Decagon vs Sierra vs Fin
Decagon and Sierra are enterprise-quoted agent platforms; Fin is the one with a published rate, at $0.99 per outcome. Pick Fin for a priced deflection layer on the helpdesk you already run, Decagon for high-volume consumer chat and voice, Sierra for an outcome-billed contact-center program. Sainapse sits somewhere else: inside the service desk your engineers already work in.
The three are not the same purchase.
All three put an AI agent in front of your customers. They differ in who buys them, what they sit on top of, and whether you can price a deployment before you ever talk to sales.
Fin is Intercom's agent, sold to support teams that want automation live this quarter. It attaches to the helpdesk you already run, publishes its rate, and asks for a 50-outcome monthly minimum. It is the only one of the three you can cost out yourself, and the only one with a free trial.
Decagon is an enterprise platform for high-volume consumer support. Its agents run chat and voice with cross-channel memory, and its build surface is Agent Operating Procedures: workflows written in natural language rather than a configuration language. There is no self-serve tier and no published rate; every deployment starts with a demo. Sierra sells the agent as the whole front door: voice, chat, email and WhatsApp in 59 languages, built with an expert agent-development team. It is the largest commitment of the three, billed against outcomes its team defines with you, and it is quoted rather than listed.
What they share matters too. All three are bought by the team that owns the customer's front door, all three are priced against the conversations that arrive there, and all three treat a conversation that never reaches a person as the win.
That is the right definition of success for a contact center. It is not the definition an internal service desk runs on, which is where a three-way comparison stops being enough.
Fin, Decagon and Sierra on the buying criteria
Everything below is public, sourced and dated further down the page. Where a vendor publishes no number, the row says so rather than repeating an estimate as if it were a rate card.
| Fin | Decagon | Sierra | |
|---|---|---|---|
| Published rate | $0.99 per outcome | None published | None published |
| What the unit is | Resolution, procedure handoff or disqualification | Per conversation, or per resolution | A defined outcome, blended with per-conversation traffic |
| Entry commitment | 50 outcomes a month | Quoted per deal, sales-led | Quoted per deal, sales-led |
| Try before buying | 14-day free trial | Demo only | Demo only |
| Helpdesk | Works with the helpdesk you have | Runs alongside your existing helpdesk | The agent platform is the surface |
| Channels | Chat, email, WhatsApp, SMS, voice | Chat and voice, cross-channel memory | Voice, chat, email, WhatsApp, 59 languages |
| Escalations | No outcome, no charge | Charged under per-conversation pricing | In most cases, no charge |
| Build surface | Procedures and knowledge sources | Agent Operating Procedures, in natural language | Agent Studio and the Agent SDK |
All three, and then Sainapse
The three above answer the same question in three commercial shapes. Sainapse answers a different one, which is why most buyers end up evaluating it beside them rather than against them.
| All three | Sainapse | |
|---|---|---|
| Where it runs | Its own agent surface, in front of the helpdesk | Inside the service desk itself, where your engineers already work |
| Who it serves first | The customer writing in | The engineer resolving the ticket, and the customer waiting on them |
| Autonomy model | Deflect what it can, escalate the rest | Shadow, then suggest, then auto-apply; proven per category, reversible |
| Work it covers | Customer conversations | Conversations, plus documents, orders and system writes |
| What it writes | Actions inside connected systems | Records into ServiceNow, SAP or Oracle, evidence attached |
| How it learns | Configured procedures and knowledge articles | Every reviewer correction, folded into the next resolution |
| What cost follows | Outcomes, or conversations handled | The volume and mix of work Sainapse resolves |
Priced facts, and their dates
When each of the three is the better buy
Fin is the better buy when you want an agent answering tickets on your current helpdesk this quarter, and when finance needs a number before signing: it is the only one of the three whose rate you can multiply by your own volume without a discovery call. Its 50-outcome monthly minimum is also a floor almost any team clears, so small volumes are not disqualifying.
