Your AI Employee Has a Rate Card. You Set It.

I had an agent doing work in a production system I run, and the work cost twenty times what it needed to because I had picked a model that was more capable than the job required.

I changed it within a few days, because at that price the system wasn't going to pay for itself. The work stayed the same, the output quality held, and the bill fell to one twentieth.

That's what I want you to look at before you decide an AI employee is cheap or expensive. The model you choose, how hard you let it think, how much information it can reuse and where it runs all change what you pay for the same job.

You have more control over this bill than you probably realize, and somebody in your firm needs to use it.

When you staff an AI employee, you add a cost that grows as it does more work. A salary usually stays the same through a quiet week and a busy one, at least until you need overtime or another person, but each new agent run can add to the bill.

Service firms already have costs that vary with the work, of course, contractors, outside services, all sorts of things. What is different here is how much the price of doing the same task can change when you change the settings underneath it.

Let me give you the numbers from my system, because a bill you can picture is more useful than a warning about runaway AI costs.

Across 2,000+ agent tasks in a production system I run, in July 2026, the middle cost for an active user conversation was about eight to nine cents. The average was seventeen to eighteen cents, because some expensive tasks pulled it up.

More than half of the user queries cost between two and ten cents. A customer service summary came in at about eight cents, a full project analysis at fourteen cents, and a monthly sales review at $1.57.

Those are different jobs, so please don't take the cheapest one and use it to price everything your team might ask for. The most expensive single task I saw in that set was a project run that cost $16.12.

At the other end, an automated monitor running in the background cost about a tenth of a cent per run. A daily collections nudge cost a fraction of a cent too, so the gap inside one system was much wider than the average makes it look.

Now, if I include those cheap background monitors in the calculation, the middle cost drops to about three cents. If I count only the active user conversations, it's back around eight or nine.

Neither number is dishonest, but they answer different questions. Before someone tells you what an AI task costs, ask what they counted as a task, because you can't compare one person's background checks with another person's project work.

It's the same discipline behind the Four Numbers, a number is useful when you know what went into it. Otherwise the next month can look better just because the system did more cheap checks, even if the work you care about got more expensive.

And there is something about my setup you need to know before using these figures as a comparison. I optimize heavily for caching, which means the system can reuse information it has already processed instead of paying the full rate to process it again.

That is part of why these costs are low. Even within my examples, the effective cost per million tokens, the small pieces of text the model processes, ranged from about three cents to thirteen cents in the cases I compared, so counting tokens alone would not tell you the bill.

You shouldn't expect my exact prices from a system built differently. They describe my workload and my settings in that month, not a price list for your firm.

The first setting I would look at is the model. That was my twenty-times mistake, and it didn't need a new business process to fix, it needed someone to look at the work and ask whether we were paying for more intelligence than it required.

We don't need Einstein to fetch data from a website. We do want a capable model helping us read a customer report and flag the inconsistency we don't want to discover in a client meeting.

Those jobs should not get the same choice by default. For the website task, use the cheapest model that reliably gets the right data, and check what it brings back.

For the report, the price of missing something matters too. A model that saves a few cents and leaves your senior person fixing the report for half an hour has not made the job cheaper.

Then look at the reasoning setting, how hard you ask the model to think. Where that setting is available, the same model can spend more or less effort on the task, and you need to see whether the extra effort improves the result enough to justify the cost.

Caching is another choice, because repeated company instructions and background information can be arranged so the system reuses them. That is work for whoever builds or maintains the system, but as the owner you should know whether anyone has looked at it.

And you can change where the model runs. Open-source models on your own hardware have a different cost structure from a cloud service, but the hardware and the person maintaining it aren't free, so I would not start there if the whole bill is only a few hundred dollars a month.

There is one combination I particularly want you to try before replacing every cheap model with an expensive one.

Use a cheaper model to do the work, then have a stronger model check and guide the result. That second model is a validator, and it runs on the output rather than doing every step itself.

I've found that this can give you a better result than the cheap model alone while costing less than using the stronger model throughout. Adding a check sounds like adding cost, but it can let you lower the cost of the much larger part of the job.

