Mike HeapMike is an experienced Product Manager who focuses on all the “non-development” areas of My AskAI, from finance and customer success to product design, copywriting, testing and more.
Table of Contents
- How are Intercom Fin and Decagon different from their old 'AI bots'?
- How does an AI customer service agent work in your helpdesk?
- How can I use Fin or Decagon in my helpdesk?
- Direct replies
- Copilot replies
- Voice
- Which is easier to set up: Intercom Fin or Decagon?
- How do Intercom Fin and Decagon differ in what they can be trained on?
- 'Static' content
- 'Dynamic' content
- Which has better answer quality, Intercom Fin or Decagon?
- Is Intercom Fin or Decagon easier to improve?
- Which has more features: Intercom Fin or Decagon?
- How easy is it to customize Intercom Fin and Decagon?
- What about vendor lock-in with Intercom Fin or Decagon?
- Do Intercom Fin or Decagon have any other AI features?
- Tagging
- Agent translation
- Sentiment and CSAT prediction
- What about security, is Intercom Fin more secure than Decagon?
- Which costs more, Intercom Fin or Decagon?
- The pricing model
- The overall cost
- Conclusion - should I choose Intercom Fin or Decagon?
- FAQs
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We scored Intercom Fin and Decagon across eleven categories: Fin wins five of them, Decagon wins three, and three finish level. Fin's rate is published at $0.99 an outcome, and you can switch it on yourself today. Decagon is sales-gated, publishes no price at all, and takes its three wins on live CRM data, agent versioning and QA coverage. Fin fits most teams below enterprise volume, and Decagon is built for the ones above it.
Fin's entry price is $49 a month, which includes 50 resolutions. Vendr puts the low end of a Decagon contract at $105,000 a year.
That gap settles most of these evaluations before anyone opens a feature list. On the calls I run, it usually settles in the first ten minutes.
Full disclosure before we get going. I co-founded My AskAI, and we sell an AI support agent that competes with both of the products below. Neither of them is ours.
We help 200+ ecommerce and SaaS businesses run AI support inside the helpdesk they already have, and our agents have resolved over a million tickets between them.
How are Intercom Fin and Decagon different from their old 'AI bots'?
TL;DR: Both replaced scripted decision trees with LLM agents that read your knowledge and act on your systems. Fin grew out of a helpdesk, and Decagon was built standalone as an enterprise agent that needs someone else's helpdesk underneath it.
Both read your content, reason over it, and take actions in your other systems.
Fin got there the long way (four major releases in three years). Fin 1 launched in March 2023 on GPT-4 and resolved about 23% of conversations. Fin 2 arrived in October 2024 on Claude and hit 51%.
Fin 3 followed in October 2025, and Fin Apex 1.0 landed in March 2026 on an in-house model built for support, which is how Salesforce describes the engine in the announcement of its deal to buy the company. Intercom renamed itself Fin in May 2026, so check which release any review you read was testing (the 23% version and today's version are not the same product).
Decagon started from the other end. Founded in 2023, out of stealth in June 2024, it sells Agent Operating Procedures as its pitch, where you write the procedure in natural language. It has raised around $481M and was valued at $4.5B in a Series D in January 2026, a valuation its own employee tender post repeats, and it has 300+ staff.
Both review well on G2:
ㅤ | Intercom Fin | Decagon |
Rating | 4.5 / 5 | 4.9 / 5 |
Reviews | 3,733 | 18 |
Reviews page |
Decagon's 4.9 rests on 18 reviews, so I read the write-ups rather than the average. One verified reviewer in e-learning puts it plainly:
"When we launched Decagon for chat, it immediately deflected 75-80% of our tickets. The admin features are robust and customizable, and updating knowledge for the bot is very simple."
We have a full guide to the Fin AI agent and a full guide to Decagon on the blog if you want either one on its own terms first.
How does an AI customer service agent work in your helpdesk?
TL;DR: Both take in your knowledge, retrieve against the customer's question, and either answer or hand off. The difference is where the agent lives. Fin is part of a helpdesk it can also leave, and Decagon always sits on top of one you already run.
Fin's engine runs seven specialized models: Apex 1.0 for the answer, plus Retrieval, a Reranker, an Issue Summarizer, a Feedback Parser, a Language Detector and an Escalation Router. An AWS case study on the architecture counts 15 to 20 subcomponents behind the reply.
