Pay for the cloud you use, not the cloud you forgot.
Cloud bills creep: a test environment left running, storage nobody deletes, a GPU instance from last quarter's AI experiment. We find the waste in your AWS, Google Cloud or Azure account using AI-assisted analysis, fix it through infrastructure-as-code, and set up guardrails so it does not come back.
Where cloud bills usually leak
These show up in almost every account we review, whatever the size of the company. Most are straightforward to fix once someone actually looks.
Idle and forgotten resources
Staging environments running all weekend, abandoned proof-of-concept stacks and instances nobody will admit to owning.
Over-provisioned compute and databases
Servers sized for a launch-day spike that never came, running at a fraction of their capacity every day since.
Idle GPU instances
GPU capacity is expensive by the hour. Instances kept for model experiments or peak inference often sit unused for most of the day.
Unattached storage and old snapshots
Disks from deleted servers, years of automated backups and log buckets with no retention policy.
Data transfer and egress
Traffic between regions or zones, NAT gateway charges and data leaving the cloud rarely appear on architecture diagrams, but they appear on the invoice.
Commitments that no longer fit
Reserved capacity bought for a previous architecture, or no commitments at all on workloads that have been steady for years.
Managed AI services on autopilot
Hosted model endpoints still provisioned after a pilot ended, and AI API usage billed through the cloud account with no owner watching it.
GPU and AI spend is now a cloud cost line of its own
For years, cloud cost optimisation meant rightsizing servers and buying reserved instances. AI workloads have changed the shape of many bills. A single GPU instance can cost more per month than a small company's entire web tier, and managed model endpoints or AI API usage can appear under service names the finance team has never heard of.
GPU costs behave differently from ordinary compute. Capacity is sometimes hard to get, so teams hold on to instances just in case. Training and fine-tuning jobs are bursty, while inference traffic follows your users through the day. Treating both the same way usually means paying for peak capacity around the clock.
We look at AI spend with the same discipline as the rest of the bill: who owns it, what it delivers, and whether it belongs on dedicated GPUs, serverless endpoints, a provider API or a smaller model. When the bigger saving lies in how the model is used rather than where it runs, our LLM inference cost work picks up from there.
On-demand, committed or spot capacity?
How you pay matters as much as what you run. Each major cloud names these options differently, but the trade-offs are the same.
| On-demand | Committed (reserved, savings plans) | Spot or preemptible | |
|---|---|---|---|
| Price | Highest per hour | Discounted in exchange for a term | Often the deepest discount |
| Flexibility | Start and stop any time | Locked in for one to three years | Can be reclaimed at short notice |
| Best for | Unpredictable or short-lived workloads | A steady baseline you are confident about | Batch jobs, CI runners, fault-tolerant work |
| GPU workloads | Simple, but costly for always-on inference | Sensible once inference volume is proven | Good for interruptible training and batch inference |
| Main risk | Paying premium rates by default | Committing before the architecture settles | Jobs that cannot resume after interruption |
We usually recommend cleaning up and rightsizing first, then committing. Buying commitments on top of waste simply locks the waste in.
How a cloud cost optimisation engagement runs
A typical review fits a two to four week Advisory Sprint. AI tooling does the heavy lifting on billing exports, usage metrics and infrastructure code, so senior time goes on judgement: what is safe to change, and in what order.
Access and baseline
Days 1 to 3Read-only access to billing and usage data, a cost breakdown by service, environment and team, and a list of resources with no clear owner.
Find and rank savings
Weeks 1 to 2Each opportunity is costed, rated for risk and effort, and grouped into quick wins, planned changes and architectural decisions.
Implement safely
Weeks 2 to 4Changes go through infrastructure-as-code where possible, are tested in non-production first, and carry rollback plans if they touch live traffic. Your team can do this, or we can.
Keep it fixed
OngoingTagging standards, budgets and anomaly alerts, a monthly cost review rhythm, and named owners for GPU and AI spend.
Questions we often hear
How can I reduce my AWS bill quickly?
The fastest savings usually come from shutting down idle resources, deleting unattached storage and old snapshots, scheduling non-production environments to stop outside working hours, and rightsizing obviously oversized instances. Commitments such as savings plans come next, once you know your steady baseline.
How much can cloud cost optimisation save?
It depends heavily on how the account has been run. Accounts that have never been reviewed often hold substantial, easy savings, while well-managed accounts may only have modest gains left. We give you a costed estimate after looking at your actual billing data rather than promising a percentage up front.
How do I reduce GPU costs in the cloud?
Match the capacity model to the workload: interruptible spot capacity for training and batch jobs, committed capacity only for proven steady inference, and autoscaling or serverless endpoints for spiky traffic. It is also worth checking whether a smaller or quantised model, or a provider API, would meet the same need for less.
Can AI help reduce our cloud and AI spend?
Yes, in two ways. AI-assisted analysis speeds up the tedious parts of a cost review, such as reading billing exports, tagging resources and drafting infrastructure changes, so the work takes fewer engineer-hours. For teams already running AI features, the biggest savings often come from the AI workloads themselves: routing requests to smaller models, caching repeated prompts and right-sizing GPU instances. Every change is still reviewed by an engineer before it reaches production.
Will cost optimisation affect performance or reliability?
It should not if it is done carefully. We change non-production systems first, monitor performance before and after each change, and avoid cutting the redundancy that protects uptime. Where a saving carries real risk, we explain the trade-off and let you decide.
Do you need write access to our cloud account?
Not for the review. Read-only billing and monitoring access is enough to find and cost the savings. If you want us to implement changes, we agree scope and permissions with you and prefer to work through your existing infrastructure-as-code and change process.
Related reading and tools
Send us the shape of your cloud bill.
Tell us which cloud you use, roughly what you spend and what has changed recently. We will reply within 24 hours with where we would look first, including any GPU or AI spend.
Working with companies globally · Response within 24 hours