AI: When the price you see isn't the price you pay

Netflix’s premium subscription recently passed £20 a month for the first time. Streaming prices keep climbing, and most people notice, because the cost of each service shows up clearly on a bank statement every month. Enterprise AI spending doesn’t offer that same clarity, and the gap is becoming harder to ignore. Flexera’s own research found just 31% of organisations have full visibility into their AI spend, which is a smaller number than most finance leaders would likely expect.

 

That gap starts with how AI itself runs. Every AI decision has a price tag, because AI doesn’t just answer a question; it reasons, retries, calls other services and pulls in data just to complete one task. 

 

Each of those steps can be billed separately, across models and data platforms, so the price a business sees rarely matches the cost it eventually pays. That distinction matters more as AI takes on a larger share of the technology budget. Goldman Sachs expects agentic AI to drive a 24-fold increase in token consumption by 2030. If organisations can’t see where that consumption is coming from, they have little chance of controlling it as it scales.

 

Consumers get a single number. Businesses get a puzzle

A streaming subscriber can look at their statement and know exactly what they’re paying for and exactly what they’re getting. An enterprise buying AI capability is piecing that picture together from several invoices that don’t connect to one outcome. A model’s headline price can look competitive and still have little to do with what a task actually costs once infrastructure and data services are added in. 

 

Finance teams are often the ones expected to weigh cost against value, and they can’t do that properly when cost is scattered across systems and billed by different vendors on different terms. What they need is visibility into the total cost of getting a task done, from the first prompt to the final output, so they can judge whether the value created justifies the spend. Without that visibility, comparing AI to the cost of a salary or a legacy process isn’t really a comparison. It’s more an assumption wearing the shape of one.

 

What the subscription market gets right

Streaming services compete partly on being easy to understand. Customers can decide quickly whether a subscription is worth keeping, because they know what shows they get and what it costs each month. Enterprise AI buyers deserve that same starting point. Comparing AI costs is harder than comparing subscription tiers, not because the technology can’t be measured, but because no vendor is yet measuring it the same way as anyone else.

 

Solving this means building a neutral way to measure AI cost and return that doesn’t depend on each vendor doing their own measuring. Every provider currently presents price differently, and at the end of the day, they are unlikely to recommend moving to a competing vendor if they provide better value

 

That’s starting to change. Bodies like the Linux Foundation and their new Tokenomics Foundation have begun defining common, vendor-neutral measures for AI economics. It’s setting out to define common, vendor-neutral measures for AI economics. 

 

Standards like that take time to build and become the norm, but the direction is right. Businesses shouldn’t have to take a vendor’s word for what something costs, any more than a customer should have to take a streaming platform’s word for what represents good value.

 

Visibility first, optimisation second

The starting point should be tracking AI cost at the level of the workload. That means looking past the advertised price of whichever model is in use and building a clear picture of what it actually costs to complete the tasks that matter, across every service involved in getting there. It also means asking vendors harder questions upfront about how costs accumulate, rather than finding out on an invoice months later. The bigger risk isn’t picking the wrong model but making financial decisions before understanding the economics behind them.

 

In the next few years, AI is likely only going to take up more of the corporate technology budget. The businesses that get ahead of this will be the ones that treat cost visibility as a discipline now, not the ones waiting for the industry to standardise it for them. 

 

Consumers have had transparent pricing for years, because providers had commercial reasons to offer it. Enterprise AI is heading toward the same expectation. The organisations that start asking these questions early will be the ones making more informed investment decisions once the answers get easier to find.

 

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Marlon Oliver is SVP EMEA at Flexera

 

Main image courtesy of iStockPhoto.com and asbe