- a named owner who can arrange read-only access
- short interviews with whoever knows how the estate grew
- one prioritization call, with engineering and finance both in it
- decision-makers in the room at the readout
That comes to between four and ten hours of your team's time in total across our fixed-scope offers, depending on the offer.
Then the report says so, and that is a finding worth having rather than one to pad.
A well-run estate with no orphaned resources and storage that matches how it is read is not unusual, particularly on a platform built recently by people who are still there. The value then moves to the AI-readiness half and to what keeps the estate from degrading as workloads grow, and we will say so at scoping if that is what we expect.
11Where to go next
If cost is not the blocker
The rest of the fixed-scope range, and where an audit finding usually goes next. The whole range sits on one page.
Where AI pays off across the business, rather than whether the platform can host it.
When the AI use case is already decided and what you need is a working pilot on your own data.
For when the review holding up your next deal is about security rather than spend.
A first release built to a fixed scope, when there is nothing running yet to audit.
For when the application on top of the bill is the thing that is actually failing.
The people who implement what the audit finds, on the same platforms they just reviewed.
Pipelines, environments and the automation that keeps a tuned estate from drifting back.
When the finding is that the current platform is the wrong one to keep optimizing.
The pipelines and storage an AI workload needs before it is worth turning on.