LLM showback vs chargeback: what do IAM teams need to know?

TL;DR: Enterprises moving AI into production face a familiar but faster cost-control problem, with Mavvrik reporting that 84% of companies see more than a 6% gross-margin hit from AI costs and nearly one in four see 16% or more. The governance issue is not just pricing, but attribution: teams cannot control what they cannot measure.

NHIMG editorial — based on content published by Kong: LLM Cost Management: How to Implement AI Showback and Chargeback

By the numbers:

• 84% of companies report more than a 6% hit to gross margin from AI costs.
• Nearly one in four reports erosion of 16% or more.

Questions worth separating out

Q: How should security teams implement AI showback in production environments?

A: Start by attributing each model call to a specific team, service, or workflow, then capture token counts and pricing in real time.

Q: When does chargeback become more useful than showback for AI governance?

A: Chargeback becomes useful when attribution is stable, pricing rules are understood, and leaders need teams to feel the budget impact directly.

Q: What breaks when AI consumption is not metered at the platform layer?

A: Without platform-layer metering, organisations lose the ability to link cost to ownership, workflow, or service in real time.

Practitioner guidance

• Map AI consumption to a named owner Assign every model, workflow, or agent to a business, product, or platform owner before usage scales.
• Meter tokens at the request layer Capture model, token, and pricing data at the point where AI traffic enters the platform so the organisation can attribute cost in real time.
• Separate showback from chargeback decisions Use showback first to validate attribution and build trust with stakeholders, then move to chargeback once allocation rules and pricing overrides are stable.

What's in the full article

Kong's full article covers the operational detail this post intentionally leaves for the source:

• Level-by-level implementation guidance for moving from basic showback to full chargeback in an enterprise AI environment
• How Konnect Metering and Billing ties token usage to teams, services, and business units for allocation and invoicing
• Examples of how gateway-layer enforcement can stop AI spend overruns before they reach finance
• The article’s product-specific description of how OpenMeter is integrated into Kong’s billing and metering layer

👉 Read Kong's analysis of LLM showback and chargeback for AI cost governance →

LLM showback vs chargeback: what do IAM teams need to know?

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