Cloud Performance Lab

See exactly where you're overpaying for cloud compute, and get specific, benchmark-backed instance recommendations that cut cost without hurting performance.

What it is

Cloud Performance Lab (CPL) looks at the virtual machines you're running today, compares them against independent benchmark data and live cloud pricing, and shows you where a different instance would run the same workload for less, or run it faster for the same money.

You don't change how anything works. In most cases you stay on the same cloud and the same processor family. You just move to hardware that gives you more for your spend.

The problem it solves

Cloud providers release new instance generations every 12 to 18 months. Each new generation usually delivers more performance per core at the same or lower price. Most teams, understandably, size a workload once when it's deployed and don't revisit it as newer hardware becomes available.

The result is that a lot of cloud compute is running on hardware that's two or three generations old, often with more capacity allocated than the workload actually uses. Neither is a mistake anyone made on purpose. It's simply what happens when sizing decisions aren't revisited. CPL is the exercise that revisits them, with data.

Where the savings come from

The largest and lowest-risk savings come from a simple move: shifting a workload from an older instance generation to the current one on the same processor architecture.

For example, a workload on an older Intel-based machine (such as Azure's Dv3 or Dv4, or AWS's M5) can move to the current Intel generation (Dv6, or the equivalent on AWS) and either:

• cost less for the same number of cores, or

• run on fewer cores because each core is faster.

This is a like-for-like hardware exercise. You're not changing cloud provider, re-architecting your application, switching CPU family, or touching your operating system. The change is a virtual machine resize. That keeps the risk low and the benefit clear.

Where it helps

CPL fits a range of situations beyond straightforward modernisation:

• Lowering cost on existing workloads: move to current-generation hardware and pay less for the same performance.

• Reducing over-provisioning: where a machine consistently uses a fraction of its capacity, right-size it to fit real usage, with a safety margin built in.

• Getting more performance for the same spend: if a workload is slow rather than expensive, newer hardware can improve speed without raising the bill.

• Sizing a cloud migration correctly: when moving workloads from on-premises to cloud, size the target instances from real performance data instead of copying physical server specifications and over-buying.

• Comparing across providers: see what the same workload would cost on AWS, Azure, and Google Cloud, normalised against benchmark performance, so a choice between them is informed.

• Sizing new workloads from the start: choose the right instance before deployment rather than guessing and adjusting later.

• Making smarter commitment decisions: right-size and modernise before signing a one- or three-year reservation, so you're committing to the machine you actually need.

• Reducing energy footprint: newer generations do more work per unit of energy, which lowers both cost and reported emissions.

How it works

A three-step model that deepens as the value becomes clear. You can stop at any step.

Step 1 — Single workload look

Pick one virtual machine. With its name and a short description of what it does, we show you a real savings number on that one workload, usually in a few minutes.

Step 2 — Full estate analysis

Share an inventory export of your machines and, if you have it, their CPU and memory utilisation. We produce a full report of total savings potential across your estate, including cross-provider comparison.

Step 3 — Managed optimisation

Our consultants review every recommendation, plan the priority and sequence of changes, and hand you a report ready for sign-off. You keep a clear, validated plan and a baseline to measure savings against.

Why the recommendations are credible

• Independent benchmark data. Recommendations are based on real-world performance benchmarks for each instance type, not on vendor marketing or vCPU counts alone.

• Live regional pricing. Cost comparisons use current pricing for your regions, not list-price estimates.

• Headroom buffers. Every recommendation includes a safety margin so you're never sized below what the workload needs.

• Priority controls. You decide whether to optimise for lowest cost, highest performance, or maximum headroom, and the recommendations adjust to match.

What you provide, and what you get back

Data and security

• The first look needs only an instance name and a description. No access to your environment is required to start.

• A full analysis uses an inventory export and, optionally, utilisation metrics. You control exactly what you share.

• Recommendations are advisory. CPL does not make changes to your environment. You decide what to implement and when.

For your account team to confirm: specifics on data retention, hosting, and any compliance certifications relevant to this customer.

Common questions

Will performance drop if we right-size? No. Moving to a newer generation on the same architecture means faster cores, not slower ones. Where we recommend a smaller machine, the headroom buffer ensures it still comfortably covers your workload.

Are we locking ourselves into one provider? No. CPL is vendor-neutral. It compares across AWS, Azure, and Google Cloud, and the recommendations are yours to use however you choose.

How accurate are the savings figures? They're based on independent benchmarks and live pricing, and they're estimates you validate before implementing anything. The point of starting with a single workload is to prove the number on something real before going wider.

What do we have to commit to? Nothing to start. The first conversation is one workload and a savings figure. You decide whether to go further from there.