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Microsoft Azure

Azure cost optimization: cut the bill without touching performance

Azure bills by the minute and by the GB. Without steering, the bill climbs every month. Here are the levers we activate, in order, and what they really deliver.

Start with visibility

You can't optimize what you can't see. The first step is to tie every euro to an application and an owner: minimal tagging linked to the CMDB, daily billing exports from Cost Management, and a dashboard that leadership and application owners can both read. That is often where the first anomalies show up: non-prod costing more than prod, resources with no owner, a finished project still running.

Rate optimization: pay less for the same consumption

These levers don't touch the applications. They are the fastest to put in place.

LeverTypical savingGood to know
Reserved Instances (1 or 3 years)≈ −50% on a prod VMCommitment on a SKU and a region: ideal for the stable baseline
Azure savings plan for computeA bit less than an RIHourly commitment in €, flexible across SKUs and regions
Azure Hybrid BenefitThe cost of the Windows Server or SQL Server licenseReuses licenses you already own with Software Assurance
Reserved capacity (storage, databases, Fabric)Depends on the serviceOnce volumes are stable
MACCNegotiated discountOverall consumption commitment with Microsoft

The golden rule: clean up before you commit. You don't commit for three years to an oversized or useless VM. We detail the method on our Reserved Instances and savings plans page.

Usage optimization: consume only what is useful

Rightsizing

A VM whose CPU and RAM stay below 50% at P95 over 30 days can move down one SKU, which roughly halves its cost. The latest SKU generations (v5 and later) are also often cheaper for the same performance.

Automatic Stop and Start on demand

Non-prod VMs and VDIs don't need to run at night or on weekends. A fixed-schedule Stop/Start saves 50 to 60%, almost as much as a reservation, and the two don't stack. The real lever is an automatic Stop every evening and a Start on demand: the VM only restarts when someone needs it, and savings reach about 90%. On Azure Virtual Desktop, the "Start VM on Connect" feature works exactly like this for virtual desktops.

Cleaning

Unattached disks, stale snapshots, unused public IPs, empty App Service Plans, forgotten POC environments: removing what's no longer used is 100% savings on the resource. In non-prod, Premium SSD disks can often move to Standard SSD.

The services that drift with volume

Some services cost little on day one, then a fortune at scale: Log Analytics and Azure Monitor, Microsoft Sentinel, Azure Backup, Synapse, Fabric. They are often the biggest savings pool on a mature Azure bill. We detail further down the levers we activate on each one, together with the experts in charge. On logs, costs can sometimes be divided by 4.

Why Azure Advisor isn't enough

Azure Advisor is a good starting point, but its rules are generic and know nothing about your business context, the criticality of your applications or your load peaks. We apply rules defined with your IT department, on your real metrics, and we then measure the real savings after implementation. See our recommendation engine.

Our approach on Azure

  1. See: read-only (Reader) access to billing, dashboard, cost mapping by application.
  2. Reduce: quick wins from the first month, then a remediation plan across all services.
  3. Stay in control: FinOps governance, monitoring and alerting, budget forecasting.
Do you have an Azure cost export? Our free analysis tool puts a €/month figure on the savings already visible in your bill, and the file never leaves your browser.

FAQ

How much can you save on an Azure bill?

Typically 15 to 35% depending on the starting FinOps maturity, and 35% on average when nothing is managed yet. The first savings come in the first month with reservations, Azure Hybrid Benefit and cleaning.

Reserved Instance or Azure savings plan?

A Reserved Instance gives the biggest discount for a stable workload on a known SKU and region. A savings plan is more flexible when the estate changes. Most companies combine the two, after cleaning up and rightsizing.

Do you need access to our subscriptions?

To get started, read-only (Reader) access to billing data and usage metrics is enough. Changes are applied by your teams or your managed service provider, through your own approval process.

Can optimizations degrade production?

Every recommendation relies on cautious thresholds (CPU and RAM P95 over 30 days), is first tested on a POC, and the impact is measured right after implementation. Critical applications expecting a load peak are handled separately.

Per-service optimization

Services that cost almost nothing at first, then a fortune at scale

A Log Analytics Workspace enabled by the security team or a default backup policy costs little on day one. Two years later, as volumes grow, they weigh tens of thousands of euros a month. The services shown below are only examples: the same approach applies to every cloud service. We have led major optimization projects on these services with the experts in charge (SOC, data teams, backup Ops). The goal is to reduce volume without losing anything in security, compliance or performance.

Log Analytics · Azure Monitor

Every GB ingested is billed

Every new resource and every diagnostic setting enabled by default adds volume, often without anyone using those logs.

  • Filter the resources and log types collected (Data Collection Rules)
  • Basic / Auxiliary logs for high-volume tables
  • Commitment tier matched to actual volume
  • Per-table retention, archive or export to a data lake

Costs sometimes divided by 4, without losing the logs that are actually used

Microsoft Sentinel

The SIEM that grows with every source

Deployed by security for a narrow scope, then extended source after source: firewalls, proxies, Entra ID, endpoints… Ingestion explodes.

  • Ingest only the logs that detection rules need, together with the SOC
  • High-volume logs (firewall, proxy) in Basic / Auxiliary logs
  • Sentinel commitment tier recalibrated
  • Long-term retention in archive for compliance

Savings achieved without weakening detection, with every filter validated by the security team

Backup · Storage

Volumes that double silently

10-year retention, non-prod backed up like prod, everything stored in Cool: the bill follows the volume, month after month.

  • Backup policy review: daily, weekly, monthly, yearly retention
  • Access tiers: Hot → Cool → Cold → Archive by age
  • Non-prod: lighter backup, or none
  • Reserved capacity on storage once volumes are stable

A cost per GB that goes down, even as volume grows

Azure Synapse

Spark pools sized for the worst case

Auto-scale without a sensible cap, auto-pause too long, UAT refreshed like prod: the data platform consumes well beyond budget.

  • Auto-pause enabled and autoscaling reduced (max nodes, node size)
  • Processing frequency matched to the real need, manual refresh on UAT
  • Less volume processed thanks to delta mode (incremental loads)
  • Shared Spark pools, unused SQL databases removed
  • Reservations once usage is stable

Every setting is tested with the data team, with performance impact monitored

Microsoft Fabric · Power BI

Capacity that grows with the datasets

Datasets and refreshes pile up, the capacity moves up a SKU, and nobody knows what is still in use.

  • Monitoring and cleaning of unused Power BI datasets
  • Non-prod capacities paused outside working hours
  • Capacity reservation once usage is stable

The right SKU at the right time, without slowing data teams down

The method

With the experts, not against them

On these services, a poorly designed saving can cost you a security alert or a backup you needed. So every project is run with the team that owns the service:

  • Volume analysis by source, table or policy
  • Identifying what is really used
  • Each option quantified
  • Validated by the experts, then real savings measured
First conversation

How much are you losing every month on a poorly optimized cloud?

SMEs: we are paid 20% of the annual savings achieved. Large enterprises: day-rate engagement. A first 30-minute conversation, no commitment.