Azure and AWS cost reduction and scaling, without the guesswork
Cloud bills grow quietly. A test VM nobody switched off, disks left behind when a server was deleted, a database sized for a launch that never needed it. We find the waste, resize what's oversized, and set up scaling and budgets so the bill follows real usage.
/ Fit check
Best for
Businesses running workloads in Azure or AWS whose monthly bill has drifted upwards
Not for
Hyperscale platforms that need a dedicated in-house FinOps team
Deliverables
Costed findings, a resize plan, budgets, alerts and a tagging standard
/ Direct answer
What does an Azure infrastructure cost reduction consultant do?
An Azure cost reduction consultant reviews your subscriptions, finds resources that are idle, oversized or on the wrong pricing model, and fixes them without breaking the workloads that depend on them. Typical changes include right-sizing virtual machines, deleting orphaned disks and IP addresses, scheduling non-production shutdowns, moving steady workloads to reservations or savings plans, and setting budgets and alerts so costs stay visible. The same work applies in AWS, where the equivalent tools are Cost Explorer, Compute Optimizer, Savings Plans and Reserved Instances.
- Every finding priced in monthly savings
- Changes made in maintenance windows
- Budgets and alerts left in place
/ 01
Where does Azure spend usually leak?
In a handful of predictable places: virtual machines sized for a peak that rarely happens, non-production environments running around the clock, unattached disks and public IPs left after deletions, oversized databases, snapshots kept long past their purpose, and premium storage holding data nobody reads.
| Leak | How we find it | Typical fix |
|---|---|---|
| Oversized VMs | 30+ days of CPU and memory data, Azure Advisor | Resize or move to a better-fitting VM series |
| Always-on dev and test | Tags and usage hours | Auto-shutdown schedules |
| Orphaned disks and IPs | Resource Graph queries | Snapshot if needed, then delete |
| Steady workloads on pay-as-you-go | Usage history | Reservations or a savings plan |
| Hot storage for cold data | Access patterns | Lifecycle rules to cool or archive tiers |
| Log ingestion | Per-table ingestion volume | Filter, shorten retention, cheaper log tiers |
/ 02
Why is my AWS bill so high?
Usually for the same few reasons: EC2 and RDS instances sized for a peak that rarely comes, non-production environments running around the clock, NAT gateway and cross-zone data transfer charges, CloudWatch logs kept forever, and old EBS snapshots and unattached volumes. Cost Explorer grouped by service and usage type normally shows which one it is within minutes.
| AWS cost driver | Typical fix |
|---|---|
| Oversized EC2 and RDS instances | Right-size using Compute Optimizer and 30 days of metrics |
| Always-on dev and test | Instance scheduling outside working hours |
| NAT gateway and data transfer | VPC endpoints, and keeping traffic in one availability zone where safe |
| CloudWatch logs with no expiry | Set retention per log group |
| Old snapshots and unattached EBS volumes | Lifecycle policies, then delete what isn't needed |
| Steady workloads on on-demand pricing | Savings Plans or Reserved Instances after right-sizing |
/ 03
How do you right-size Azure resources safely?
We look at at least a month of CPU, memory, disk and network data before changing anything, resize a non-production copy first where one exists, and schedule production changes in a maintenance window with a rollback plan. A VM resize usually needs a restart, so we treat it as a change, not a tweak.
/ 04
Reservations or savings plans: which should we use?
Reserved instances give the deepest discount but tie you to a specific VM family and region for one or three years. Azure savings plans give a smaller discount but apply across VM sizes and regions, as long as you commit to an hourly spend. For stable, predictable workloads, reservations usually win. For an estate that's still changing, a savings plan is the safer commitment.
Commit after right-sizing, not before. Buying a reservation for an oversized VM locks the waste in for years.
/ 05
How does scaling control cost as well as performance?
Autoscaling adds capacity when load rises and removes it when load falls, so you stop paying for peak capacity all day. Virtual machine scale sets, App Service autoscale rules and serverless options like Azure Functions all do this. Which one fits depends on how the application is built.
Not every application can scale out. Older apps that keep state on the server need work before they can run on several instances, and we'll say so plainly instead of promising savings the architecture can't deliver.
/ 06
How do you keep Azure costs under control after the review?
With budgets and alerts per subscription or resource group, a tagging standard so every resource has an owner and a cost centre, Azure Policy to block expensive SKUs or untagged deployments, and a monthly cost review that looks at the trend rather than just the total.
- Budgets with alerts at agreed thresholds
- Owner and cost-centre tags on every resource
- Azure Policy guardrails on SKUs and regions
- Auto-shutdown on non-production by default
- Monthly trend review, not just the invoice
- Reservation utilisation checked quarterly
/ 07
How is an Azure cost review priced?
As a fixed-scope engagement, agreed in writing before work starts. The scope depends on the number of subscriptions, the size of the estate, and whether you want us to make the changes or only recommend them. Ongoing cost management can then be added to a managed cloud agreement.
Frequently Asked Questions
A VM resize needs a restart, so that machine is briefly unavailable. We schedule it in an agreed maintenance window and, for anything critical, test the new size on a copy first.
Read access through a named account, using the Reader and Cost Management Reader roles, is enough for the review. Any changes use separately approved, time-limited permissions.
It depends entirely on how the estate has been run, so we don't quote a number up front. The review report lists each finding with its estimated monthly saving, so you can decide what's worth doing.
Yes. Azure and AWS are covered in detail on this page, and Google Cloud follows the same approach with its own tools, such as committed use discounts and Recommender. The principles of right-sizing first and committing second apply on every platform.
Yes. New environments are built with sizing, tagging, budgets and policies in place from day one, which is far cheaper than cleaning up later.
/ Next step
Want this reviewed against your own environment?
Share your users, tools and the problem you are trying to solve. We will tell you plainly whether this service fits, and what we would look at first.