Know what AI costs, down to each user.
Every model your staff use is included in the platform, so there are no separate licenses to buy for each person. Cost Control attributes the spend behind every prompt to the user and the model, so you can budget, charge back, and spot runaway usage early.
- Spend, tokens, prompts, and threads for each user
- Average cost per thread
- Filters by source, model, RAG, and web search
- Any date range, exported to CSV
- A tokens-per-minute quota for each model
- One view across every model and provider
AI costs sprawl when every model is a separate purchase.
Buying a license for each model and each person gets expensive quickly, and it leaves you paying for seats nobody uses. MultiModel puts every model behind one platform and shows you exactly where the spend goes.
No model licenses to buy
ChatGPT, Claude, Gemini, Copilot, Grok, and Perplexity are included in your plan, with no separate licenses to buy, renew, or manage for each.
AI model choiceSpend attributed to each user
Every prompt's cost is tied to the person who sent it and the model that answered, so there is no guesswork about who is driving spend.
Cost next to adoption
See spend beside usage for the same people and period, and show your board what the AI program costs against the work it supports.
Measurement & reportingAttribute AI spend to users and the models they use.
One table shows each user's spend, with the tokens, prompts, and threads behind it and the average cost per thread. Sort by any column to find the heaviest users, then filter to see which models and features the spend comes from.
- Spend, tokens, prompts, threads, and $ per thread
- Filter by source, model, RAG, and web search
- Download CSV for budgeting and chargeback
Attribute AI spend to users and the models they use.
Match spend to the people getting value from it.
The adoption view breaks usage down by person: the threads they started, the prompts they sent, and the tokens they used. Put it next to Cost Control to see whether your biggest spenders are also your most productive users, and which seats aren't being used.
- Threads, prompts, and token usage for each user
- Spot power users and seats that sit idle
- Export to CSV alongside your spend data
Track AI adoption across your organization.
Set a usage quota for each model.
Give every model its own tokens-per-minute limit, anywhere from 0 to 1M. Allow plenty of capacity on the everyday models your staff rely on, keep tighter limits on costly or External models, and adjust any quota as usage changes.
- A tokens-per-minute quota for each model
- Tighter limits for costly or External models
- Raise or lower a quota at any time
Set how many tokens per minute each model can use across your organization.
Claude Sonnet
Internal
Gemini
Internal
Copilot Chat
Internal
Perplexity
External
Choose the right model for each job.
Send one prompt to several models and switch between their answers. Your staff see which model handles a task well, so routine, high-volume work goes to fast, inexpensive models and deep-thinking models are saved for the problems that need them. The result is the right balance of speed, reasoning, and cost for every task.
- One prompt, several models, one screen
- Compare answers before you pick a model
- Balance speed, reasoning, and cost task by task
Multi
Internal
Use prompt ⌘ ↵
Summarize my notes from yesterday’s meeting with Bayside Logistics and generate a list of follow-up items with owners and due dates.
bayside-meeting-notes.docx AI can make mistakes. Verify important information with alternative sources.Key points
- Wants to refinance a $4.2M equipment loan and add a $750K revolving line for seasonal swings.
- Decision drivers: speed to close and online treasury access.
- Also talking to one competing lender, so timing matters.
Action items
| Task | Owner | Due |
|---|---|---|
| Confirm underwriting timeline | Jordan Ellis | Oct 2 |
| Send term sheet | Jordan Ellis | Oct 3 |
| Send 3 yrs financials + equipment list | Bayside CFO | Oct 8 |
| Treasury demo with AP team | Priya Shah | Week of Oct 6 |
I can also draft the recap email or a CRM note from this.
Meeting summary
Bayside Logistics is looking to refinance a $4.2M equipment loan and open a $750K revolving line of credit to cover seasonal cash flow. Their CFO flagged turnaround time and online treasury access as the deciding factors, and they’re comparing us with one other lender.
Follow-up items
- Send the term sheet for the refinance and credit line — Jordan Ellis, Fri Oct 3
- Request three years of financial statements and the equipment list — Bayside CFO, Wed Oct 8
- Book a treasury management demo with their AP team — Priya Shah, next week
- Confirm underwriting timeline with credit — Jordan Ellis, Thu Oct 2
Want me to turn this into a follow-up email to the client?
Here’s a summary of your meeting with Bayside Logistics and the next steps.
Overview
Bayside is weighing us against another lender to refinance its $4.2M equipment loan and open a $750K line of credit. Winning will come down to how fast we can close and how good our online treasury tools are.
Next steps for our team
- Jordan: check the underwriting timeline with credit (Thu)
- Jordan: send the term sheet (Fri)
- Priya: set up a treasury demo for their AP team (next week)
Waiting on Bayside
- Three years of financial statements and the equipment list (Oct 8)
Tip: since speed is a deciding factor, consider sharing the underwriting timeline with the term sheet.
Levers you control.
Cost Control shows where the spend goes. Model quotas and these settings let you act on it without slowing your staff down.
The right model for the job
Point routine, high-volume work at fast models, and save deep-thinking models for the hardest problems.
Recommended AI modelsModels by user and group
Decide which groups get which models, so each team has what its work needs and nothing more.
AI guardrailsSize costs before you roll out
If you run models in your own Azure tenant, the Azure price estimate helps you size usage costs up front.
Azure price estimateAI cost control: FAQ
Straight answers for CIOs, CISOs, and compliance officers evaluating AI.
Talk to usWhat does Cost Control show?
AI spend attributed to each user, with the tokens, prompts, and threads behind it and the average cost per thread. You can filter by date range, source, model, RAG, and web search, and export any view to CSV.
Do we need to buy licenses for ChatGPT, Claude, or Copilot as well?
No. Access to every model is included in the platform, so you don't buy or renew a separate license for each model and each person. Your price is based on your plan and the number of seats. See pricing and AI model choice.
Can we charge AI costs back to departments?
Yes. Spend is attributed to each user, so you can download the CSV and roll it up by department or cost center for budgeting and chargeback.
Can we limit how much each model is used?
Yes. Each model has its own tokens-per-minute quota, which you can set anywhere from 0 to 1M and adjust at any time. You might allow more capacity on the everyday models your staff rely on and keep tighter limits on costly or External models.
How do we keep model costs down?
Give each group the models its work needs, and point routine, high-volume tasks at fast models while saving deep-thinking models for the hardest problems. Our guide to recommended AI models covers which model suits which job, and Cost Control shows the effect in real usage data.
How do we estimate costs before we roll out?
Talk to us for a quote based on your headcount. If you plan to run models in your own Azure tenant, the Azure price estimate helps you size usage costs.
See where your AI spend would go.
Tell us which teams will use AI first. We'll show you the Cost Control view, and how spend rolls up by user and model.