![Intercom Fin’s conversation thread with an end customer, showing Fin’s greeting, the run-time trail ("Fin set Language to English", "Fin’s personality: Professional, Standard", an "Asking shopify details" step marked failed) and the grounded answer below it carrying a [1] citation.](https://www.notion.so/image/attachment:65db5122-fca0-4b2a-833f-31331df457b2:intercom-fin-decagon-ai-comparison-2026-screenshot-1.png?table=block&id=3b8cbb2d-d767-8190-afad-c3542063ea7c&cache=v2)
Decagon is model-agnostic and mixes OpenAI, Anthropic and Cohere. Its own twist is a supervisor model that checks the draft before it goes out and catches hallucinations pre-send (the check I want most on a refund flow). It quotes p95 latency under 400ms, and runs fine-tuned models for voice.
If you have been reading the "are these just wrappers?" threads, both trust centers answer part of that. Fin and Decagon each name OpenAI as a subprocessor, and each builds a great deal of its own machinery on top.
Decagon has no helpdesk of its own. It needs Zendesk or Salesforce underneath it for human handoff, because there is nowhere else for an escalated conversation to land. That single fact drives more of this decision than any feature does, so I check where an escalation lands before I look at anything else.
How can I use Fin or Decagon in my helpdesk?
TL;DR: Both do direct customer replies and both do voice. Copilot is where they split: Fin's runs in Intercom, Zendesk and Salesforce inboxes, and Decagon's Agent Assist is Zendesk-only.
Direct replies
This is the main mode for both, and Fin reaches further. It answers in Intercom Messenger on web, iOS and Android, plus email, phone, WhatsApp, SMS, Facebook Messenger, Instagram, Slack, Discord, the public API, and inside Zendesk and Salesforce. Salesforce's announcement lists the agent resolving queries
"across every channel, including live chat, email, WhatsApp, SMS, phone, and Slack."
Decagon covers chat, email, voice and SMS, with memory carried across channels so a customer who starts on chat and calls later is not starting again. Its own site navigation lists exactly four surfaces: Voice, Chat, Email and Duet. No social channels are documented anywhere, so if Instagram DMs are a real queue for you, I would get that answered before the demo ends.
Decagon has no listing on the Zendesk marketplace, the Intercom App Store or Salesforce AppExchange. Every integration is direct API work, which is a line item for whoever owns your engineering time.
Copilot replies
Fin Copilot drafts answers for your human agents inside the inbox, and it works in Intercom, Zendesk and Salesforce. It is a paid seat, at $29 per agent per month billed annually, or $35 billed monthly. Every plan gets 10 Copilot conversations free each month (a demo's worth, and no more).
Decagon's equivalent is Agent Assist, and it only runs in Zendesk. If your agents work in Salesforce, Freshdesk or anything else, the copilot half of Decagon is not available to them.
Voice
Both have a real voice product. Fin Voice is quoted by sales, covers 28 languages, and handles one language per phone line. Decagon runs fine-tuned voice models and added outbound campaigns with Voice 2.0 in spring 2026, so it does more than answer the phone.
Voice sits outside the published rate on both sides, so I ask for it as its own line on the quote.
Which is easier to set up: Intercom Fin or Decagon?
TL;DR: Fin, and it is not close. You can start it yourself on a 14-day trial with no card and no call. Decagon has no self-serve path at all, so every deployment starts with a sales conversation and runs about six weeks with Decagon staff embedded.
Fin lets you buy it like software. The pricing page says the trial gives you
"unlimited access to Fin outcomes, with no limits on usage. There are no seat minimums or maximums"
and that free trials need no credit card. Intercom claims you can be live in under an hour with no consulting, and its own Zendesk customer testimonial says within 30 minutes.
Capterra reviewers report Fin resolving around 28% out of the box, and describe deliberate content engineering to get it to 50%.
Turning it on is an hour. Getting a number you would show your CEO is weeks of writing, and I book that writing time before the trial clock starts.
Decagon's onboarding runs in six phases with an assigned project manager and forward-deployed engineers, over roughly six weeks.
Co-founder Ashwin Sreenivas has said core infrastructure can be up and running in days, and some G2 reviewers report under a week. You cannot test that yourself, though: Decagon's pricing page still returns a 404 today, there is no signup button anywhere on the site, and the documentation sits entirely behind a login.
Buyers hit the same wall, and the clearest version I found came from u/Many-Personality-157, in a July 2026 thread comparing Decagon against other agents:
"the product is genuinely impressive, but it seems designed for larger companies. The pricing discussion quickly moved into enterprise contracts and custom quotes, which put it outside the range of a lot of startups and smaller teams."
No named Decagon customer has published their actual deployment time, so those timings all trace back to Decagon or to a review. I would push the rep for one.
My AskAI installs into Intercom, Zendesk, Freshdesk, Gorgias or HubSpot in about ten minutes, with no developer. We built it that way so you can run a third agent against your incumbent inside the helpdesk you already have.
