Never Trust a Vendor Selling the Cure to a Disease They Created: The FinOps Conflict of Interest

Chef's Rule: Never Trust a Vendor Selling the Cure to a Disease They Created

AI workloads are the #1 driver of uncontrollable cloud costs. The vendors who sell you tools to manage those costs charge 3–5% of your growing cloud spend. When your AI bill goes up, their revenue goes up. This is the pharmaceutical model: create the ailment, sell the treatment.

The Numbers Nobody in FinOps Wants to Discuss

Let's put the uncomfortable facts on the table:

72%

of IT and finance professionals say AI-driven cloud spending is becoming unmanageable

Source: Tangoe State of Cloud Report, 2025

30%

average year-over-year increase in enterprise cloud costs, driven primarily by AI workloads

Source: Vanson Bourne / Tangoe, 500 enterprises surveyed

$758B

projected global AI infrastructure spending by 2029 — up from $82B in a single quarter (Q2 2025)

Source: IDC AI Infrastructure Tracker

$1.03T

projected public cloud spending in 2026 — crossing the trillion-dollar mark for the first time

Source: Forrester Public Cloud Market Outlook

And every FinOps vendor's 2026 pitch? “AI-powered cost optimization.” Read that again. AI workloads are driving your cloud bill through the roof, and the proposed solution is... more AI. Layered on top of tools that charge you a percentage of the bill they're supposed to reduce.

The Vendor Business Model You Should Understand

Most FinOps tools charge based on a percentage of your cloud spend under management. Here's what that actually means for your budget:

Vendor	Pricing Model	On $5M/yr Spend
CloudHealth (Broadcom)	~3% of cloud spend	$150,000/yr
Vantage	2.25–2.5% of cloud spend	$112,500–$125,000/yr
Finout	~1% of cloud spend	$50,000/yr
Enterprise platform (typical)	3–5% of cloud spend	$150,000–$250,000/yr

Sources: AWS Marketplace listings, vendor pricing pages, Deloitte research (2025). Actual pricing varies by contract.

The Incentive Problem

When your AI bill increases from $5M to $8M next year (a common trajectory), the FinOps vendor's revenue automatically jumps from $150K to $240K. They earn more when you spend more. The tool designed to reduce your cloud costs generates more revenue when those costs grow.

This doesn't mean every vendor acts against your interests. Many deliver genuine value. But the structural incentive misalignment is real, and you should account for it when evaluating solutions.

The FinOps Track Record: Honest Assessment

The FinOps movement has achieved enormous adoption. But let's look at outcomes, not adoption metrics:

Self-reported cloud waste~32% of total spend

Source: State of FinOps Survey, FinOps Foundation

Container compute waste>80% idle resources

Source: Datadog State of Container Costs

Waste reduction as #1 priority72% of practitioners

Source: State of FinOps, FinOps Foundation

Years of FinOps adoption. Thousands of certified practitioners. Cloud waste went from roughly 35% to 32%. That's a trillion-dollar cloud market with over $330 billion in annual waste that has barely moved.

What FinOps Got Right

Created a common language between finance and engineering
Made cloud cost visibility mainstream
Built a community of practice that didn't exist before
Pushed cloud providers toward better billing transparency

Where FinOps Fell Short

Waste percentage barely moved after years of adoption
40% of practitioners can't get engineers to act on recommendations
Created a cottage industry of dashboards, not behavioral change
Certification culture prioritized credentials over outcomes

The AI Cost Spiral: Why This Is Different

Previous cloud cost surges (lift-and-shift, container sprawl, serverless overuse) were manageable because the cost drivers were relatively transparent. AI workloads are different:

GPU Costs Are an Order of Magnitude Higher

A single p5.48xlarge instance (8x H100 GPUs) costs $98.32/hour on-demand — over $71,000/month. A model training run that overruns by a weekend burns more than most teams' monthly compute budget. Traditional FinOps tooling built for $0.10/hour EC2 instances wasn't designed for this magnitude.

Inference Costs Scale Unpredictably

Training is expensive but one-time. Inference runs continuously and scales with user adoption. When your AI feature goes viral, the per-request inference cost compounds at a rate that traditional auto-scaling budget alerts can't catch. Some enterprises are seeing monthly AI bills in the tens of millions.

Shadow AI Is the New Shadow IT

38% of SaaS spending is already attributed to shadow IT. Now add AI API keys, team-level OpenAI subscriptions, unapproved fine-tuning jobs on managed endpoints, and experimental SageMaker notebooks that nobody turns off. AI spend is harder to track because it's embedded in application code, not infrastructure templates.