You still have to test that combination on your own work. If the checker misses the same mistakes, or sends everything back for another attempt, you need to count that too, not assume that two models are automatically better than one.

ICONIQ's 2026 State of AI snapshot describes companies sending most workloads to smaller or fine-tuned models and reserving the strongest models for the harder tasks. That point comes from operating perspectives in its community, alongside a survey of around 300 executives building AI products, not a controlled test of my setup.

The human version is the named validation gate, where someone is responsible for deciding whether the work clears the standard. The machine check helps that person, it doesn't take away their responsibility for what reaches a client.

So how do you put this underneath a retainer without letting one busy month eat the fee?

Start by separating what the client buys from what it costs you to deliver it. The client can buy a finished outcome, a block of capacity or an ongoing service at a stable price, while you track the changing agent cost inside that price.

Software vendors do versions of this too, but read the unit they charge for carefully. Fin lists $0.99 for resolutions, configured procedure handoffs and disqualifications, while qualifications cost $9.99, and it charges only once per conversation.

That handoff detail matters. A conversation simply passed to your team without an outcome is free, but a procedure you configured to finish with a human handoff can be billable, so "it reached a person" does not mean "we won't be charged."

Zendesk describes pricing by automated resolution. Salesforce's May 2025 Flex Credits announcement priced an action at ten cents, and those are different billing units, not prices you can put side by side as if they buy the same thing.

Anthropic's guide to its usage-based Claude Enterprise plan describes another version, a seat fee for access with usage billed on top. The useful distinction for you is the one between access and consumption, not copying another company's contract.

A vendor handling huge volumes can spread variation across far more work than a small firm can. But a small firm can still sell a fixed fee responsibly, provided the scope, expected use, review work and spending limits are part of the decision.

For the budget, use the average cost for the mix of tasks you expect, multiplied by the number you expect to run. The middle task tells you what is typical, but it leaves out the effect of the expensive work, and the bill includes that work too.

At seventeen to eighteen cents on average, a thousand comparable conversations would cost roughly $170 to $180. That is an illustration using my July workload, not a quote for a thousand of whatever your clients might request.

Then look at the worst tasks when you set limits. Put a ceiling on spending per run and per engagement, decide how retries stop, and decide who gets called when the limit is reached, because the most expensive task you've seen is not a guarantee about the most expensive task you'll ever see.

To put the scale in perspective, imagine $175 of monthly agent spend supporting a $5,000 retainer. That's 3.5% of revenue, and it is only the agent-spend line, not the full cost of delivering the engagement.

Bessemer's discussion of 50% to 60% gross margins for AI companies, compared with 80% to 90% for SaaS, is about businesses selling AI products. You cannot take that comparison and tell a service-firm owner that a small agent bill will push their company into the same margin range.

And a retainer does not keep the gain by itself. You still need to choose what you sell and how you price it, because a lower delivery cost and a higher fee are separate decisions.

For most service engagements I would keep the agent spend in the internal cost sheet rather than create a new invoice line for every run. The client is buying the result, subject to whatever disclosure and billing terms you've agreed, and you need to know whether that result pays before you promise more of it.

I also expect the workload to change as people get better at using the system. In my experience, heavy users ask for harder work and pull the average up, while cheaper and more capable models can push the cost of a comparable job down.

I don't know which force will win in your firm. ICONIQ also reports rising internal AI spending as a share of revenue as adoption matures, but that does not tell us what the next month of your service work will cost.

That is why this needs a standing owner rather than a setup you never revisit. I expect cost-tuning responsibilities to grow, but at your size that doesn't have to mean hiring somebody with a new title.

Give someone already responsible for the system a monthly review of spending by task, what changed, what got rejected and what needed another attempt. Thirty minutes is a reasonable place to start that review, not a promise that every problem it finds can be fixed in thirty minutes.

This is part of the move from Stage 2 toward Stage 3, where the firm can take on more work without adding people at the same pace. Some responsibilities appear as the work changes, and owning this bill is one of them.

Before your next review, pull last month's agent spend, split it by task and look at the average alongside the typical and most expensive runs. Then ask whether the model and the checking step were chosen for those jobs, or whether you're still using whatever somebody picked in the first week.

That was my mistake. You don't need to keep paying for it.