Cloud Cost and AI-Readiness Audit based on your actual invoice
We run a cloud cost assessment, or FinOps audit, of your AWS or Azure spend and readiness for AI workloads.
Our team helped a B2B SaaS client reduce 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
Cloud engineering experience behind the audit
Our cloud engineers connect billing data to the infrastructure that generates each charge.
- 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
Assess cloud costs and AI readiness together
A cloud cost assessment reviews an existing AWS or Azure environment against its actual invoice and usage. unicrew's FinOps audit turns that review into a written plan ranked by saving, effort and risk. When AI readiness is in scope, we also examine whether the architecture, data pipelines and access model can support the planned AI workloads.
03The answer
Turn cost recommendations into engineering priorities
Native cost tools provide recommendations. Our cloud cost optimization assessment connects those recommendations to the architecture and usage behind your invoice, then ranks the proposed work.
- Half 01
Where can cloud spend be reduced?
We review rightsizing and idle or orphaned resources. We check storage tiers and recurring data-transfer (egress) charges against actual usage and your invoice.
- You get
- A line-item teardown of your real invoice
- Half 02
What does the platform need for AI?
We review the architecture, data pipelines and access model against the planned AI workload, then identify the changes needed to manage cost and security.
- You get
- An AI-workload readiness read
- Both
Which changes should we prioritize?
We rank findings by potential saving, engineering effort and risk, so finance and engineering can agree the order of work.
- You get
- A prioritized plan, in order
Choose the cost review alone or both reviews
We assess AWS, Azure or an estate using both. If cost is your only question, we scope the fee to the cost teardown.
04Fit
Match the audit to your cloud decision
Use the audit to plan changes in an existing cloud estate. For a defined implementation or a broader business decision, these unicrew services provide the next step.
Plan a cloud investment
Good fit- Your cloud bill is growing faster than usage, and you need to understand which resources account for the difference.
- You are planning AI or data workloads and want to assess the infrastructure they will use.
- Account ownership has changed, and you need a clear record of the running resources and their costs.
- Finance and engineering need one evidence-based plan for savings and platform capacity.
Start with a defined delivery need
Better first step- You are choosing where AI should contribute to the business. Start with the AI Readiness Assessment.
- Your application needs stabilizing before infrastructure changes. Start with Legacy Software Rescue.
- You have an agreed change plan ready to implement. Continue with cloud consulting or DevOps.
- Your cloud spend is modest. Bring the figure to the scoping call so we can agree an appropriate review.
We audit cloud spend against your invoice, line by line. Beyond server sizes, we check data copied between countries every night. We also look at files nothing has opened in two years and test systems running at full size.
Yaroslav HavrylivSenior Engineering Manager05Deliverables
Written outputs for your cloud investment
The written outputs give finance and engineering a common basis for the next investment.
- 01
A cost teardown
We show where the money goes and where spend can be reduced, against your actual invoice. The teardown opens with an executive summary for finance.
- Format
- Executive summary, then line-item findings
- 02
A prioritized savings plan
We rank every finding by saving, effort and risk, giving you a clear order of work.
- Format
- Ranked plan, saving against effort
- 03
An AI-readiness read
We assess the architecture, data pipelines and access model against the needs of the planned AI workload.
- Format
- Findings log and remediation list
- 04
A FinOps starting point
We define what to watch, who owns the costs and which routines sustain the savings. FinOps calls this phase Operate.
- Format
- Practices, metrics and owners
06Delivery
Review costs with finance and engineering
We carry out the analysis and agree priorities with your technical owner and finance sponsor.
- Scope and accessWe agree the accounts, subscriptions and environments in scope, including whether AI readiness is included. We confirm the fee and timeframe in writing, then arrange read-only access after the NDA.You getScope and timeframe confirmed in writingFrom youRead access to cloud billing and architecture, and a named owner
- AnalyzeWe compare spending with usage and investigate the resources behind each charge. When AI readiness is in scope, we assess architecture, data and access against the planned workloads.You getFindings shared during the analysisFrom youShort interviews with whoever knows how the estate grew
- PrioritizeWe rank the findings by saving, effort and risk. Engineering and finance review the trade-offs and agree the sequence.You getThe order to work in, agreed with youFrom youOne prioritization call, with engineering and finance both present
- Readout and planWe deliver the written audit and savings plan, then review the findings in a working session. Any implementation is scoped separately.You getThe written audit and the savings planFrom youA working session with the decision-makers present
07Proof
Cloud savings and production AI experience
Our cloud delivery connects infrastructure decisions to operating costs. Our own AI products inform the workload-readiness review.
