Category: Billing Models

Per-seat pricing worked because seats and value moved together. More people using the software meant more work getting done, so charging per person was a rough but honest proxy.

AI agents break the proxy. An agent does the work and never logs in.

The arithmetic that panicked the industry

Take a support team of fifty on a CRM at $150 a seat. Deploy agents that handle most of the ticket volume, keep fifteen people for escalations, and the work still gets done. The vendor’s revenue from that account falls by seventy percent while the customer’s outcome is unchanged or better.

Nobody churned. Nobody complained about price. The unit the contract was denominated in simply stopped correlating with the value delivered.

That is the whole story, and it explains behaviour across the industry in 2026 that otherwise looks like ordinary greed.

What vendors are doing instead

Four responses, and most large vendors are running more than one.

Metering the agent. The seat survives, wrapped in a consumption meter for delegated work. Salesforce, Microsoft, ServiceNow, Workday, Zendesk, HubSpot and Atlassian have all added a version of this. Reporting puts Salesforce’s agent revenue at around $800 million in a single recent quarter, and Microsoft has added a separate per-user charge for agent governance in the region of $15.

Licensing the agent as a user. Agents get identities, authenticate, and appear in the billing system much as employees do. Conceptually tidy, and it makes the seat count go back up.

Outcome pricing. Charging per resolved ticket, reviewed contract or qualified lead. Goldman Sachs has taken to calling this Results-as-a-Service. Attractive in a demo, hard to write, since it requires both sides to agree what counts as a result and who adjudicates when they disagree.

The flat agentic bundle. Salesforce’s Agentic Enterprise License Agreement replaces per-seat and consumption billing with a flat unlimited-use fee across a multi-year term. Predictability in exchange for commitment and lock-in, which is a genuine trade rather than a trick.

How fast this is moving

Seat-based arrangements are reported to have fallen from roughly 21% to 15% of enterprise software contracts in about a year. IDC expects 70% of vendors off pure per-seat models by 2028. Hybrid structures, a base fee plus a variable component, are said to be running around 43% of SaaS companies and heading toward 61%.

Numbers like these come from analyst and vendor research with an interest in the narrative, so hold them loosely. The trend is corroborated by the thing that is hardest to spin, which is what vendors actually shipped: nearly every major platform changed how it charges within the same eighteen months.

What it means if you are buying

The comfortable assumption to abandon is that headcount reductions produce software savings. Under the new models they may not, and under a flat agentic bundle they explicitly do not.

Practical consequences:

• Your renewal maths changes. Cutting seats used to be the reliable lever. If the value has moved into a meter, cutting seats saves less than you expect and may trigger an audit. See what triggers a software audit.
• Two vendors are no longer comparable. One prices per seat, one per outcome, one per credit. Comparing them requires modelling your own workload, not reading a pricing page.
• Agents multiply, employees do not. Headcount is bounded by hiring. Agent count is bounded by whoever can deploy one, which is a much looser constraint and a much faster-moving cost.
• Term length is the real negotiation. Flat bundles buy predictability with lock-in, at exactly the moment the market is repricing. Two years is a long time to be certain about this.

The one thing to do now

Find out, for your largest contracts, whether the metric has already changed. Not the price. The metric.

A per-seat agreement that quietly became per-seat-plus-consumption is a different contract from the one you signed, and it usually arrives as a product announcement rather than as a commercial notice. Most organisations discover it on an invoice.

Tracking those changes is what CopperFeed is built for. The credit mechanics are in AI Credits Explained, and forecasting against them in How to Budget for Usage-Based Pricing.

Figures here come from published analyst and press reporting rather than vendor disclosure, and are indicative. General guidance, not procurement advice.

Most writing about usage-based pricing is aimed at the companies selling it. Metering platforms, billing infrastructure, advice on migrating your pricing model. Very little is written for the person who has to put a number in next year’s budget for something with no fixed price.

This is that. It assumes you did not choose this model and cannot opt out of it.

Why the usual budgeting approach fails

A seat-based line item is trivial to forecast. Headcount times rate, adjusted for growth. You could do it on a napkin and be close.

Usage is not distributed like headcount. It is distributed like a power law, and this is the single fact that breaks most forecasts.

Within the same plan tier, individual consumption commonly varies by more than an order of magnitude. Your median user is not your average user, and your average is dragged around by a handful of heavy ones. Budget from the median and you will be wrong by a multiple. Budget from the mean without knowing its shape and you will still be wrong, because the mean is unstable when the tail moves.

The practical consequence: a pilot with ten people tells you very little about a rollout to two hundred, unless you looked at the distribution rather than the total.

Build the forecast from the tail

Run a pilot long enough to see a full work cycle, then throw away the average.

What you want is per-user consumption sorted highest to lowest. Look at your top decile, because they are the ones who will define your bill, and look at what makes them different. Frequently it is a role rather than a personality: the people doing the work the tool is genuinely good at will use it constantly, and they are the reason you bought it.

Then forecast three numbers rather than one. What it costs if usage looks like the pilot. What it costs if the heavy decile becomes a quarter of the org as the tool catches on, which is the realistic case for anything useful. And what it costs at the ceiling, which is the number your CFO will ask for and the one nobody prepares.

Present all three. A single figure implies a confidence the model does not support, and you will own that number when it is wrong.

Where the surprises come from

Rarely from people using the tool more. Usually from something changing underneath.

• A model swap. The vendor routes to a newer, larger model. Your behaviour is identical and your consumption is not.
• A feature that quietly costs more. Agentic workflows, background jobs and long-context features consume dramatically more than chat, and they usually launch as an improvement rather than as a price change.
• Automation. Something gets wired into a pipeline and starts running without a human triggering it. The bill stops being a function of headcount, and no seat-based instinct catches it.
• The conversion rate. If your contract lets the vendor change what a credit buys, they can raise your price without touching a published figure.

