The 2025 Cloud Cost Crisis: Why Data, BI and AI Teams Must Act Before 2026

TL;DR Executive Summary

Cloud waste is currently at a 10-year high², while cost visibility across organisations remains low³. Misconfigurations, opacity in Fabric and Databricks, limited native monitoring in Qlik Cloud, and the behaviour of AI workloads contribute to ongoing silent cost inflation. In addition, many major cloud and SaaS vendors are raising their prices for 2026 and beyond.

Several customers told us:

“The unpredictability hurts more than the cost itself.”

Even internally, one forgotten VM doubled our dev spend. 2026 will reward predictable architecture, not flexible architecture.

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Introduction: A Hard Year and a Structural Turning Point

2025 was one of the most challenging years for cloud-driven organisations.
CFOs across DACH told us:

“The bill isn't the problem. The black box is the problem.”

Even companies with accurate cost planning were caught by surprises, not because of new features but because of:

• silent misconfigurations
• opaque platform billing
• inter-region transfer costs
• RAG and AI inference spikes
• GDPR-driven architecture caution
• monitoring gaps (Fabric, Qlik Cloud)
• Databricks autoscaler quirks
• and FinOps immaturity

Cloud complexity plus AI complexity plus compliance complexity equals unpredictability. And unpredictability is exactly what 2026 budgeting cycles will not tolerate.

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1. Why Cloud Costs Exploded in 2025

1.1. The 20/80 Reality: 20% of Workloads Drive 80% of Spend

Across our audits, we consistently found:

• a handful of workloads
• silently consuming the majority of spend
• with almost no cost attribution
• and no accountability

BCG validates this pattern: up to 30% of cloud spending is wasted.² This mirrors what we see in BI, RAG, and data engineering landscapes.

1.2. Azure and Fabric: Opaque Monitoring Means Opaque Costs

Azure Cost Monitoring and Microsoft Fabric abstract complexity and obscure cost behaviour:

• Fabric bundles ingestion, compute, and serving
• serverless ingestion appears with delay
• Log Analytics may replicate logs across regions
• no per-pipeline cost attribution

TechRadar confirms: 94% of IT decision-makers struggle to optimize cloud costs.³ When visibility collapses, predictability collapses.

1.3. Our Own Lesson: One Forgotten VM Doubled Dev Cost

During internal development of a high-performance AI service:

• one VM stayed active at full capacity
• monitoring had not been enabled
• workload did not change
• no alerts were triggered

Result: our cloud cost doubled.
A reminder: defaults are dangerous.

1.4. AI, LLM, RAG and GPU Jobs: The New Cost Wildcards

AI workloads behave nothing like BI workloads:

• GPUs stay warm even while idle
• inference spikes create unpredictable surges
• embedding loops run for hours if unbounded
• training in the wrong region multiplies spend

The European Commission notes that multi-region cloud setups significantly increase cost.⁴

Real examples we observed:

• €1,600 burned overnight by a stuck embedding loop
• unbounded RAG indexing
• agent-triggered inference loops

AI makes everything more powerful including your cloud bill.

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1.5. GDPR-Driven Region Behaviour and Logging Pressure

Most organisations are not yet implementing AI-Act-grade traceability.
But GDPR uncertainty and digital sovereignty concerns already shape architecture:

• region-specific placement
• redundant replicas
• cautious BI logging practices
• extended retention of metadata
• avoidance of cross-border transfers

Companies now pay a regulatory safety premium.

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2. The Monitoring Blind Spots Nobody Talks About

2.1. Fabric: Capacity Without Transparency

• ingestion not broken out
• transformations bundled
• region-dispersed logs

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2.2. Databricks: Autoscale Is Not Safe by Default

• scaling up is rapid, scaling down is slow
• retry storms create runaway cost
• inactive clusters remain active

2.3. Azure: Traffic Is the Silent Cost Killer

• inter-region egress
• silent blob growth
• serverless triggers firing unexpectedly

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2.4. Qlik Cloud: Limited Native Monitoring

• no built-in cost analytics
• no per-app reload visibility
• no ingestion cost breakdown
• opaque capacity consumption
• unmonitored storage growth
• abstracted auto-scaling

We have seen tenants where teams had no idea which apps consumed their capacity.

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3. How to Respond: A Cost-Intelligent Roadmap for 2026

Stage 1: Visibility

• tagging
• workload attribution
• drift dashboards
• anomaly alerts
• forecasting

Stage 2: Waste Reduction

• remove zombie workloads
• right-size compute
• delete unused storage
• optimise region traffic
• remove duplicate ingestion

Stage 3: Architecture Redesign

• decouple compute and storage
• sovereign region placement
• event-driven workloads
• inference caching
• predictable RAG orchestration

Stage 4: FinOps Culture

• cost KPIs
• sprint reviews
• modelling for CFOs
• cost impact for every decision

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4. Why This Matters Going Into 2026

Customers told us:

“We're not afraid of cloud cost. We're afraid of not understanding it.”

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And 2026 will intensify this problem:

• BI becomes AI-augmented BI
• RAGs and agents generate more metadata
• sovereignty requirements tighten
• transparency expectations rise
• each new AI workload adds a new cost wildcard

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Integrated Option A: PriceAdjustments for 2026 and Beyond

Across several client engagements we already observe announced price adjustments for 2026 and beyond across cloud and SaaS vendors. These increases vary by contract and region, but the trend is clear: base platform pricing is rising. Combined with structural cost drivers, cost unpredictability will increase.

Cloud cost is no longer a finance issue. It is a governance and architecture issue.

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5. How inics Helps

We offer external cloud cost audits with no long-term commitment. We identify:

• your 20/80 cost pattern
• misconfigurations
• sovereignty and GDPR-related cost leaks
• AI and RAG workloads causing spikes
• architecture risks affecting 2026 budgets

If your cloud feels like a black box, we help you open it.

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Final Note

2025 reminded everyone, including us, that predictability is the new performance.

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Definition Snippet: What Is Cloud Cost Unpredictability?

Cloud cost unpredictability occurs when organisations cannot forecast cloud spend due to hidden workloads, AI pipelines, misconfigurations, or opaque pricing models.

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FAQ: Cloud Cost Optimization 2025

What causes sudden cost spikes?
Misconfigurations, AI inference bursts, autoscaling behaviour, cross-region traffic, opaque billing.
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Why are AI workloads so expensive?
GPUs remain warm, inference spikes scale instantly, embedding and training workloads generate unpredictable consumption.
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Why is Qlik Cloud risky for cost control?
Limited monitoring, no cost attribution, opaque auto-scaling.

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How can companies reduce cost quickly?
Shut down waste, enforce monitoring, right-size compute, reduce region traffic.

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Why will 2026 be worse without action?
RAG and agent workloads combined with transparency requirements will increase complexity and volatility.

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Sources:
1. Flexera – State of the Cloud (2025)
2. Boston Consulting Group (BCG)
3. TechRadar / Sapio Research / Crayon
4. Europäische Kommission – OOTS Cloud Infrastructure
5. ISACA – Cloud Data Sovereignty
6. BearingPoint – European Sovereign Cloud Strategy

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