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24/06/2026 7:28 pm
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?
Explore further
25/06/2026 4:46 am
LLM cost governance is becoming an identity problem, not just a finance problem. The moment AI usage is attributed to a team, service, or workflow, the programme is doing a form of identity governance over machine-driven consumption. That is why showback and chargeback matter beyond FinOps. They define ownership for non-human usage in the same way IAM defines accountability for access. Practitioners should treat cost attribution as a governance primitive, not a reporting extra.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who should be accountable for AI overspend when multiple teams share the same model?
A: Accountability should follow the consuming team, product, or service, not the model provider. Shared infrastructure still needs a named operational owner for allocation, exceptions, and policy enforcement. If no one owns consumption, costs will remain diffuse and governance will stay informal.
👉 Read our full editorial: LLM showback and chargeback expose the hidden AI fragmentation tax
