Sierra vs Decagon vs Intercom Fin: Where the Crossovers Land

At 12,121 monthly support tickets, Sierra and Intercom Fin cost a CFO the same amount. Below that number, Fin is cheaper. Above it, Sierra wins by a widening margin that hits roughly $47K/year by 25K tickets. Decagon's crossover with Fin lands lower, around 10,100 tickets/month. Ada's lands lower still. Same support stack, same deflection assumption, three different break-even points — and the year-1 setup invoices reorder the table all over again.

Most CFOs evaluating support AI agents look at one vendor at a time. Sierra's deck shows Sierra winning. Decagon's deck shows Decagon winning. Intercom's deck shows Fin winning. They're all right for different ticket volumes, and the choice that minimizes year-3 cost is often not the choice that minimizes year-1. Here's the head-to-head, with the exact crossover math.

Try the free calculator — 15 seconds, no signup.

Quick refresher: the two pricing models

We covered the methodology in detail in the Intercom Fin Zendesk seat-replacement math post, so this is the short version. Two pricing models for support AI agents dominate the market in 2026:

• Per-resolution. Intercom Fin charges $0.99 per resolved ticket. Cost scales linearly with ticket volume × deflection rate. No setup fee at the entry tier. Predictable per-unit, unpredictable in total.
• Flat-fee with setup. Sierra charges $6,000/month plus $35,000 in setup. Decagon charges $5,000/month plus $25,000 in setup. Ada charges around $4,500/month with custom enterprise contracts. Cost is fixed at sign-time; volume risk lives entirely on the buyer.

The math for whether per-resolution or flat-fee is cheaper turns on a single variable: your monthly ticket volume. The crossover point is where monthly tickets × deflection rate × $0.99 × 12 = annual flat-fee cost. Solve for tickets and you get the unlock threshold for every flat-fee competitor.

For the rest of this post we'll assume a 50% deflection rate, which is the catalog mid-point for Fin against Zendesk-class queues. Vendor case studies sometimes show 60–65%, and conservative year-one deployments often land at 35–45%. We'll show how the crossover shifts at each end of that range in the worked example.

The three-way crossover, steady state

At 50% deflection, every resolved ticket costs Fin $0.99. Annualized, every ticket of monthly volume costs Fin $0.99 × 0.50 × 12 = $5.94/year. That number is the unit of conversion between flat-fee monthly invoices and Fin's variable bill.

Plug in each flat-fee agent's steady-state annual cost and divide:

Sierra steady-state crossover  = $72,000 / $5.94  = 12,121 tickets/month
Decagon steady-state crossover = $60,000 / $5.94  = 10,101 tickets/month
Ada steady-state crossover     = $54,000 / $5.94  =  9,091 tickets/month

Below 9,091 monthly tickets, Fin is the cheapest of the four at steady state. Between 9,091 and 10,101, Ada wins. Between 10,101 and 12,121, Decagon wins. Above 12,121, Sierra is the cheapest of the four — but only after year 1, when the setup fee is in the rear-view mirror.

Year 1 is a different table. Setup adds $25K (Decagon), $35K (Sierra), or roughly zero (Ada's enterprise contracts vary; for this post we'll model Ada as steady-state-only to keep the comparison conservative). Recomputing:

Sierra year-1 crossover  = $107,000 / $5.94 = 18,013 tickets/month
Decagon year-1 crossover = $ 85,000 / $5.94 = 14,310 tickets/month
Ada year-1 crossover     = $ 54,000 / $5.94 =  9,091 tickets/month

Year 1, the order swaps. Ada wins between 9,091 and 14,310. Decagon wins between 14,310 and 18,013. Sierra only wins above 18,013, even though Sierra is the steady-state winner above 12,121. That gap — 12,121 to 18,013 — is the "setup hump" where Sierra is steady-state-cheapest but year-1-most-expensive. It's where 24-month commitments matter, because year 2 onwards is when Sierra's flat fee dominates.

The contrarian read: Decagon's lower year-1 setup makes it the strongest middle-market option. It crosses Fin's per-resolution price at 14,310 tickets year 1, beats Ada at 10,101 steady-state, and never carries Sierra's 35K setup obligation. The vendor pricing pages don't frame it this way because none of them have a reason to.

A worked example: 10,000-employee SaaS with 25K monthly tickets

Take a mid-enterprise B2B SaaS company. 10,000 employees, US-based, growth-stage. Support team running on Intercom. Monthly support volume is 25,000 tickets, growing slowly (10% YoY). Deflection assumption: 40% — the conservative-mature-deployment number, not the vendor-case-study number.

