Client Story
- lower model inference spend than an unrouted baseline, through caching and smaller models
- 35 to 60%
Cloud and AI Cost Optimisation
Cloud and AI bills you can explain, with savings that hold after the first review.
Why It Matters
Cloud bills often grow faster than usage, as teams share accounts and tags go missing.
Nobody can say which service caused the rise. AI adds a line that is harder to see, because agent workflows can call a large model at every step.
What We Do
Cloud Cost Reporting
Each line of spend has an owner and a trend against the baseline, so you can see which team or feature changed it.
AI Usage Cost Control
Model calls and accelerators grouped by feature, with caching and smaller models lowering the bill without lowering the quality bar.
Right-Sizing and Commitments
Workloads matched to real demand and commitments bought against stable use, with the risk shown before you commit.
Energy-Aware Computing
Work sized and scheduled with its power use in view, and reports that give your sustainability team the figures it needs.
What Changes for You
Every Cost Has an Owner
Each line of spend has an owner and a trend, so you can see which team or feature changed it.
A Lower Bill
Waste is removed, workloads fit their demand and commitments match use, with savings measured against a recorded baseline.
Savings That Hold
Teams get targets, reports and a routine review, so the saving survives after the first clean-up.
What We Run and Watch
We look at what you spend and what drives it. The scope names each account and each team that owns a line.
Cloud Spend
Accounts, services and data transfer are measured by team and workload. Shared costs are split by a rule your finance team accepts.
Workloads
Each workload is compared with its demand. Idle capacity, oversized machines and unused storage are listed with a fix.
AI Usage
Usage is grouped by feature, not only by account. Caching and smaller models are tested against a quality bar.
Commitments
Reserved capacity and savings plans are matched to stable use. We show the risk before you commit.
Service Levels
| Priority | Response | Escalation | Resolution |
|---|---|---|---|
| P1 | under 15 minutes | immediate war-room mobilisation | under 2 to 4 hours |
| P2 | under 30 minutes | within 1 hour | under 8 hours |
| P3 | under 4 business hours | the next daily triage | 24 to 48 business hours |
| P4 | under 1 business day | the next scheduled sprint | the next scheduled release |
Response
- P1
- under 15 minutes
- P2
- under 30 minutes
- P3
- under 4 business hours
- P4
- under 1 business day
Escalation
- P1
- immediate war-room mobilisation
- P2
- within 1 hour
- P3
- the next daily triage
- P4
- the next scheduled sprint
Resolution
- P1
- under 2 to 4 hours
- P2
- under 8 hours
- P3
- 24 to 48 business hours
- P4
- the next scheduled release
Onboarding
The work starts with the bill and ends with a change your team owns. Each stage produces evidence you can check.
Collect the Data
We connect billing exports, tags and usage records. You give us access to each account and name the owner of each tag.
Set the Baseline
We record current spend, usage and performance for each workload. You confirm the baseline before we count any saving.
Find the Waste
We list idle capacity, oversized machines, unused storage and model calls that add nothing. You decide which items to fix first.
Make the Change
We resize, remove, cache or route one item at a time. You approve each change and its route back before it is applied.
Review and Repeat
We compare the new bill with the baseline and report what worked. You agree the next round from that result.
Reporting
You see the spend, the change and the evidence behind both. Reports use the same figures your finance team already keeps.
A Monthly Report
Spend by team, service and AI feature, with the change against the baseline. Every line has an owner.
A Forecast
Expected spend for the coming months, with the assumptions written down. You see the effect of each planned change.
A Board Summary
Leaders see the trend, the savings and the choices ahead. Detailed tables follow in an appendix.
Platforms and Technologies
We work inside Amazon Web Services (AWS), where most of the spend is visible. Kubernetes shows how workloads use their capacity. OpenTelemetry, Prometheus and Grafana supply the usage and performance data behind each change.
- Cloud
- Amazon Web Services
- DevOps and observability
- Kubernetes
- OpenTelemetry
- Prometheus
- Grafana
Naming a product does not mean a partnership with its vendor.
Industries
Media and Telecommunications
Consumer platforms, streaming, connected devices and ad-tech that stay fast at peak audience, with AI for content and personalisation.
Retail and Omnichannel Commerce
POS integration, stock and price sync, card payments, loyalty and e-commerce for retailers with many sites.
Software and Technology Companies
Software companies and technology teams: multi-tenant products, platforms and the AI features and billing that keep them growing.
Insights
Questions
What finance and technology leaders ask before they start.
How much can you save?
It depends on the waste we find and how fast your team can change. We estimate each item first and report the result against the baseline.
When will we see a lower bill?
The first changes show in the next billing cycle. Larger commitments take longer and need your finance team's approval.
What does the work cost?
The scope, the accounts and your tagging set the effort. You receive a proposal with the stages and the effort before work starts.
Do we need to change how teams work?
Teams get a monthly figure and a target for the lines they own. A short review keeps the saving from drifting back.
Does this cover AI costs as well as cloud bills?
Yes. We measure model calls, tokens and accelerator use by feature. Caching and smaller models are tested before they go live.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.