Only the third of those is under your control, which is why the contract questions matter more here than in any seat-based deal.

Controls worth having

In descending order of how much they help.

1. A hard cap you administer. Not a notification. A ceiling where spend stops. Vendors do not offer this readily and it is the most valuable thing you can negotiate.
2. Per-user or per-team limits. Turns one uncapped organisational exposure into many small bounded ones, and it puts the decision next to the person who understands the work.
3. Alerts at 50, 75 and 90%. Standard advice, worth doing, and weaker than it sounds. An alert tells you the money is already gone.
4. A monthly actuals review for the first quarter. Boring and effective. Most overruns are visible for weeks before anyone looks.

The negotiation is different too

Discount percentages matter less than they do on a subscription, because the discount applies to a quantity nobody has agreed on. What matters is the floor, the ceiling and who controls the rate.

Push for a committed spend with a rate that improves at volume rather than a large allotment you may not use. Push for unused capacity to roll. And get a written commitment that the conversion rate is fixed for the term, which is the clause that decides whether your forecast means anything at all.

If a vendor will not fix the rate, that is not a detail to concede late in a negotiation. It is the price being variable at their discretion, and it should change what you are willing to commit.

What credits are and how to read them is in AI Credits Explained. The structural shift behind all of this is in The End of Per-Seat Pricing. CopperFeed records billing model changes as they happen, including the ones that arrive without an announcement.

General guidance, not procurement or financial advice. Contract terms vary and yours govern.

AI credits are the unit almost every enterprise vendor invented in 2026 to charge you for AI features. You buy an allotment, your usage draws it down, and when it runs out you either stop or start paying overages.

The pitch is that credits give you predictability. What they mostly give you is a currency you cannot price, cannot compare, and cannot forecast.

Every vendor minted its own money

SAP has AI Units. Oracle has AI Units, which are not SAP’s. Microsoft has Copilot Credits and ACUs. Workday has Flex Credits. ServiceNow meters Assist consumption, AWS has Bedrock AgentCore, Google has Agent Engine.

None of them are comparable. Each meters differently, converts differently, and expires under different rules. A credit is not a unit of anything. It is a private currency issued by the company selling you the goods, at an exchange rate it sets and can change.

That is the part worth sitting with before you evaluate any of it on price. You are not comparing rates. You are comparing seven currencies against a basket of goods nobody has itemised.

What a credit is actually pegged to

Usually tokens, sometimes actions, occasionally both, and the conversion is frequently unpublished.

Tokens are the underlying unit of model usage: text going in, text coming out, plus cached context. Most buyers have no intuition for how many tokens a task consumes, which is the root of the problem. Nobody can estimate an annual spend denominated in a unit they cannot picture.

Vendors are candid about this in places. Abacus AI states plainly that its credits are not tokens and that it does not publish exact per-model credit rates, because the rates keep changing. That is honest, and it also means the price of the thing you are buying is not knowable at the time you buy it.

Where a vendor does publish, read carefully for three things: whether cached and input tokens are billed at the same rate as output, whether credits expire monthly or roll over, and whether the model you actually use is priced differently from the default one in the marketing table.

The bills that made this famous

Zylo’s 2026 SaaS Management Index surveyed 218 IT leaders and found 78% had hit unexpected charges tied to AI or consumption in the past year. Not a fringe experience. The majority.

The GitHub Copilot transition on 1 June 2026 is the case study, because it was well signposted and still went badly for heavy users. GitHub replaced premium request units with AI Credits metered on token usage, and held base plan pricing exactly where it was. Reporting since has described individual bills rising as much as sixtyfold, from $29 to around $750 and from $50 to roughly $3,000. Uber is reported to have exhausted its 2026 AI coding budget by April.

Those individual figures come from secondary coverage rather than from GitHub, so treat the multiple as illustrative. The direction is not in dispute, and the mechanism is the point: the plan price did not change at all. The plan price was never where the money was.

Why credits exist

Not villainy. Inference costs real money per use, inside a business model built on near-zero marginal cost, and a flat subscription over variable cost is a losing trade for the vendor when usage is unbounded.

Credits solve a genuine problem. They also happen to solve several others in the vendor’s favour: revenue that grows without a renegotiation, a price that can be changed by adjusting a conversion rate rather than a published number, and breakage on unused allotments. A mechanism can be both necessary and asymmetric.

What to ask before you sign

1. What is the conversion rate, in writing, and can you change it during our term? The single most important question, and the one most likely to get a vague answer. A rate the vendor can move unilaterally is a price they can raise without telling you.
2. Do unused credits roll over or expire? Monthly expiry means you are buying peak capacity every month and discarding the difference.
3. What happens at zero? Hard stop, automatic overage, or a call from your account manager. Each has a very different failure mode, and the hard stop is not always the worst one.
4. Can we cap it? A hard ceiling you control, not an alert. Alerts arrive after the money is spent.
5. What does a typical week cost for one heavy user? Make them model it. If they will not, that tells you they cannot, which tells you something about your own forecast.

Instrument it before you need to

Whatever you agree, assume the first two months of actuals will surprise you, and set alerts at 50%, 75% and 90% of the allotment so you find out while there is still time to change behaviour.

Then watch for the change nobody announces. Conversion rates move, allotments get restructured, and a model swap in the background can change your consumption without any decision on your side. That is what CopperFeed records: dated entries for repricings and billing model changes as they land.

Related: How to Budget for Usage-Based Pricing, and The End of Per-Seat Pricing.

Figures here come from published research and reporting, attributed inline. Individual bill increases are drawn from secondary coverage and not confirmed by the vendors involved. Verify current terms before acting on any of it.