55% of AI Spend Is Now Inference, Not Training

IDC data shows inference now exceeds training in infrastructure spend. This means the expensive phase isn't the one-time model build — it's the ongoing, 24/7 serving of requests. And it grows linearly (or worse) with adoption.

What Actually Reduces Cloud Waste (No $300K Tool Required)

The fix isn't another AI-powered dashboard. It's three engineering practices that cost nothing to implement and work because they change behavior, not just visibility.

Engineering Cost Ownership: Make Devs See What They Spend

40% of FinOps practitioners say getting engineers to act on cost recommendations is their top challenge. The reason: engineers don't see costs. They deploy infrastructure and forget about it. Cost data arrives weeks later in a finance dashboard nobody on the engineering team has access to.

Tag every resource with the owning team. Use AWS Config Rules or SCPs to enforce Team and Service tags. No tag, no deploy.

Send weekly cost reports to team Slack channels. Not a dashboard link — the actual numbers, in the channel they already read. “Your team spent $14,200 last week, up 18% from last week. Top cost: idle SageMaker notebooks ($3,100).”

Include cost in sprint reviews. Add a 2-minute “cost update” to every retrospective. When engineers see that their feature costs $0.008 per request instead of the $0.003 target, it becomes a technical problem to solve — not a finance complaint to ignore.

Gamify it. 30% of FinOps respondents cite “celebrating successes” and gamification as effective tactics. Leaderboards showing which team reduced cost-per-request the most create healthy competition.

# Slack bot: Weekly team cost alert (using AWS Cost Explorer API)
aws ce get-cost-and-usage \
 --time-period Start=2026-02-03,End=2026-02-10 \
 --granularity DAILY \
 --filter '{
"Tags": {
"Key":"Team",
"Values": ["platform-engineering"]
 }
 }' \
 --metrics"UnblendedCost" \
 --group-by Type=DIMENSION,Key=SERVICE

Cost Gates in CI/CD: Fail Deploys That Exceed Budget

Just as security shifted left into CI/CD pipelines over the past decade, cost accountability needs the same treatment. The idea: estimate infrastructure cost changes before they hit production, and block deploys that exceed thresholds.

Infracost in pull requests. Open-source tool that estimates Terraform cost changes and posts them as PR comments. Engineers see “this change adds $1,200/month” before it merges. Free for open-source and small teams.

Budget threshold gates. Configure your CI/CD pipeline to fail if estimated monthly cost increase exceeds a per-service threshold (e.g., >$500/month increase requires team lead approval, >$5,000 requires FinOps review).

Tag enforcement as a gate. Fail the deploy if required cost allocation tags (Team, Service, Environment) are missing from Terraform/CloudFormation resources.

GPU instance approval workflows. Any deploy requesting GPU instances (p4d, p5, g5, etc.) should require explicit approval with a documented business justification and auto-shutdown schedule.

# GitHub Actions: Infracost cost gate example
- name: Run Infracost
 run: infracost diff --path=. --format=json --out-file=/tmp/infracost.json

- name: Cost Gate Check
 run: |
 DIFF=$(jq '.diffTotalMonthlyCost | tonumber' /tmp/infracost.json)
 if (( $(echo"$DIFF > 5000" | bc -l) )); then
 echo"::error::Cost increase exceeds $5,000/month threshold"
 exit 1
 fi

Kill What You Don't Need: The Oldest Recipe in the Book

No tool, no AI, no dashboard required. This is the unglamorous work that actually moves the needle. The FinOps Foundation's own data shows waste reduction is the #1 priority for 72% of practitioners — because it works.

Weekly zombie hunt. Query for instances with <5% average CPU over 14 days. Query for unattached EBS volumes, unused Elastic IPs, idle load balancers, and SageMaker notebooks running 24/7. Shut them down.

Non-production schedules. Dev and staging environments don't need to run evenings and weekends. Schedule them off. That alone cuts non-production costs by 65%.

GPU instance auto-shutdown. Training jobs that finish but leave instances running are the single most expensive form of cloud waste. Implement auto-stop after idle detection (no GPU utilization for 30 minutes = auto-terminate).

TTL on everything. Every non-production resource gets a time-to-live tag. Resources past their TTL get automatically terminated. No exceptions, no extensions without justification.