How do Intercom Fin and Decagon differ in what they can be trained on?
TL;DR: Fin takes more source types but fences the best ones off, because Notion, Guru and Confluence feed Copilot only and never autonomous replies. Decagon takes fewer named sources but pulls live CRM and internal API data into the answer itself.
'Static' content
Fin's list is long and public. Help Center articles, internal articles, snippets, conversation snippets from resolved tickets, PDF and DOCX uploads, and up to 10 external sources of 3,000 URLs each. Sync cadence varies by source: Intercom content is instant, Zendesk hourly, Notion, Guru and Confluence every 24 hours, crawled URLs weekly, and PDFs only when you refresh them by hand.
Notion, Guru and Confluence content feeds Fin Copilot only. If your best internal documentation lives in Confluence, your agents get it and your customers do not. I always check where the best articles actually live before counting them as training for the customer-facing side.
Decagon calls its layer a Unified Knowledge Graph. It ingests Zendesk, Kustomer, Guru, Confluence and Contentful help centers, standard operating procedures, and Slack.
File-type support is not documented anywhere public, because the whole documentation surface sits behind a login, and configuration happens during white-glove onboarding.
'Dynamic' content
Fin reaches live systems through Data Connectors, which are wired up per customer (so treat it as a scoping question for your engineers).
Decagon goes further here, and I would call it the clearest reason its training row beats Fin's. It reads Salesforce Customer 360, Shopify and Stripe, and connects to your own internal business APIs for live order and account data. The agent answers out of your live database as well as your help center.
Source | Intercom Fin | Decagon |
Help center articles | ✅ | ✅ |
Past resolved tickets | ✅ (conversation snippets) | ✅ (during onboarding) |
Notion, Confluence, Guru | ✅ (Copilot only) | ✅ (Confluence, Guru) |
Google Drive, OneDrive, SharePoint | ❌ | — (not documented) |
Salesforce customer records | ❌ | ✅ (Customer 360) |
Shopify and Stripe | ✅ (Fin for Ecommerce) | ✅ |
Internal APIs | ✅ (Data Connectors) | ✅ |
Standard operating procedures | — (not documented) | ✅ |
Direct file uploads | ✅ (PDF, DOCX) | — (not documented) |
My AskAI reads Google Drive, Notion, Confluence, SharePoint, OneDrive, Dropbox, Salesforce and Shopify for customer-facing answers.
Which has better answer quality, Intercom Fin or Decagon?
TL;DR: Fin publishes a 76% resolution rate and Decagon publishes 80%+ deflection, which are different measurements and do not compare. Fin's own billing counts a customer who stops replying for 24 hours as a resolution, while Decagon's glossary insists the issue has to be fixed to count.
Each vendor leads with one number. Fin's comparison page says:
"Fin's 76% average resolution rate across 8,000+ customers reflects genuine positive resolutions."
Decagon publishes 80% average deflection and a 93% agent-quality score, with named customer results behind it: Substack at 90%, Duolingo at 80%, Chime at 70%, and Rippling moving from 38% to 50%.
A resolution-rate benchmark study puts the field median at 70% across 195 rated deployments spanning 38 vendors, with the middle half between 56% and 80%. Treat it as a rough map of where the market sits (every vendor in it defines its own headline metric, the same slipperiness sitting under Fin's and Decagon's numbers here). The published figures behind it are self-selected wins too, so the real market number is probably lower still.
Every head-to-head comparison of these two sets the numbers side by side as if they meant the same thing. Fin's page defines deflection like this:
"Deflection rate measures the percentage of conversations that never reach a human agent. This includes abandoned conversations, customers who gave up, partial answers that technically addressed the question without solving the underlying issue, and frustrated users who stopped responding."
"A contact counts as deflected only when the underlying issue is actually resolved, not merely when the customer abandons the interaction before reaching a human."
So Fin is criticizing a definition Decagon publicly rejects. I ask for both definitions in writing before accepting either headline figure.
The twist is what Fin actually bills you for. From Fin's own help center article on outcomes:
"A resolution is counted when, following Fin's last answer in a conversation, the customer either confirms the answer was satisfactory (confirmed resolution), or exits the conversation without requesting further assistance (assumed resolution)."
"If a customer disengages from the conversation for 24 hours after Fin's last answer, it is considered an assumed resolution."
The customer who gave up is inside Fin's invoice, under the name "assumed resolution". That is the exact behavior Fin criticizes deflection for counting.
Fin does correct for it. If the customer comes back to that conversation later, even in a later billing period, the resolution is deducted and not charged (most vendors do not do it). Decagon publishes no billing definition at all, so there is nothing to compare it against.
