Compare
Decagon vs Fin
A plain-English comparison to help you choose between them.
A platform purchase against a layer you switch on. Decagon is bought as its own system, enterprise agents on chat and voice with testing and monitoring tooling, quote-only and scoped through sales; Fin layers over the helpdesk you already run, grounds in your help content, and prices by outcome, with a published average resolution rate of 76 per cent that should still be tested on your own volume. One caution from the record: Salesforce has agreed to acquire Fin's company, with Fin expected to fold into its Agentforce line. Pick Decagon when support AI is an infrastructure decision; pick Fin when layering outcome-priced resolution over the existing stack is the faster path.
Both tools chosen. Compare is enabled.
Side by side
- Summary
Decagon builds enterprise AI agents for customer support, described on its own site as the AI concierge for every customer: agents that hold real conversations across chat and voice, resolve what they can end to end, and hand the rest to humans with context. It is one of the category's heavily funded independents, self-reporting a large Series D in early 2026.
The commercial shape is strictly enterprise: no self-serve signup, no published pricing, a sales conversation as the only door. Its case studies carry resolution and deflection figures for named customers; those are the vendor's own numbers, and evaluations should treat them as claims to test in a trial rather than benchmarks to plan on.
MoreLess
It belongs on the shortlist for large support operations weighing the new AI-native agent platforms against the AI layers of the incumbent helpdesks.
- Best for
- Enterprise support agents across chat and voice
- End-to-end resolution with human handoff and context
- Large support operations adopting AI-native platforms
- Deployments scoped and priced through a sales process
- Teams comparing agent-native entrants against helpdesk AI layers
- Cost
- Enterprise
- Ease
- Openness
- Hosted service
- Data
- It works inside your support stack and customer conversations, so data-handling, retention and sub-processor terms belong in the contract negotiation, not a settings page after rollout.
- Summary
Fin, formerly Intercom, is an autonomous support agent that resolves customer conversations end to end: grounded in your help content, working across chat and email, and priced by outcome rather than seat, so you pay per resolution it actually completes. It layers over existing helpdesks rather than demanding a migration.
The operating loop is the product: simulate answer quality before customers see it, deploy on routine volume, escalate the rest to humans with full context attached, and mine unresolved questions for the content gaps behind them.
MoreLess
Fin publishes an average resolution rate of 76 per cent across its customers; definitions of resolution vary across the market, so the honest baseline for staffing and economics is a trial on your own volume rather than any vendor's headline figure.
- Best for
- Autonomous end-to-end resolution of routine conversations
- Outcome pricing: pay per resolution, not per seat
- Layering over an existing helpdesk without migration
- Escalation to humans with full context attached
- Mining unresolved cases for missing help content
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Fin answers from your help content and past conversations under Fin's commercial terms. One strategic note: Salesforce has agreed to acquire the company, with Fin expected to fold into its Agentforce line, which is worth weighing in any long-term platform decision.
Pricing
- Decagon
- No published price
- Fin
Custom·Custom
Prices as of August 2026.
- Fin with your current helpdesk
- Price on application
- Fin with the Intercom helpdesk
- Price on application
By area
Where each one pulls ahead, area by area.
| Area | Decagon | Fin |
|---|---|---|
| By job | ||
| Customer support | Decagon makes the evaluation about trust boundaries rather than features: what the agent may resolve alone, when a human enters, how quality is watched, piloted first on a scoped slice of volume | Fin lets a budget-sensitive team model real volume before committing, since the mechanics of what gets charged are visible up front |
| By task | ||
| Customer support & chatbots | Decagon designs the escalation paths so a handover preserves the context it was carrying rather than starting the customer again from the top | Fin asks to be judged on resolution quality rather than deflection volume, with published rates that lead the mainstream field |
Common questions
What does outcome pricing change about the comparison?
It moves the risk. Fin charges per resolved conversation, so a failed answer costs nothing and success scales the bill with value delivered. Decagon's quote-scoped platform pricing is a negotiated commitment. Modelling your volume against both shapes tells you which bill you would rather carry.
Does the pending Salesforce acquisition matter here?
For long-term planning, yes. The catalogue's own record, verified in August 2026, notes Salesforce has agreed to acquire the company with Fin expected to fold into the Agentforce line. Roadmap and contract questions belong in any multi-year decision, and sales teams should answer them directly.
Which is the bigger implementation lift?
Decagon, by design: a platform bought as infrastructure brings integration and knowledge work before value shows. Fin's pitch is speed, layering on the helpdesk and help centre you already maintain. The trade is depth of ownership against time to first resolution, which is the real axis of this page.
Related comparisons
Read the full guides
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Tool facts last checked August 2026