Bring Financial Accountability to Enterprise LLM Usage with Konnect Metering and Billing
Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs.
This is very similar to what happened with cloud.
In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance got the bill. Engineering got the blame. No one had the data to make good decisions. It took years of hard-won FinOps discipline to fix that.
LLM spend is on the same trajectory and moving faster. LLM cost management is essential.
The data is already alarming. According to the 2025 State of AI Cost Governance Report by Mavvrik, 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.
This is the hidden AI fragmentation tax: the compounding cost of running AI workloads across disconnected tools, providers, and environments with no unified view of what anything actually costs.
The unit of consumption driving much of this is not a VM or a storage bucket. It is the token. And — depending on your approach to LLM consumption visibility — tokens are invisible until the invoice arrives.
This is where showback and chargeback come in.
Showback and chargeback are not the same thing. Most organizations conflate these two concepts, and that conflation delays action. Understanding the LLM showback vs chargeback difference is the first step toward effective AI cost governance.
Both are valuable, and each has its place given certain use cases, organizational imperatives, etc. No matter which your org needs, you’ll need to build the same foundation: a platform that can attribute LLM token consumption to a specific team, application, or service, and translate that consumption into actual dollar cost in real time.
Most enterprises do not have that foundation today.
Kong Konnect Metering and Billing is here to fix this, with a new built-in LLM cost registry and cost analytics layer that is purpose-built for this problem. It serves as the definitive LLM token cost visibility tool for modern AI architectures.
Rather than requiring teams to instrument their own cost tracking, the platform automatically updates the registry of per-token pricing across major LLM providers and model versions. Whether you need to integrate OpenAI pricing with Kong Konnect, track Anthropic usage, or monitor open-source deployments, the system handles it natively. Every AI API call flowing through the Kong data plane is tagged with consumption data and priced automatically, without any custom code. If you're not leveraging market pricing but need custom contract pricing, simple overrides can be configured at each model.
That data flows directly into cost analytics dashboards that let platform and FinOps teams answer the questions that matter:
From there, the path to potential chargeback is straightforward. Cost data can be allocated back to business units using the same metering infrastructure that already tracks API consumption, with no separate tooling required.
Not every organization is in the same place on this journey. Some are just getting started with AI cost visibility. Others are ready to run full financial chargeback with enforced entitlements. Kong supports all of it, and the path is designed to let you start simple and scale up as your program matures. We describe this path through the lens of four “levels.”
Level 1: Basic showback tied to API call volume. This is the fastest starting point. Using Kong with observability, you get immediate visibility into request volume by team or service — who is calling what, and how often. No billing infrastructure required. This is the right entry point for organizations that want to see the API-request-specific portion of their cost landscape before they decide how to act on it. This level — while still valuable — doesn't bring in AI-aware dimensions like token counts, agent runs, compute, storage, etc.
Level 2: Advanced showback with richer metrics. As your AI footprint grows, basic request counting stops telling the full story. Konnect Metering and Billing brings in a wider array of consumption metrics: agent runs, actual token counts, provider-level cost data, model version tracking, trend analysis over time, and more. This gives FinOps and engineering teams a much more precise picture of where spend is concentrated and how it is evolving.
Level 3: Advanced showback, chargeback, and entitlement enforcement. This level combines the power of Konnect Metering and Billing with Kong AI Gateway to meter, charge for, and enforce limits around AI consumption — all in a single platform layer. Teams or products can be given usage entitlements. When they approach or hit those limits, enforcement happens in real time at the gateway layer, before overruns occur. Importantly, this enforcement capability stands on its own — organizations can implement usage controls without needing full invoicing infrastructure in place.
Level 4: Full chargeback with invoicing. When visibility and control need to become financial accountability, Konnect Metering and Billing supports true chargeback. Usage data is aggregated by business unit, team, or product, and drives real invoicing back to the teams responsible. This is how you make AI cost a first-class input into budget planning rather than an unpleasant surprise at the end of the quarter. It is the architecture that makes AI unit economics predictable and defensible at enterprise scale.
...companies that master cost visibility and control will protect their margins while competitors watch profits disappear into untracked infrastructure costs.
LLM spend is not yet a crisis for most enterprises. But that window is closing.
The fragmentation tax compounds quietly: token costs stack up across providers, teams scale AI workloads without budget guardrails, and by the time finance surfaces the problem, the habits are already baked in.
The organizations that will manage AI costs well at scale are the ones building cost visibility into their AI platform infrastructure today, before usage explodes and before the invoices become uncomfortable.
Showback and chargeback are not accounting exercises. They are signals that help teams make better decisions.
For engineering, that means knowing when to cache, when to use a smaller model, and when a workflow is consuming more than it should.
For product and business owners, it means knowing when a feature or workflow is economically viable, where budget is being absorbed, and how to plan with AI cost as a real input rather than an afterthought.
As the Mavvrik report puts it: “companies that master cost visibility and control will protect their margins while competitors watch profits disappear into untracked infrastructure costs.”
A platform that can't answer "what did that cost?" can't support the kind of deliberate, scalable AI deployment that enterprises need. LLM cost intelligence in Konnect Metering and Billing is how Kong closes that gap.
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