The two companies have had this argument in public too. Eoghan McCabe, Fin's CEO, posted in June 2025:
"The result is that we've beaten Decagon on performance in 100% of bake-offs that we've engaged in and have been chosen as vendor of choice 100% of those times too."
"In a customer-run bake-off, Fin achieved a resolution rate of 63% (which has continued to increase to 72%, as of last week), while Decagon achieved a resolution rate of 49%."
That is one vendor's CEO on X, describing bake-offs his own company took part in, at a customer he does not name. Fin does back its number with money on the Fin Guarantee page:
"If you sign up for our Fin Guarantee Success Program and do not achieve at least a resolution rate of 65%, we will pay you $1M."
Eligibility is over 250,000 monthly conversations, in North America or Europe, on Intercom or Zendesk. So it is not a promise to most people reading this, and I would not weight it heavily unless you are already at that volume. It is still a number with a check behind it.
The counterweight comes from Contrary Research's independent profile of Decagon, the closest thing to a neutral source I found on either vendor, and it carries a customer saying:
"Though we already had a robust Voice of the Customer program and an understanding of customer inquiries we thought we could deflect, we saw 10x higher deflection at launch than we anticipated."
Decagon is also sharper about the failure mode than most vendors would be. From its post on why chatbots fail:
"Chatbots fail because they are designed to prioritize deflection metrics over successful escalation paths, trapping frustrated customers in loops when they desperately need human assistance."
Support practitioners say the same thing in less polished language. u/escalation_queen, in a February 2026 thread on deflection rates:
"people are literally saying 'i tried to get help and just gave up.' that's not deflection, that's abandonment dressed up in a metric."
So verifiability is what gives Fin this row in my scoring. Its definition, its billing rule and its clawback are all published, and its number has a payout attached.
Side by side, the two headline numbers are counting different events:
ㅤ | Intercom Fin | Decagon |
Headline metric | 76% resolution rate | 80%+ average deflection |
Basis of the claim | Across 8,000+ customers | Named customer case studies |
Counts a customer who gave up | Yes, as an assumed resolution | No, per its own glossary |
Billing definition published | ✅ | ❌ |
Money behind the number | $1M Fin Guarantee above 65% | None published |
Decagon's product is strong, and its weakest G2 category score is Ticket Resolution at 7.9 out of 10. Get the metric definition in writing from both before you compare any two percentages, because I have not seen two vendors in this market measure it the same way.
Is Intercom Fin or Decagon easier to improve?
TL;DR: Decagon. Its improvement tooling is version-controlled like software, so every change is a commit you can diff and roll back, and Watchtower reviews every conversation. Fin's equivalent depth sits behind a $99 a month Pro add-on.
Decagon treats the agent as a codebase. Agent Versioning tracks every procedure, tool and guideline edit as a versioned commit, with production and staging workspaces, diff review before release, rollback, branch protections and release gates. If your compliance team wants to know who changed the refund policy and when, the answer is a diff (no other support vendor in this market ships this).
Around it sit three more loops. Watchtower reviews every conversation, AI and human, against rubrics you write.
Experiments A/B test changes on live traffic. Knowledge Suggestions drafts new articles each month based on how your human agents actually resolved things, which is the loop I want running from month one.
Fin's loop is good, and more of it is metered. Optimize is now surfaced as Recommendations, which delivers weekly, impact-ranked findings across content gaps, customer-data gaps and action gaps. The richest version needs the Pro add-on:
"Pro is an optional add-on at $99/month, which includes analysis of 1,000 conversations/month and 2,000 credits per month."
That is from Intercom's pricing page. Around it you get Simulations, batch tests of 50 questions at a time, Previews, Controlled Rollout and a hard resolution cap so a runaway agent cannot run up a bill (I set that cap on day one).
When an answer came from a Procedure, there is no Improve Answer button on it. The most complex answers are the ones you cannot correct in one click, so I would put that in front of whoever owns your content before you build on Procedures.
Which has more features: Intercom Fin or Decagon?
TL;DR: A tie, and they are broad in different directions. Fin reaches further across channels and commerce, and Decagon goes deeper on enterprise operations, with QA on every conversation, decision tracing and autonomous debugging.
Fin's headline capability is Procedures: multi-step workflows written in natural language, with Python snippets, if/else branching and wait-for-webhook steps. Tasks, the older version, was deprecated and new Task creation was switched off on 12 March 2026, so check which one your trial is building on. Alongside that sit Data Connectors, Fin Vision for reading customer screenshots, Fin Guidance, a Fin for Ecommerce package with live Shopify catalog sync and cart actions, and an API platform exposing Apex, RAG, Retrieval and the Reranker from $250,000 a year.