Fin year-1 cost     = 25,000 × 0.40 × $0.99 × 12 = $118,800
Fin year-2 cost     = 27,500 × 0.40 × $0.99 × 12 = $130,680
Fin year-3 cost     = 30,250 × 0.40 × $0.99 × 12 = $143,748

Sierra year-1 cost  = $107,000 (incl. $35K setup)
Sierra year-2 cost  = $ 72,000
Sierra year-3 cost  = $ 72,000

Decagon year-1 cost = $ 85,000 (incl. $25K setup)
Decagon year-2 cost = $ 60,000
Decagon year-3 cost = $ 60,000

Ada year-1 cost     = $ 54,000
Ada year-2 cost     = $ 54,000
Ada year-3 cost     = $ 54,000

Three-year cost stack:

Fin total     = $393,228
Sierra total  = $251,000
Decagon total = $205,000
Ada total     = $162,000

Ada wins the three-year cost contest by a margin of $43K over Decagon, $89K over Sierra, and $231K over Fin. At this volume and this deflection assumption, the per-resolution model is by far the most expensive option, despite Fin's marketing positioning as the budget-friendly entry point.

But that table is incomplete because it ignores deflection quality. Sierra at 55% deflection deflects more tickets than Fin at 40%. If Sierra's catalog rate against Intercom (55% per prisma/seed.ts) holds in this customer's deployment, the labor counterfactual — humans not hired because the agent absorbed the volume — favors Sierra. We covered the labor counterfactual framing in the Intercom Fin Zendesk seat-replacement math post, and it's the larger of the two savings terms.

Said differently: the three-year cost stack above is the infrastructure cost of each agent. The headline savings come from avoided hires, which scale with deflection percentage, not with the raw agent fee. A CFO doing this evaluation should run the agent fee table side-by-side with the avoided-hire table and let the larger number drive the decision.

This is also why our broader shortlist of AI agents that replace SaaS seats flags Sierra and Decagon as "high-floor, high-ceiling" agents while flagging Fin as "low-floor, low-ceiling." Per-resolution pricing is forgiving on the way in and unforgiving at scale; flat-fee pricing is the inverse.

Three scenarios where flat-fee wins

The Fin post outlined this framing for one volume bucket. With Sierra, Decagon, and Ada now in the comparison, the picture sharpens.

Scenario 1: High volume + mature deflection workflows. Any enterprise support org pushing more than 18,000 tickets/month — which describes most of the 5K–50K employee target we write for — clears the year-1 Sierra crossover. Above 25K, Sierra steady-state cost ($72K) is roughly half the Fin year-1 invoice. Mature support orgs with multi-year deflection tuning, voice + chat + email + in-app channels, and a stable ICP get the most out of Sierra's flat fee because deflection rates trend toward the upper end of the catalog band (55–65%) and ticket volume keeps climbing. The marginal Fin ticket is $0.99; the marginal Sierra ticket is $0.

Scenario 2: Voice-heavy support. Fin is text-first. If 40% or more of your tickets come through voice channels — common in B2C SaaS, fintech consumer support, and managed-services businesses — Fin can't deflect them. Sierra is voice-native; Ada is multi-channel with strong voice. At a 30%-text / 40%-voice / 30%-chat mix, Fin's effective deflection on total volume drops to maybe 20–25%, while Sierra holds 45–55%. The per-resolution math suddenly looks worse than the flat-fee math even at modest volumes, because Fin's denominator shrinks while Sierra's stays the same.

Scenario 3: Predictable, capped budgets. This one is procurement-driven, not unit-economics-driven. Finance teams budgeting a year out prefer fixed line items they can defend in a board deck. A $5,000/month Decagon invoice is the same number for 36 straight months; a $0.99/resolution Fin invoice can swing 30%+ in either direction with a single product launch or a single customer-base expansion. Some CFOs will pay 10–15% more in total cost for the budget stability. That preference shows up most strongly at the Enterprise tier, where annual budgets get committed three quarters in advance and surprise variability is operationally expensive even if it's mathematically free.

The honest admission: scenarios 1 and 2 are mathematical. Scenario 3 is psychological — but psychological constraints drive real procurement decisions, and any CFO modeling these agents should treat budget predictability as a feature even when the cheapest-on-paper choice is the variable one.

A voice-channel footnote

Voice deserves more than a parenthetical. The catalog tracks Sierra at 55% compression on Intercom and 60% on Zendesk; Ada at 50% on Intercom and 60% on Zendesk; Fin at 60% on Intercom (its native platform) and 50% on Zendesk. These rates assume text-or-chat ticket mixes, which is the dominant pattern for B2B SaaS.

For voice-heavy or omnichannel support, the rates effectively bifurcate. Text/chat tickets follow the catalog rates. Voice tickets get deflected by zero for Fin (no voice channel support at the entry tier), and by 35–50% for Sierra and Ada with their voice deployments. If voice is 40% of your inbound, a Fin deployment that "deflects 50% of tickets" is really deflecting 50% of 60% of tickets = 30% of total volume. A Sierra deployment deflecting 50% across the full channel mix is deflecting 50%. That's a 67% relative advantage for Sierra on the same nominal deflection rate, before you even compare invoices.

