You can't hand your CFO a number for AI when the bill moves every time someone reruns a prompt. So access gets rationed, and the people left out open whatever free chatbot sits in the next browser tab.
Built for Multiplayer AI, AgentSpot makes AI spend predictable with DO credits (Data to Outcome), counted weekly and tied to work that actually runs. Below, you'll see what one credit covers, what costs nothing, and how to turn a typical week into a forecast finance can sign off on.
Why Is AI Spend Hard to Forecast?
AI spend is hard to forecast because most pricing meters what goes into the work: seats and tokens. Seats grow with headcount, and tokens grow with every prompt, retry, and test run. Can either number tell you what you got for the money?
If you've been asked "What will AI cost us next quarter?", you know the honest answer is usually "it depends." That answer doesn't survive a budget review for long, because finance can't run on guesswork.
When you can't predict the bill, the fastest fix is limiting who gets access. Fewer people use the AI you're paying for, and the ones left out often find their own tools. That's how shadow AI shows up in your stack: through a pricing model that made broad access feel risky, with no bad intent required.
What Does Per-Seat Pricing Cost Multiplayer AI?
Per-seat pricing charges you for every person who might touch an agent, and that runs against how shared agents create value. Multiplayer AI gets more useful as more people run the same agents, workflows, and apps. When each new user means another license, every share turns into a budget decision.
Say your finance team builds a forecasting agent that regional VPs would love to use. Should they get access? Under a seat model, that question lands with procurement before it reaches the VPs.
In practice, seats put a toll on the network effect that makes shared agents worth building. The agent works the same for 10 people or 100, yet the cost climbs with every name you add.
Why Does Per-Token Pricing Create Token Anxiety?
Per-token pricing creates token anxiety because the meter runs on effort, whether or not the effort pays off. You pay while you build an agent and while you test it, and you pay again each time a step fails and retries.
Add multi-agent AI and the bill gets harder to predict, since one request can pass through several agents and models before a result comes back. A single question might trigger a research step, a calculation, and a summary, and each one adds its own token count. Would you want to explain a spike that came from someone debugging a prompt on a Friday afternoon?
So you hesitate. You experiment less, and you set usage caps you'd rather not set.
Model choice happens behind the scenes, since AgentSpot picks the right model for each job and routes between them. As a result, you never have to think about token budgets.
Which Three Principles Shape AgentSpot Pricing?
Three principles shaped how AgentSpot prices usage: maximize access, keep spend easy to predict, and charge only for positive outcomes. Here's what each one means for your budget:
Access stays wide open, with unlimited users and unlimited agents on both plans.
Spend stays predictable because you get a fixed weekly allowance of DO credits.
Charges apply only when an agent produces something you can use.
Everything leading up to that point is free. Building, testing, and iterating cost nothing, and neither does adding users. If a workflow fails or an app doesn't load, you aren't charged for that either.
What Counts as One DO Credit?
One DO credit covers one unit of finished work in AgentSpot. Here's how the meter reads:
| Action | DO credits |
|---|
| Start a new conversation with an agent | 1 |
| A workflow runs on a user's behalf (each run) | 1 |
| Load an agentic app | 1 |
| Build, test, or iterate on an agent | 0 |
| A workflow fails or an app doesn't load | 0 |
| Add a user | 0 |
| A conversation, workflow, or app stays active beyond a continuous 24 hours | 1 per additional 24-hour period, or portion of one |
How does that last row play out? A conversation left open for 30 straight hours uses two credits, while one open for 50 hours uses three.
How Do You Plan Weekly DO Credits?
You plan DO credits the way you'd plan any recurring budget: estimate a typical week, then compare it with your allowance. Start with one person before you scale to a team.
One Account Executive's Week
Here's a typical week for an account executive:
One weekly pipeline-prep workflow run uses one credit.
Five opens of a deal-review live app use five credits.
Five deal-coach conversations use five credits.
That's 11 DO credits for the week, which fits inside the Free plan's 25 with room to spare. What happens when the whole team joins?
A 40-Person Team on Fleet
The numbers below are illustrative, so swap in your own usage patterns:
Two shared-app loads per person across 40 people use 80 credits.
Two new agent conversations per person use another 80 credits.
Each weekday, 12 scheduled workflows run, using 60 credits.
That adds up to 220 DO credits a week, leaving 30 credits of headroom under Fleet's 250. On the finance side, Fleet costs $1,650 a month billed annually, which comes to $19,800 a year. Adding a 41st person doesn't add a seat fee, though that person's usage still draws from the same 250 credits.
Long-running sessions are the one variable to watch. If someone on your team keeps an app active across a full weekend, plan for the extra credit each additional 24-hour period adds.
Why Don't Weekly DO Credits Roll Over?
Weekly DO credits don't roll over, and that's on purpose. A fresh allowance each week keeps your spend flat, so a quiet month can't turn into a surprise spike later.
Think about what rollover would do to your forecast: unused credits pile up, then get spent all at once near quarter end. Without rollover, the plan cost you approve in January is the plan cost you see in June. Wouldn't your VP of finance rather track one steady line item than a growing balance?
How Do You Track AI Spend Centrally?
Once your weekly number is set, the next step is watching it. You track spend inside AgentSpot, where credit consumption shows up in real time.
Because agents, workflows, and apps run in one centrally managed environment, you can see what's running without waiting for an invoice. IT also gets full visibility and complete audit trails for compliance.
That visibility is the practical answer to shadow AI. When your whole team has access to governed agents, there's far less reason for anyone to paste company data into a consumer chatbot.
If your agents touch revenue, cash, or customer data, controls matter as much as cost. Role-based access, strict data isolation, and full logging come built in, so your spend data sits next to the same controls IT already audits. That's how you scale AI adoption without scaling shadow AI.
Which AgentSpot Plan Fits Your Team?
You can start on Free and move to Fleet once you know your weekly pattern. Here's how the two plans compare:
| Free | Fleet |
|---|
| Price | $0 | $1,650/month, billed annually |
| DO credits | 25 per week | 250 per week |
| Agents | Unlimited | Unlimited |
| Users | Unlimited | Unlimited |
Free works well for a pilot, where one team runs a handful of agents and you get a real read on weekly usage. Fleet suits rollouts where shared apps and scheduled workflows run across a department. Either way, the rule stays the same: build and iterate for free, and pay only when your agents run.
See How DO Credits Fit Your Team
A budget you can forecast is a budget you can say yes to. So map one typical week for your team, then see how it lines up against 25 or 250 DO credits.
See AgentSpot in action and request a demo. Prefer to start on your own? Try it free with 25 DO credits a week, no card needed.
