FinOps consulting: control and reduce your AWS and Azure cloud bill

Why cloud bills get out of control

Cloud bills drift because the cloud charges by usage, by the second or by the gigabyte, and any team can create resources in minutes. That is its strength and the source of the drift. The symptoms look alike from one company to the next: a bill growing faster than the business, line items nobody can explain, test environments running over the weekend, storage volumes and snapshots whose purpose nobody remembers.

The problem is rarely technical at first. It is organisational: finance sees a total, engineering sees resources, and nobody connects the two. FinOps exists to create that connection. If you first want to understand the discipline itself, our article FinOps: definition, method and levers to cut your AWS bill covers the basics. This page describes what we actually do for you.

Our approach: the FinOps Foundation framework, applied to your context

We follow the three phases of the FinOps Foundation framework, as a loop rather than a sequence.

Inform: make costs visible and attributable

• Cost allocation: a tagging strategy (application, environment, team, cost centre), separate accounts or subscriptions by usage, cost categories for what cannot be tagged.
• Tags applied at the source: built into Terraform and Infrastructure as Code modules and enforced by policies, to avoid manual catch-up work that never lasts.
• Dashboards for each audience: an executive view (trend, forecast), a product view (cost per service or per customer), an engineering view (detail per resource).
• Shared costs: explicit rules for splitting networking, monitoring or shared platforms.

Optimise: act on usage and on rates

• Usage: removing orphaned resources, scheduling non-production environments to shut down, storage lifecycle policies, rightsizing instances and databases based on actual metrics.
• Architecture: better-suited instance families, managed or serverless services where they simplify things, fewer costly data transfers.
• Rates: a commitment strategy (Savings Plans and Reserved Instances on AWS, reservations and savings plans on Azure), sized to the stable part of your usage.

Operate: make the practice last

• Budgets and alerts per scope, anomaly detection, thresholds that trigger an action rather than just an email.
• Routines: a short monthly review between finance, product and engineering, with a log of the decisions taken.
• Guardrails: policies that block untagged resources or off-catalogue sizes, linked to your cloud governance.

Savings Plans and Reserved Instances: the order of the levers

Committing for one or three years is often the most visible lever, and the most misused. Buying commitments before cleaning up means paying less for capacity you do not need, for the whole length of the commitment.

Our rule: clean up, rightsize, then commit on the stable baseline, and review coverage regularly.

Question	What it determines
Is your compute usage stable over the year?	The share of the workload to cover with a commitment
Do you plan to change instance family or region, or move to containers?	Savings Plans rather than Reserved Instances, to keep flexibility
Do your managed databases run continuously?	Whether reservations dedicated to those services are worthwhile
Who holds the commitment: a central account or each entity?	How discounts are shared and internal chargeback
Can your cash position support upfront payment?	The payment option, decided with finance

AI costs: a new line item to manage

Generative AI projects add spending lines that often escape existing dashboards: API calls billed per token, GPU instances, vector databases, storage for evaluation logs. We bring them into the same practice:

• cost allocation by use case and by team from the moment a project goes live;
• tracking cost per request or per conversation, to compare models at equal quality;
• specific levers: choosing the right model for the task, caching, limiting context size, quotas per application.

This work ties in with our approach to putting AI into production (LLMOps).

Concrete deliverables

• FinOps assessment: a map of your spending, the quality of your allocation, and a list of levers costed on your data, ranked by estimated gain and effort.
• Documented tagging and allocation strategy, with the policies that enforce it.
• Dashboards in the native tools (AWS Cost Explorer, Azure Cost Management) or in your reporting tool.
• Budgets, alerts and anomaly detection configured as code.
• Commitment plan: recommended coverage, purchase schedule, review rules.
• FinOps runbook: routines, roles and tracked metrics, so your teams can carry on without us.

Common pitfalls

• Running a one-off audit with no routine behind it: the savings erode within a few months.
• Looking for the right tool before the right allocation: without reliable tags, no tool will tell you who is spending.
• Leaving FinOps to engineering alone or to finance alone: the practice only exists if both decide together.
• Optimising at the expense of reliability: aggressive rightsizing on a production database is paid for in other ways. Every recommendation is reviewed with the application owners.

How we work with your teams

We follow our four-step method. Scope: assessment and prioritisation with finance and engineering. Build: allocation, dashboards, budgets, first optimisations. Industrialise: tags and guardrails in Infrastructure as Code, alerts, commitment plan. Improve: monthly routines, coverage reviews, growing autonomy.

The decisions remain yours; we bring the data and the options. Everything is documented and handed over: the goal is for your organisation to manage its cloud spending without depending on a provider. We stay neutral between AWS and Azure.

Who it is for, and when to call us

Our FinOps consulting is aimed at small and mid-sized companies that already have significant cloud spending, or are about to after a migration to AWS or Azure. The usual triggers: a bill that surprises the finance department, a budget to present without visibility, commitment renewals to decide on, or AI projects whose costs are not tracked.

We are based in Villeneuve-d’Ascq, in the Lille metropolitan area (northern France), and work on site or remotely. You can book a 15-minute call to present your situation.

[ YOUR QUESTIONS ]

Frequently asked questions

What does a FinOps consultant do?

A FinOps consultant makes cloud spending readable, helps you decide which optimisations to make and sets up the routines that prevent costs from drifting. In practice: cost allocation by team or product, budgets and alerts, rightsizing and commitment analysis, then handing the practice over to your teams.

How can I reduce my AWS bill?

Start by making costs visible and attributable, then remove waste (orphaned resources, environments left running for no reason), rightsize resources, and only then commit through Savings Plans or Reserved Instances. The order matters: committing to oversized capacity locks the waste in.

When should I buy Savings Plans or Reserved Instances?

After cleaning up and rightsizing, and only on the stable part of your usage. Savings Plans offer more flexibility across instance families and some compute services, while Reserved Instances remain relevant for services such as managed databases. The right choice depends on how stable your usage is and on your plans, and should be analysed against your usage history.

Does FinOps apply to Azure too?

Yes. The principles are the same, the tools differ: Cost Management, budgets and reservations on Azure; Cost Explorer, AWS Budgets and Savings Plans on AWS. We work on both, including multicloud setups with a shared allocation model.

How much can a FinOps programme save?

We do not quote a percentage up front: it depends on your architecture, your maturity and your history. The initial assessment puts figures on each lever using your actual data, and you then decide which ones to pull.