The procurement implication: don't pick a per-resolution agent for a voice-heavy queue. The math fundamentally doesn't work, because you're paying per-resolution on the text subset while the voice subset still consumes your human capacity. A flat-fee multi-channel agent — Sierra, Ada, or Decagon's voice tier — turns the voice queue from a cost center into another deflection target.

The 4-quadrant framework

Putting this on a 2x2 makes the procurement decision obvious:

                      Low ticket volume     High ticket volume
                      (< 10K/month)         (> 18K/month)

Text-first queue      → Fin                  → Sierra or Decagon
Voice or omnichannel  → Ada (steady-state)   → Sierra (voice-native)
                        or wait + scale

Three of those four quadrants resolve to a clear winner on year-1 math. The fourth — high-volume text-first — is the messy middle where Sierra and Decagon both compete. Decagon wins year 1 on setup-fee math; Sierra wins steady-state on flat-fee math. The right answer depends on contract length: signing for 12 months favors Decagon, signing for 36 months favors Sierra. We've watched CFOs pick Decagon for year 1, run a 12-month proof-of-value, and switch to Sierra at renewal once the deflection rate is established. That's a defensible play.

For the high-volume voice-heavy quadrant, Sierra is usually the only viable answer at the flat-fee tier. Decagon's voice product is newer and less mature; Ada's voice deflection is strong but its catalog pricing assumes a longer ramp than Sierra. If voice is more than half of your inbound and you're doing more than 15K tickets/month, Sierra is the default pick.

How this fits in the broader picture

Per-resolution and flat-fee are two of the three pricing axes worth tracking for AI agents in 2026. The third — per-employee — shows up on a different class of tools (HRIS, ITSM, productivity) and we'll cover the crossover dynamics in the dimensional SaaS compression post.

What unifies all three is the discipline of treating pricing model as a strategic question, not a procurement detail. A per-seat tool, a per-resolution agent, and a per-employee platform all do similar work for the CFO P&L, but they all have wildly different scaling properties. The compression playbook that works against a per-seat tool (renegotiate at renewal, drop unused licenses) doesn't transfer to per-resolution agents — there, the playbook is volume forecasting and deflection-rate negotiation. We covered the per-seat compression math in the calculator deep-dive, and the per-resolution math in the Fin post. This post sits between them, showing where the two pricing models compete head-to-head and where the choice falls.

The lesson for CFOs running a 12-month evaluation: don't pick a support agent in isolation. Pick the category (per-resolution vs flat-fee), then pick the vendor inside the category. The category choice is driven by ticket volume and channel mix. The vendor choice inside the category is driven by setup-fee tolerance, deflection-rate confidence, and contract length. Get the category wrong and the vendor choice is mostly aesthetic.

CFO bottom line

For the 5K–50K employee enterprise we write for, here's the practical compression of this whole post:

• Under 10,000 monthly tickets: Fin or Ada. Fin if text-first and growing; Ada if mixed channels and stable.
• 10,000 to 18,000 monthly tickets, year 1: Decagon. Lower setup hump than Sierra, faster crossover than Ada.
• 10,000 to 18,000 monthly tickets, steady-state (year 2+): Sierra. Flat $72K beats the volume-scaled alternatives once setup is amortized.
• Above 18,000 monthly tickets: Sierra year 1 and steady-state. The setup investment pays back in the first quarter.
• Voice or omnichannel at any volume: Sierra (voice-native) or Ada (multi-channel). Fin and Decagon's voice tiers aren't mature enough to anchor a high-voice deployment in 2026.

The 4-quadrant framework above is the version you should put in front of your support leader and your CFO. The crossover numbers are computed from the catalog rates and the published vendor pricing. They'll move slightly as vendors adjust pricing — Decagon is rumored to be testing a hybrid per-resolution tier, and Ada's enterprise pricing has been climbing — but the relative ordering is stable.

One last note on data quality. The numbers in this post come from prisma/seed.ts (catalog mid-points based on vendor pricing pages and customer-disclosed deals) plus vendor pricing pages directly. We don't make up deflection rates; we use the catalog mid-points and tell you when to apply conservative haircuts. If your support team is post-deployment, run your actual deflection rate against your actual ticket volume and rebuild the crossover for your specific case. The formula is in the Fin post and the math in this post is just the multi-vendor extension of it.

Try the free calculator — 15 seconds, no signup. Plug in your ticket volume, deflection assumption, and channel mix; the calculator runs Sierra, Decagon, Fin, and Ada side-by-side with year-1 setup folded in. Most CFOs find the crossover quadrant within the first minute, and the right vendor inside that quadrant within the next two.