Seat Allocation Optimizer
Assign plan tiers to developers based on their usage distribution — stop overpaying.
A band is promoted to a pricier tier only when its usage value exceeds the cheaper tier by 1.5× — avoids flip-flopping borderline seats.
saved versus putting all 50 developers on the heavy tier — $4,600.00/mo optimized vs $10,000.00/mo naive ($64,800.00 per year).
| Band | Devs | Tier | Subtotal |
|---|---|---|---|
| Heavy | 5 | Heavy | $1,000.00 |
| Standard | 15 | Heavy | $3,000.00 |
| Light | 30 | Light | $600.00 |
Cost by band
How it works
This optimizer answers a question most teams answer by default rather than by math: which plan tier should each developer actually be on? Set your team size, pick a usage distribution (or keep the power-law preset, which matches what real orgs look like), set the monthly usage value per band and your three tier prices — all editable, because vendor pricing changes — and the tool recommends a tier per band, totals the optimized cost, and shows the savings against the naive everyone-on-the-top-tier baseline.
The core insight is distribution. AI usage inside an engineering org is not uniform: a small fraction of developers runs agents all day and genuinely needs the heavy tier, a middle band uses AI daily but moderately, and a long tail touches the tools a few times a week. Buying one tier for everyone prices the entire org at the margin of its most expensive users. The fix costs nothing in productivity — light users on a light tier lose nothing they were using.
The recommendation logic is visible and deliberately conservative. A band is promoted to a higher tier only when its usage value clearly exceeds the cheaper tier with a 1.5× margin, so borderline developers are not flipped back and forth between tiers every billing cycle. The savings number in the hero is the honest monthly delta between the optimized allocation and the naive baseline; multiply by twelve before taking it to your budget review, because that is the figure finance will care about.
One caveat worth stating plainly: presets are a starting point, and your real distribution is knowable. A month of per-developer usage data beats any model — sort developers by actual spend and the bands separate themselves. That measurement is the part FORG automates: continuous per-developer tracking that turns this one-off exercise into a quarterly routine where seat allocation follows observed usage instead of last year's guess. Everything on this page runs locally in your browser.
Frequently asked questions
Why does putting everyone on the same tier waste money?
Because AI tool usage inside an engineering org is power-law distributed, not uniform. In most teams, roughly the top tenth of developers drives the majority of token volume while the bottom half barely touches the tools weekly. Buying the premium tier for every seat means paying heavy-user prices for light-user behavior — typically the single largest line of avoidable spend in an AI tooling budget, and the easiest to fix because it requires no behavior change.
How does the optimizer decide which tier each usage band should get?
It compares each band's monthly usage value against the tier prices you set. A band whose usage clearly exceeds what the standard tier is worth gets the heavy tier; a band whose usage sits below the standard threshold drops to light. The thresholds use a 1.5× margin so developers near a boundary are not constantly flipped between tiers — churn in seat assignments costs more in admin time than the marginal dollars it saves.
What is the difference between the power-law and uniform presets?
The power-law preset models the distribution observed in practice: about 10% heavy users, 30% standard, 60% light. The uniform preset splits developers evenly across the three bands, which almost never matches reality but is useful as a sensitivity check — if your savings hold under both presets, the recommendation is robust to your uncertainty about who actually uses what. Real telemetry beats both presets; use whichever is closer until you have it.
How do I find out which band each developer actually belongs in?
Measure, do not survey. Self-reported usage is reliably wrong in both directions — enthusiasts overestimate and quiet heavy users underreport. Pull a month of actual usage data per developer from your provider dashboards or a spend-tracking tool, sort by monthly cost, and the bands usually separate visibly. This per-developer visibility is exactly what FORG tracks continuously, which turns seat right-sizing from an annual guess into a monthly routine.
How often should seat allocations be revisited?
Quarterly is the working cadence. Usage patterns shift as projects change — a developer deep in a migration may be a heavy user for one quarter and light the next. Revisit more often and you burn admin time on churn; less often and you carry months of misallocated seats. Pair each review with the same usage data you used here, and treat a band move as routine rather than a judgment about anyone's productivity.
Budgeting AI spend for a team? FORG is $15/dev with hard budget caps and per-seat attribution.
See FORG pricing