By the Onsites AI team · Last updated · 5-minute read
Per-resolution pricing is the support industry's cleverest meter: instead of charging per seat or per month, it charges every time a conversation ends successfully — most visibly when your AI assistant closes one, at rates around $0.99 per resolution. The pitch is seductive ("pay only when work gets done"), and at low volume it can even be fair. The failure arrives with two shapes of month. This guide works the meter honestly: how it behaves on a real volume curve, where it hides inside modern pricing pages, what finance sees in November that nobody modeled in March, and the alternative shapes — flat seats, prepaid credits — that bill inputs instead of outcomes. The core discipline this article teaches, wherever you land: model the busiest month first.
Start fairly: the model's appeal is real. It sounds like software finally sharing risk — no seats idle in quiet weeks, no charge for the tool just existing. For a small desk with 200 conversations a month and light AI, a resolution meter can genuinely cost less than a seat bill. The problem is structural: resolutions are not a cost the desk controls — they are a function of the business succeeding. Better marketing, a viral month, the seasonal peak: each lands as more conversations, and a resolution meter converts every one of them into a line item at the meter's rate. Worse, it double-prices success twice: volume rises and AI adoption — the deflection everyone is told to grow — raises the AI-resolved share. The meter is calibrated so the desk pays more precisely when it is winning more. No other department budgets this way: warehouses do not pay per pallet shipped, and finance does not pay per invoice issued.
Take a desk averaging 3,000 conversations monthly and giving AI a 20% share — a modest, realistic start. Shape one: the average month. 600 AI resolutions at ≈$0.99 ≈ $594/mo — noticeable, budgetable, survivable; the meter's pitch works at this distance. Shape two: the peak month. Volume climbs 40% (retail November; freight Q4; the trade-fair spring) and the team — confident now — has raised AI's share to 35%: 4,200 conversations × 35% = 1,470 resolutions ≈ $1,455. The line more than doubled in the month margins were already thinnest from discounts and overtime. The annual view is where the audit lands: a desk with a spiky curve pays the meter twice — the base line all year, then the spike repriced at the meter's full rate. And the peak is the one month finance examines. Model it first: take your average, apply your real seasonal multiple, apply your AI target share, multiply by the published rate — the number that emerges is the meter's honest annual cost.
Set the meter inside a budget review and its behavior becomes vivid. The support lead presents January: AI resolutions at 1,470, the resolution line ≈$1,455 — up 145% year over year. Finance asks the natural question: "What did we get for the increase?" The honest answer — "the holiday quarter succeeded, and the meter shares in it" — satisfies no one, because every other line in the review (ad spend, overtime, inventory) was a chosen spend against a forecast, while this one repriced itself with the volume. Then the second question: "What does next year look like?" — and the only honest forecast is the meter's rate times an unbudgetable guess. A CFO who cannot pre-approve a cost that scales with revenue treats it, correctly, as a variable expense at consumer prices. The flat meter answers both questions in one line: seats (a known headcount) and credits (a prepaid balance at published cents). The peak still costs something — prepaid credits spent — but the number was set in March, at the pack price, by the desk. That control gap, more than any feature, is what the audit surfaces.
Modern pricing pages rarely bill "per resolution" naked; the meter wears clothes. AI add-on packs: "generous" resolution quantities bundled per month — exhausted mid-peak, they top up at worse rates; unused mid-slump, they vanish (subscribed meters do not roll over). The blended seat: plans including some resolutions per seat, then metering beyond — the effective rate hides in the blend. Bot steps and automation meters: per-action billing on workflow engines is the same meter with a different name. The included-tier decoy: "first 50 resolutions free" prices the 51st at a marginal rate nobody computed against volume. The audit that catches every costume: take last year's twelve monthly conversation counts, apply your AI share target, apply the plan's published per-unit rates including pack structures — and total what the year would have cost. Vendors' calculators default to averages; build the spreadsheet honestly or a finance analyst will build it for you, in November.
The flat meter's insight is that you cannot control outcomes but you can control inputs — so price the inputs, publish the rate, and let the outcome meter vanish. Flat seats: $15/seat/mo beyond three free on the Onsites desk — a line that moves with headcount (a decision) and never with volume (a fact). Prepaid AI credits: the same draft, suggested reply, translation or summary that resolution meters would bill at outcome rates bills at published credit prices instead — 3 to 6 credits, roughly a cent each, packs ($10/1,100, $20/2,400, $50/6,500) that never expire. The busy month costs more in credits but at cent-rates, not dollar-rates, and the quiet month refunds nothing because nothing was prepaid that expires. The 10-person worked comparison at 4,000 conversations: ≈$255/mo flat versus ≈$3,100/mo on seat-plus-resolution stacks (full derivation) — same workload, two meters, one order of magnitude apart in the peak month.
The honest column exists here too. Tiny desks, low AI share: 200 conversations, 10% AI → 20 resolutions ≈ $20/mo — cheaper than any seat price; the meter is arguably correct at this scale. Spiky-lean teams wanting elasticity: a desk that would rather pay $0 in a dead month than carry idle seats has a real argument, and resolution meters honor it. Well-audited adoption: finance teams that model the year's curve (as above) and cap AI share can hold the meter within bounds. The case against is about curves and control: any desk with seasonal peaks, headcount growth, or an AI-adoption goal — most desks — hands the meter both hands and watches it bill success twice. If your curve is flat, your AI share modest and your volume tiny, price both shapes and choose; otherwise, prefer the meter whose worst-month line you can compute in March: flat seats, published cents.
What is per-resolution pricing?
A meter that charges each time a conversation is successfully closed — most commonly by the AI assistant, at rates around $0.99 per resolution. It sounds like paying only for outcomes, but outcomes are driven by your business success, not by choices the desk makes.
Why should we model our busiest month first?
The average month hides the meter's real cost. A 3,000-conversation desk at 20% AI share pays about $594/mo — but the seasonal peak (40% more volume, 35% AI share) bills ~$1,455, in the very month margins are thinnest. The annual audit uses the peak, not the mean.
Where does the resolution meter hide on pricing pages?
As AI add-on packs that vanish unused and top up expensively, as resolutions bundled into per-seat tiers, as per-step or per-automation bot meters, and as "first N free" decoys. The audit: last year's real monthly volumes × your AI share × published rates, including pack structures.
What is the alternative to per-resolution billing?
Billing inputs instead of outcomes: flat seats ($15/seat/mo beyond three free on Onsites) plus prepaid AI credits at about a cent each that never expire. Volume then rides only the credits line — at cent rates — and quiet months cost credits, not subscriptions.
When is per-resolution pricing actually fair?
Small desks with low volume and AI share — e.g. 20 resolutions/month ≈ $20 — where the meter undercuts any seat price, and teams that genuinely prefer $0 in dead months. Past that, spiky curves, growth and rising AI adoption hand the meter both hands.