Decagon's set is built for operating an agent at scale: Agent Operating Procedures with a copilot for writing them, plus AI Actions for refunds, order updates and identity verification through Stripe, Shopify and Salesforce. Then Watchtower for QA, Trace View for following a decision, and Ask AI for querying conversation data in plain English. Its spring 2026 launch added Proactive Agents, an Agent Workbench that debugs the agent for you, and Duet.
Feature | Intercom Fin | Decagon |
Multi-step natural-language workflows | ✅ (Procedures) | ✅ (AOPs) |
Image input from customers | ✅ (Fin Vision) | — (not documented) |
Live commerce actions | ✅ (Shopify catalog, cart) | ✅ (Stripe, Shopify, Salesforce) |
Custom-rubric QA on every conversation | ❌ | ✅ (Watchtower) |
Decision tracing | ✅ (conversation logs) | ✅ (Trace View) |
Autonomous agent debugging | ❌ | ✅ (Agent Workbench) |
Outbound and proactive messaging | ✅ (not in standalone mode) | ✅ (Proactive Agents) |
Public API platform | ✅ (from $250k/yr) | — (not documented) |
Voice | ✅ | ✅ |
The row is a tie in my scoring. Fin covers more of the customer-facing surface, while Decagon goes deeper on the operator-facing one.
How easy is it to customize Intercom Fin and Decagon?
TL;DR: Both let a non-engineer write behavior in plain English, and both still reach for a developer at the edges. Fin caps its guidance at 100 active pieces of 2,500 characters each, and Decagon's procedures go further but compile into logic you may need help with.
Fin gives you named categories to write rules into: Communication Style, Context and Clarification, Content and Sources, Spam, Escalation, and channel-specific behavior. There are five tone presets, answer-length controls, and per-language pronoun formality, which is more thought than most vendors give to the difference between "tu" and "vous".
The limits are published, and I prefer that to a vague promise: 100 active guidance pieces, 2,500 characters each. Big support operations hit that ceiling.
Decagon's AOPs are pitched as combining the flexibility of natural language with the precision of coded logic. In practice that means the simple procedures are writable by anyone and the advanced ones reach for an engineer.
Every AOP edit lands as a versioned commit, so changing how the agent acts is a change-control exercise as much as a writing one. I would ask who on your team owns that before you buy it.
Fin's own comparison page argues exactly that. Its table describes Decagon's setup as
"Advanced AOP changes often done with Decagon"
and its iteration as
"Iteration often involves coordination with Decagon team"
against Fin's own column of "Fully self-managed". Fin is not a neutral witness here, but the point also shows up in Decagon's G2 reviews, which describe basic user roles and shallow audit logs.
Neither vendor gives you a self-serve way to test the ceiling, because Decagon has no trial at all and its pricing page still 404s. If deep customization is your requirement, I want those limits in writing before anyone signs.
What about vendor lock-in with Intercom Fin or Decagon?
TL;DR: Fin can leave Intercom, and runs standalone on Zendesk, Salesforce, Freshdesk and HubSpot, though it loses Slack and outbound when it does. Decagon cannot stand alone at all, so you keep paying for a helpdesk underneath it.
Fin runs on helpdesks other than Intercom. Intercom's pricing page spells it out:
"Use Fin with your current helpdesk including Salesforce and more."
That page lists it from $0.99 per Fin outcome, with no seats required.
Slack and proactive outbound are not supported outside Intercom, and neither are Fin's sales outcomes. The help center article on outcomes is explicit:
"Fin for sales (qualifications and disqualifications) is currently not available with Fin for platforms outside of Intercom"
Billing is USD-only, annual contracts cannot be canceled mid-term, and enterprise renewals need 30 days' notice (read the mid-term clause closely if your volume is seasonal).
Salesforce signed a definitive agreement on 15 June 2026 to buy Fin for approximately $3.6 billion. The transaction has not closed:
"The transaction is expected to close in the fourth quarter of Salesforce's fiscal year 2027, subject to the satisfaction of customary closing conditions, including the receipt of required regulatory clearances."
Nothing changes for a customer today. But if you are signing a three-year deal, the roadmap you are buying belongs to a company that is being acquired, and I would put that question to your rep early. Fin and Salesforce's own Agentforce are still separate products.
Decagon's version is structural and harder to unwind. It has no helpdesk of its own, so it needs Zendesk or Salesforce underneath it for human handoff, and you pay for both (that second bill is easy to forget when you are comparing quotes).