- AWS and Heroku efficiency on a live platformPatrick W., Founder and President of a New York B2B SaaS company, credits unicrew with helping "reduce our AWS costs by 30% or more". Our web development and infrastructure work streamlined how the platform used AWS and Heroku.30%+AWS cost reduction reported by a B2B SaaS client
- Infrastructure optimization experienceIn our delivery experience, infrastructure optimization has produced immediate savings of 20% to 40%. We also stabilized a healthcare platform and reduced its AWS operating costs. Your audit quantifies the opportunities in your own estate.20% to 40%Immediate infrastructure savings in our delivery experience
- Production AI experience informs the readiness reviewWe build, run and sell Snaplore and Talkmetry, our own AI products. Snaplore uses Whisper, GPT models and AssemblyAI; Talkmetry applies GPT and AssemblyAI to call intelligence for HubSpot. That experience informs our review of the data pipelines and access an AI workload needs.2AI products built and operated by unicrew
08Case studies
Cloud platforms we have improved
Review the projects closest to your cloud estate to assess the engineering experience behind our spending and reliability recommendations.
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
Client views on cloud and platform delivery
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 which AWS or Azure accounts you want reviewed, your approximate cloud spend and whether AI workloads are planned. We confirm the scope, fee and timeframe on the scoping call.
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 your cloud cost assessment
Confirm the audit's scope and your team's role before booking.
The audit produces a cost teardown, a prioritized savings plan and a FinOps starting point, with an AI-readiness read when that half is in scope.
- The cost teardown reviews your AWS or Azure spend against the invoice.
- The AI-readiness read assesses the architecture, data and access needed for AI workloads.
- The prioritized savings plan ranks findings by saving, effort and risk.
- The FinOps starting point defines what to watch and who owns the costs.
Implementation is a separate engagement. Your own team can use the plan, or we can scope the work through cloud consulting.
Native tools can recommend cost changes. The audit validates the opportunities against your architecture and usage, then ranks them with the engineering effort and risk.
For example, AWS Cost Explorer rightsizing identifies idle and underutilized EC2 instances. Azure Advisor recommends shutting down or resizing the resource it evaluates.
We use your existing provider reports and any third-party cost platform as inputs. The scoped AI-readiness review adds the requirements of your planned workloads.
Savings projections use your own billing data at current usage.
We state assumptions beside each figure and rank changes by saving, effort and risk. Actual savings depend on subsequent usage and on implementing the recommendations.
The audit estimates the savings available in your estate; the amount depends on its current usage and configuration.
A New York B2B SaaS client reported that we helped reduce AWS costs by 30% or more. In our infrastructure optimization delivery experience, immediate savings have ranged from 20% to 40%.
Those are delivery results, not a forecast or guarantee for your audit.
We agree one scope and one fee in writing before work starts.
The fee depends on the accounts and subscriptions in scope and whether the AI-readiness review is included. We quote your cloud cost assessment on the scoping call.
We agree the delivery timeframe at scoping and confirm it in writing before you commit.
The number of accounts and the infrastructure in scope determine the duration. Most engagements start within two to four weeks.
We need read access to your cloud billing and architecture.
- We review cost and usage reports.
- We read the AWS console or Azure portal.
- We use existing infrastructure-as-code and documentation.
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. We do not resize resources, switch them off or change your accounts during the audit.
No. We can run the cost teardown alone and scope the fee accordingly.
Include AI readiness when you need to establish whether the architecture, data pipelines and access model can support the planned workload, and what that workload would add to the bill.
We perform the audit analysis. Your team provides access and joins the interviews, prioritization call and readout.
- A named owner arranges read-only access.
- People who know the estate take part in short interviews.
- Engineering and finance join the prioritization call.
- Decision-makers attend 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.
Implementation is separately scoped and can be carried out by unicrew or your own team.
The report records that finding and identifies which cost practices should continue.
If AI readiness is in scope, that review still establishes what the platform needs for the planned workload. The audit then confirms the cost baseline and assigns the routines and owners that keep it under review. We discuss the likely focus during scoping.
11Where to go next
Plan the next step after the audit
Use the audit findings to choose the next engagement, or compare the fixed-scope offers for another starting point.
Choose and prioritize AI use cases across the business.
Test a defined conversation use case on your own data.
Prepare security evidence for an enterprise buyer's review.
Build a first release with an agreed scope in six weeks.
Stabilize the application before changing its infrastructure.
Implement the audit's cloud recommendations in a separately scoped engagement.
Improve deployment pipelines and environment automation after the audit.
Plan and execute a cloud migration when moving platforms is the next decision.
Build the data pipelines and storage needed for the planned AI workload.