// Team AI Usage Calculator

The Team AI Usage Calculator.

Estimate monthly AI usage costs across your team. Enter actual employee counts, choose specific models per workflow (mix and match across providers), compare API direct vs seat-based subscriptions vs hybrid routing.

Workflows Modeled 10
Models Available 44 · across providers
Last update May 2026 LIVE · UPDATED WEEKLY
1

Your team.

Enter actual numbers — the calculator scales from 5-person startups to 100K+ employee enterprises.

// Power users calculated at High intensity (2×). Remainder use the intensity selected above.
2

What workflows will your team run?

Toggle workflows on/off, pick the specific model you'd use (mix providers freely across workflows), and adjust expected runs per user per month. Defaults reflect typical patterns; edit any to match your scenario.

💬

Chat + Research

General-purpose questions, brainstorming, research synthesis.
/ user / mo
8K input / 1K output tokens per run

Meeting Summary

Transcribe → summarize → action items from meeting recordings or notes.
/ user / mo
25K input / 1K output tokens per run
📄

Long Document Summary

Process and summarize long PDFs, contracts, reports, research papers.
/ user / mo
50K input / 2K output tokens per run
@

Email Follow-ups

Draft, polish, or auto-respond to routine emails.
/ user / mo
0K input / 300 output tokens per run

Support Triage & Follow-ups

Classify support tickets, draft responses, suggest resolutions.
/ user / mo
2K input / 500 output tokens per run

Internal Knowledge Search

Retrieval-augmented questions over internal docs, wikis, files.
/ user / mo
5K input / 800 output tokens per run

Agent Workflows (with tools)

Multi-step automations: research → analyze → act via tool use.
/ user / mo
20K input / 3K output tokens per run

Coding

Code completion, refactoring, code review, debugging assistance.
/ user / mo
12K input / 2K output tokens per run

Image Generation

Generate marketing images, mockups, product visuals.
images / user / mo
Per-image pricing applied

Video Generation

Generate short marketing or product videos (~6 seconds each).
videos / user / mo
Per-second pricing × 6s default duration
3

How will you buy AI?

API direct, seat-based subscriptions, or hybrid. The math differs significantly — toggle to compare.

4

Your monthly AI cost estimate.

Recalculates live as you adjust any input above. Low / Expected / High scenarios cover realistic usage variance.

Monthly Cost · Three Scenarios

Plan for the range — usage varies in real deployments.

Low Estimate
$—
per month
Annualized: $—
Expected
$—
per month
Annualized: $—
High Estimate
$—
per month
Annualized: $—
Per Employee · Average

Expected monthly cost per seat

$—
Total expected ÷ all employees
Per Power User

What your heaviest users actually cost

$—
Power-user share ÷ power-user count
Cost Breakdown by Workflow
Routing recommendation

Adjust your team config and workflow mix above to see a routing recommendation tailored to your usage profile.

Estimates are directional planning figures, not procurement quotes. Real-world usage varies based on workflow mix, model routing, and adoption velocity. For procurement-ready estimates and managed deployment, book a scoping call →.
// Custom Estimate · AI Transformation Audit

Want a procurement-ready estimate?

This calculator gives directional planning numbers. For the version we actually use with clients — a full workflow audit, real cost modeling against your team's specific systems and adoption profile, plus ROI projections — book a scoping call with us.

  • Workflow-by-workflow audit of where AI fits in your current operations
  • Cost modeling with your actual data volumes and team structure
  • ROI projections accounting for productivity gains, not just inference cost
  • Implementation roadmap with timing, dependencies, and risk flags

Most engagements scope within 2 weeks · paid pilot or full project

Frequently asked questions.

How much does AI cost per employee?

The 2026 range for serious AI usage runs $20–$200 per employee per month, depending on intensity and workflow mix. A team where everyone does light chat + email assistance averages closer to $20–$40 per seat. A team doing heavy coding, agent workflows, and document analysis can hit $150–$400 per seat. The calculator above gives a Per Employee (Average) figure scoped to your team's specific profile — that's the right number to bring into budget conversations.

Can I mix providers across workflows?

Yes — that's the whole point of the workflow-level model selection. You can use Claude Opus 4.7 for coding, GPT-5.5 for chat, Gemini 3.1 Pro for long documents, and Veo 3 for video, all in a single estimate. This reflects how most production AI deployments actually work — different workflows get routed to the model that fits best, rather than locking your entire team to one provider.

API direct vs seat-based subscriptions — which is cheaper?

It depends on team size and workflow mix. For moderate, consistent usage across most employees, seat subscriptions usually win because you pay one fixed rate per seat regardless of usage within plan caps. For high-volume agent workflows, coding, or specialized power-user scenarios, API direct is usually cheaper because you only pay for what you actually consume. Most teams end up choosing one path based on their primary usage pattern — toggle between the two modes in the calculator to see which fits your team's profile.

What's the difference between Low, Average, and High intensity?

The intensity selector applies a global multiplier to the run counts in your workflow mix. Low (0.5×) reflects light adoption — only some employees engage with AI, workflows run less than baseline. Average (1×) is the baseline assumption. High (2×) reflects heavy adoption — employees use AI throughout their workday, workflows run at 2× baseline rates. Power Users (if specified) are always calculated at High regardless of the global intensity.

What's the difference between Low, Expected, and High estimates in the output?

The intensity selector controls your team's typical usage profile. The Low/Expected/High output then adds an uncertainty band on top — because even at "Average" intensity, real deployments vary. Low estimate is ~60% of Expected (slow adoption ramp). Expected is your configured scenario. High is ~160% of Expected (faster adoption than planned). Budget at Expected; reserve High to cover overshoot.

How do you handle Power Users in the calculation?

If you enter a number in the Power Users field, those employees are calculated at High intensity (2× run counts) while the rest of the team uses your selected global intensity. If you leave Power Users blank, all employees use the same intensity. The "Per Power User" output card shows what your heaviest users actually cost — useful for deciding whether to upgrade them to API access or Max-tier subscriptions specifically.

What's the best AI plan for a team of 50?

For most 50-person teams, seat subscriptions at $25–30 per seat (Claude for Work, ChatGPT Team, Gemini for Workspace) provide predictable budgeting with usage caps that fit standard workloads — running ~$1,250–$1,500/month total. API direct often wins when your workflow mix is heavy on agent automation, coding, or document processing, where token volume per user is high. Run the calculator with your actual workflow mix to see which path nets out cheaper for your specific scenario.

Why do the estimates change so much when I switch models per workflow?

Frontier-tier model pricing varies dramatically — Claude Opus 4.7 lists at $15/$75 per million tokens (input/output), GPT-5.5 at $5/$15, Gemini 3.1 Pro at $3/$12, DeepSeek V4 Pro at $0.30/$1.20. For a workflow doing 12K input and 2.5K output tokens, that's a 40-50× cost spread between the most expensive and cheapest options at the same capability tier. Pick the model that matches the task — frontier reasoning is wasted on routine email triage; lightweight models will struggle on agent workflows.

What's not included in this estimate?

The calculator estimates raw AI inference costs only. It doesn't include: AI implementation and integration services (engineering time to wire up workflows), fine-tuning or training costs, vector database and infrastructure costs, prompt engineering or evals work, or the productivity gains from AI deployment. For organizations standing up serious AI operations, expect implementation and ongoing services to be 1–3× the raw inference cost — at least in year one. Book a scoping call if you need numbers that account for full deployment cost.

Spot incorrect pricing or workflow defaults?

We update pricing and workflow profiles weekly. Tell us where the math is off — we'll review within 7 days.