Its own site lists four surfaces, Voice, Chat, Email and Duet, with no inbox among them, and Agent Assist only runs on Zendesk. Contracts are annual by default with no published rate to check them against, since its pricing page returns a 404, and Vendr records a "Median contract value $432,750 per year" for exactly that reason.
There is no marketplace app to uninstall, because every connection was built for you by API and the documentation describing that surface sits behind a login. I would ask what an exit looks like, in writing, before you sign.
Fin's lock-in is contractual and Decagon's is structural.
My AskAI installs into Zendesk, Intercom, Freshdesk, Gorgias or HubSpot. If you change helpdesk later, we carry the training, guidance, custom answers and tasks across to the new one.
Do Intercom Fin or Decagon have any other AI features?
TL;DR: Both auto-classify, and both analyze every conversation. Decagon includes its QA layer in the platform, and Fin's best analytics sit behind a $99 a month add-on.
Tagging
Fin's classification runs through Insights. Topics Explorer groups every conversation, the Topic Trends Report shows what is growing, Query Type classification splits questions from actions, and it flags duplicate or contradictory content in your help center.
Decagon's version lives inside Watchtower and its QA hub. It classifies every conversation, and goes further than topic labels: it detects sentiment, flags mentions of fraud, and raises regulated-complaint alerts for compliance teams. If you are in financial services, that last one is the reason Decagon's name comes up in the deals I hear about.
Agent translation
Fin publishes 45 languages for the AI Agent and Copilot, 28 for Voice, and one language per phone line, which is a real constraint if you run a single international number. Every plan includes 10 AI auto-translation conversations a month.
Fin's marketing suggests cross-language retrieval works, while its own FAQ calls the behavior currently unpredictable when your content is in one language and the customer writes in another.
Decagon claims any language, backed by Rituals Cosmetics running 15 in one deployment from a single knowledge base. It publishes no per-language quality benchmarks, so ask for results in your second-biggest language (that is where the coverage usually thins).
Sentiment and CSAT prediction
Fin's strongest analytics argument is CX Score, applied to 100% of conversations, against a typical CSAT response rate of 5 to 10%, so you stop guessing from a sample. It sits behind the Pro add-on at $99 a month for 1,000 conversations analyzed, with extra credits at $0.01 each.
Decagon's Watchtower includes sentiment on every conversation as part of the platform, alongside its 93% agent-quality score. Included beats metered here, which is why I gave the row to Decagon.
What about security, is Intercom Fin more secure than Decagon?
TL;DR: Neither is the weak link. Fin holds more certifications and three data-residency regions, both trust centers are Vanta-hosted and gate their evidence, and the one durable asymmetry is PCI DSS 4.0.1, which Decagon holds and Fin does not.
Fin's trust center is the longer list: SOC 2, ISO 27001:2022, ISO 27018, ISO 27701, ISO/IEC 42001:2023, GDPR, CCPA, HIPAA, HDS, AIUC-1, CSA STAR, CSA STAR for AI and the EU AI Act. It lists the SOC 2 Type 2 report, the ISO certificates, the HIPAA report, and penetration tests for 2024 and 2025 as downloads. Subprocessors are published, and data can be hosted in the US, EU (Dublin) or Australia.
Decagon's trust center is shorter but not weak: SOC 2 Type II with reports for two periods, ISO 27001:2022, HIPAA, GDPR, CCPA, the EU AI Act, and PCI DSS 4.0.1. A SIG Lite from May 2026 and an Information Security Overview from June 2026 have both appeared recently, alongside an October 2025 pentest, published subprocessors, Google DLP auto-redaction, and zero-day retention with its LLM providers.
Both pages carry a "Request access" style gate on the documents, so neither vendor lets your reviewer self-serve the evidence. I put that request in on day one and budget a few days for it.
The one difference that survives all of this is PCI DSS 4.0.1. Decagon holds it and Fin does not, so if card data touches your support flow, that single line may decide the security review on its own. Running the other way, Decagon's G2 reviewers describe basic user roles and shallow audit logs, which is the sort of thing a security reviewer finds in week three.
Which costs more, Intercom Fin or Decagon?
TL;DR: Decagon, by a distance, and you have to book a call to find out by how much. Fin publishes $0.99 per outcome on a $49 a month base plan that includes 50 resolutions, and Vendr's marketplace data puts Decagon's median annual contract at $432,750.
The pricing model
Fin charges for results. Its help center says:
"Fin is priced at $0.99 per outcome (United States Dollars), which is billed as part of a base plan costing $49 per month."
"The base plan for Fin with your helpdesk includes 50 resolutions, with additional outcomes charged at $0.99 each."
Four things count as an outcome, and the rates are not equal:
"Resolutions, Procedure handoffs, and disqualifications are $0.99 each. Qualifications are $9.99 each."