Decagon is the better buy when your load is high-volume consumer traffic across chat and voice and you want one vendor owning the whole concierge experience, and when you would rather pay a predictable per-conversation rate than relitigate every month what counted as a resolution. Decagon's own writing makes the same argument against outcome billing. Sierra is the better buy when the agent itself is the customer experience you are purchasing (a branded front door across voice, chat and messaging in dozens of languages), and when you have the budget and the runway for a sales-led program with a dedicated agent-development team beside you. If you want the vendor paid only when a case is resolved, that alignment is Sierra's entire model.
None of the three presents the other kind of work as its territory: the work that is not a conversation at all. Purchase orders arriving as PDFs, emails that have to become service requests, order lines that have to land in SAP as records: a different pipeline, with different failure modes, and the one Sainapse was built around.
Buy an agent for the front door; look at Sainapse for what reaches the desk behind it.
What Sainapse runs in production
Ford, four-plus years live
300+ specialists, 250K+ tickets a year, embedded natively in ServiceNow. 95% copilot accept rate. Sainapse-recommended resolutions applied by engineers without edits. ~100% triage and routing accuracy. Hand-offs per resolved ticket 4 → 1.2, a 70% collapse in resolution effort, alert to close (automated steps excluded).
An enterprise SaaS support operation
400+ specialists, 1.2M+ tickets a year, 12 months live inside the helpdesk. Customer-initiated touchpoints per resolved ticket 4 → 1.2, a 70% collapse in customer effort (agent replies and internal notes excluded). CSAT +35%, concurrent with the Sainapse period (correlation, not a causal claim).
Avery Dennison, 28,377 orders reconstructed
Avery Dennison's order desk: 1,126,728 header fields compared. Zero-touch orders 26% → 71% and header field-edit rate 8.4% → 1.37% over thirteen months, while monthly volume grew ~13×. Human edits per order 2.84 → 0.62 while fields processed grew 17×.
How these numbers are measured
Accept rate is read from the service desk's own accept/reject telemetry, not a survey. Hand-off counts exclude automated steps. Satisfaction movement is published as correlation because that is what it is. The order figures reconstruct every order in the period field by field, and the source document states the method's limits.
Sources and retrieval dates
Common questions
Only Fin publishes a rate: $0.99 per outcome, with a 50-outcome monthly minimum. Decagon quotes per conversation or per resolution and publishes neither number. Sierra bills defined outcomes, blended with per-conversation traffic, also unpublished. So one of the three can be modelled in a spreadsheet before a sales call, and two cannot.
All three sit in front of customer conversations, so they fit a contact center more naturally than an internal service desk. If the work you want automated is engineers resolving tickets inside ServiceNow, that is the shape Sainapse ships: embedded in the desk, resolving and routing there, with hand-offs measured rather than deflection alone.
Their public material describes customer conversations across chat, voice, email and messaging, with actions taken in connected systems during those conversations. Sainapse's document work is a different pipeline: purchase orders and emails read in any layout, matched against your master data, written into SAP, Oracle or ServiceNow as records.
It depends on your resolution rate and on the contract's definition of a resolution. Decagon argues per-conversation billing avoids monthly arguments about what counted. Sierra argues outcome billing aligns the vendor with results. Ask each vendor to price your own volume mix both ways before choosing.
Yes. Sainapse works inside the service desk rather than replacing the front door, so a deflection agent can answer customers while Sainapse handles what reaches an engineer: triage, drafted resolutions, exceptions and system writes. Pilots start on your real tickets with a day-0 baseline you can point to.
Where to go next
- Decagon alternatives
What to weigh when Decagon's enterprise minimum is more than the problem needs
- Zendesk AI alternatives
The same criteria applied to the helpdesk-native AI add-ons
- ServiceNow AI alternatives
For estates where the service desk, not the contact center, is the buyer
- How we compare vendors
The method behind these pages: what gets sourced, dated, and left unclaimed
- Sainapse pricing
How Sainapse is priced against the volume and mix of work it resolves.
- Autonomous resolution
The rungs a category climbs before Sainapse acts alone, and how it rolls back
- Knowledge from resolutions
Where the learning in the comparison table above comes from
- End-to-end system write
What it means to write a record into ServiceNow, SAP or Oracle
- Enterprise IT operations
The Ford deployment in full: hand-offs, accept rate, and how it ramped
- End-to-end ticket automation
Intake to resolution inside the desk your team already runs
- Cross-system customer context
Why an agent that reads one system answers half the question