# Find zombie EC2 instances (< 5% CPU for 14 days)
aws cloudwatch get-metric-statistics \
 --namespace AWS/EC2 \
 --metric-name CPUUtilization \
 --period 86400 \
 --statistics Average \
 --start-time $(date -u -d '14 days ago' +%Y-%m-%dT%H:%M:%S) \
 --end-time $(date -u +%Y-%m-%dT%H:%M:%S) \
 --dimensions Name=InstanceId,Value=i-0abc123def456

# Find unattached EBS volumes (instant savings)
aws ec2 describe-volumes \
 --filters Name=status,Values=available \
 --query 'Volumes[*].{ID:VolumeId,Size:Size,Type:VolumeType}' \
 --output table

Fair Point: When FinOps Tooling Does Make Sense

This isn't an anti-vendor screed. There are legitimate use cases where FinOps platforms earn their keep:

Multi-cloud visibility at scale

If you're spending $50M+ across AWS, Azure, and GCP, normalizing billing data across three providers is genuinely hard. A vendor can save your team hundreds of hours.

Automated commitment management

Savings Plans and Reserved Instance portfolio optimization at scale requires algorithms that track utilization across hundreds of accounts. Tools like ProsperOps or Spot.io charge on savings, aligning incentives better.

Kubernetes cost allocation

Attributing shared cluster costs to individual workloads is technically complex. Open-source tools like OpenCost handle this, but if you need enterprise support and integration, a vendor may be justified.

Anomaly detection at volume

If you have 500+ accounts with thousands of services, detecting cost anomalies manually is impractical. This is where AI-powered tooling provides genuine, non-ironic value.

The litmus test: Does the tool change behavior or just provide visibility? If it's another dashboard that nobody opens, save your money. If it puts cost data in the hands of engineers at the moment they make decisions, it's worth evaluating.

How to Evaluate FinOps Vendors Without Getting Played

If you do need a tool, ask these questions before signing:

“How do you make money when my cloud bill goes down?” If the answer involves a percentage of spend, you have a misaligned incentive. Prefer flat-fee, percentage-of-savings, or tiered pricing.

“What does your tool do that AWS Cost Explorer + CUR + Athena can't?” AWS native tools are free and increasingly capable. If the vendor can't articulate specific value beyond what's already in your console, walk away.

“Show me a customer who reduced cloud spend by 20%+ using your tool.” Not “identified savings opportunities” — actually reduced spend. With verified numbers. Recommendations don't count; implemented savings do.

“Does your tool integrate with our CI/CD and Slack, or is it another standalone dashboard?” If engineers have to log into yet another portal to see cost data, they won't. Cost insights need to live where engineers already work.

“What happens if we cancel? Can we export all our data?” Vendor lock-in in cost management is ironic. Ensure your cost data, allocation models, and dashboards are exportable or reproducible.

The Free Stack: What You Can Build Today

Before spending $150K/year on a vendor, see how far you get with what's already available:

AWS Cost Explorer + Budgets (Free)

Per-service cost breakdowns, anomaly detection, budget alerts. Already in your console. Set budget alerts per account and per tag.

CUR + Athena + QuickSight (Low cost)

Export Cost and Usage Reports to S3, query with Athena ($5/TB scanned), visualize with QuickSight. The same data every paid tool uses — you just query it directly.

Infracost (Free / Open Source)

Cost estimation in Terraform pull requests. Shows cost impact before merge. Free for open-source and individual use.

OpenCost (Free / CNCF)

Kubernetes cost allocation and monitoring. Open-source, CNCF incubating project. Real-time cost visibility per namespace, pod, and deployment.

AWS Instance Scheduler (Free)

Automatically start and stop EC2 and RDS instances on a schedule. The single most effective cost-saving tool for non-production environments.

Chef's Pro Tip

Before buying any FinOps tool, spend one sprint implementing engineering cost ownership. Tag everything, send cost reports to team Slack channels, add cost reviews to retros. If that alone cuts waste by 15–20% (it usually does), evaluate whether you need a vendor at all.

The most effective cost optimization doesn't come from better dashboards. It comes from making the people who provision resources accountable for the cost of those resources. That's a culture change, not a SaaS purchase.

The Bottom Line

The FinOps industry has a structural problem: the vendors who sell cost optimization tools profit when your costs grow. That doesn't make them evil — it makes them misaligned. Understand the incentive, and you'll make better buying decisions.

The real fix for cloud waste has never been a dashboard. It's engineering cost ownership (make devs see what they spend), CI/CD cost gates (fail deploys that exceed budget), and killing what you don't need (the oldest recipe in the book).

No certification required. No $300K/year tool required. Just engineering discipline and a willingness to look at the numbers honestly.

Sources:

#finops#cloud-waste#ai-costs#cost-optimization#vendor-evaluation#engineering-culture#shift-left#cicd