A sales qualification bills at ten times the support rate, which is easy to miss when you turn Fin loose on your pre-sales queue. You are charged once per conversation even if Fin resolves several things in it, and minimum resolution commitments apply. I would get that commitment number in writing, because it sets your floor.
Run Fin inside Intercom's own helpdesk and you also pay seats: $29 a month on Essential, $85 on Advanced, $132 on Expert. Intercom runs a time-limited new-customer promotion of 35% off Essential, but $29 is the list price (promotions expire, and the list price is what your renewal uses).
On another helpdesk there are no seats and no platform fees. Add-ons stack on top: Pro at $99 a month, Copilot at $29 or $35 per agent, and billing is USD-only.
Decagon's model you have to be told. Decagon's pricing page returns a 404, and always has.
Both per-conversation and per-resolution models exist, and Contrary Research reported in December 2024 that the "majority of [Decagon's] customers gravitate toward" the per-conversation one, with the bracketed insertion being Contrary's. Sacra estimates around $1.50 per resolution, and I treat that as a rough marker only.
Volume commitments and negotiated discounts sit on top of both, and none of that is published either. I would ask for the per-conversation and the per-resolution quote side by side, because they are not interchangeable at your volume.
The overall cost
Vendr's Decagon marketplace page is the only public read on what Decagon actually costs. It reports a median contract value of $432,750 per year, a low of $105,000 and a high of $923,183, and estimates a $50,000 redline threshold. For comparison, the same page has Dixa at a $179,000 median.
Fin's own comparison page puts different numbers on it: a $50,000 annual platform fee, around $0.99 per conversation, a reported $0.50 per resolution, and median annual contracts around $400,000. That is one vendor describing a rival's undisclosed pricing, so treat those as claims, and note they land near Vendr's anyway.
One scenario I run for people: 10,000 conversations a month at a 50% resolution rate.
ㅤ | Intercom Fin | Decagon |
Model | $0.99 per outcome, $49 base plan | Per conversation or per resolution, negotiated |
Published rate | Yes | No |
10,000 conversations, 50% resolved | ~$4,950 a month | Not calculable without a quote |
Contract floor | $49 a month | $105,000 a year (Vendr low) |
Free trial | 14 days, no card | None |
Fin's side is arithmetic: 5,000 outcomes, minus the 50 in the base plan, at $0.99 each, plus the $49 base. Decagon's cannot be worked out until someone sends a quote, and Vendr's low end is roughly $8,750 a month before you negotiate.
A per-outcome bill goes up precisely when the agent gets better at its job, so pushing your resolution rate from 50% to 75% raises the invoice by half. Run that sum before you sign an annual commitment.
Fin's own users see it. One on r/intercom:
"We have been on Fin for about fourteen months and the bill has roughly tripled as we have scaled ,which is fine in principle because volume tripled too,but the per resolution cost is starting to feel like we are subsidising Intercom's R&D."
There is a third pricing model in this market, a flat rate per ticket. My AskAI charges about $0.10 per ticket, so the AI charge stays level as the resolution rate climbs, on the same basis as Fin's $4,950 of outcomes, and we give you 30 days with all features unlocked, unlimited tickets and no card.
We have full pricing breakdowns for both Fin and Decagon on the blog if you want the line-by-line version.
Conclusion - should I choose Intercom Fin or Decagon?
TL;DR: Fin wins the scorecard, five categories to three, and it is the answer for almost every buyer under enterprise scale. Decagon earns its price if you are running enterprise volume and need version-controlled agent operations with QA on every conversation.
For most teams reading this, Fin is my answer, and price is the reason before quality is. You can look up the rate, start a trial today, and keep the helpdesk you have. Decagon flips that when your volume is enterprise, your procurement team has requirements about how the agent is changed and reviewed, and six figures a year is already in the budget.
The eleven categories, scored:
ㅤ | Intercom Fin | Decagon | ㅤ |
Modes | 9/10 | 7/10 | Fin win |
Ease of setup | 9/10 | 5/10 | Fin win |
Training/Integrations | 8/10 | 9/10 | Decagon win |
Answer quality | 9/10 | 8/10 | Fin win |
Improving | 8/10 | 9/10 | Decagon win |
Features | 9/10 | 9/10 | Tie |
Price | 8/10 | 3/10 | Fin win |
Customization | 8/10 | 8/10 | Tie |
Lock-in | 7/10 | 4/10 | Fin win |
Other AI features | 8/10 | 9/10 | Decagon win |
Security | 9/10 | 9/10 | Tie |
That is five rows to Fin and three to Decagon, with three level. I do not read the three Decagon wins as consolation prizes.
Read them together and they describe one buyer. Decagon wins on training reach because it pulls live CRM and internal API data into the answer.
It wins on improvement because the agent is versioned like software, and it wins on other AI features because QA on every conversation is included rather than metered.
That combination is what a large support operation with a compliance function actually buys. Fin has no equivalent to Agent Versioning today, and I raise that on every Fin call I sit in on.
Choose Decagon if:
- You are running enterprise conversation volume, which is the only tier Decagon sells into.
- You already run Zendesk or Salesforce and are happy to keep paying for it underneath the agent.
- Version-controlled agent operations and per-conversation QA sit in your procurement requirements.
- Your budget starts above $105,000 a year.
Choose Fin if:
- You want to know the unit rate before you book a call, and $0.99 an outcome is published.
- You want to test the agent yourself, on a 14-day trial with no card.
- You want to keep Zendesk, Salesforce, Freshdesk or HubSpot and run the agent standalone on it.
- Your customers reach you on WhatsApp, SMS, social or Slack as well as chat and email.
There is a third route: a helpdesk-agnostic agent on a flat per-ticket rate. My AskAI runs inside Zendesk, Intercom, Freshdesk, Gorgias or HubSpot at about $0.10 a ticket, so the bill does not climb as the agent improves, and you can trial it beside your current agent without changing helpdesk.
We have alternatives roundups for both Fin and Decagon on the blog, and if the vendor across the table is Zendesk, our Zendesk AI versus Intercom Fin head-to-head is the closer match.
FAQs
Can Decagon fully replace Intercom Fin inside your helpdesk?
No, Decagon has no helpdesk of its own, so it needs Zendesk or Salesforce underneath it for human handoff, and its Agent Assist copilot only runs on Zendesk. It has no marketplace listing on Zendesk, the Intercom App Store or AppExchange either, so every connection is direct API work. Fin does run standalone on Zendesk, Salesforce, Freshdesk and HubSpot with no seat costs, so replacement runs more easily in that direction.
What can each AI agent be trained on?
Fin takes more named source types, and Decagon reaches deeper into live data.
Source | Intercom Fin | Decagon |
Help center articles | ✅ | ✅ |
Notion, Confluence, Guru | ✅ (Copilot only) | ✅ |
Salesforce customer records | ❌ | ✅ (Customer 360) |
Internal APIs | ✅ (Data Connectors) | ✅ |
File uploads | ✅ (PDF, DOCX) | — (not documented) |
Content from Notion, Guru and Confluence reaches your agents but never your customers, so I size Fin's customer-facing training from the help center alone.
How long does setup take and do we need developers?
Fin can be live in under an hour by Intercom's own account, on a 14-day trial with no credit card. Capterra reviewers report it resolving around 28% out of the box, with deliberate content work needed to reach 50%, so budget weeks for the content.
Decagon runs a six-phase onboarding with an assigned project manager and forward-deployed engineers, over roughly six weeks. There is no self-serve path to test that yourself, because Decagon's pricing page 404s and the documentation sits behind a login. I ask for a reference customer's actual timeline instead.
Intercom Fin vs third-party AI agents, which is better value?
It depends on how good your agent gets. Per-outcome billing charges you more precisely when the AI is working best, so the bill climbs with the resolution rate. A flat per-ticket rate does not move.
ㅤ | Intercom Fin | A flat per-ticket agent |
Model | $0.99 per outcome | ~$0.10 per ticket |
10,000 conversations, 50% resolved | ~$4,950 a month | ~$1,000 a month |
10,000 conversations, 75% resolved | ~$7,425 a month | ~$1,000 a month |
Free trial | 14 days, no card | 30 days, all features, unlimited tickets, no card |
Both columns are the AI charge only. You keep paying your helpdesk seats either way, so those cancel out and are left off both sides. My AskAI is the per-ticket example here, and we charge about $0.10 a ticket.
Is Fin's 76% resolution rate the same as Decagon's 80% deflection rate?
No. The two vendors are counting different events, and they do not agree on what the second one means: Fin's comparison page treats an abandoned conversation as deflected, while Decagon's glossary counts a contact only where the problem behind it actually got fixed.
Fin's own billing rule is the part worth reading twice. A customer who stops replying for 24 hours is booked as an assumed resolution and charged as an outcome, which is the behavior Fin criticizes deflection for counting, though Fin does deduct the charge if that customer comes back later.
Can they handle multilingual support and agent translations?
Fin publishes 45 languages for the AI Agent and Copilot, 28 for Voice, and one language per phone line. Its own FAQ describes cross-language retrieval as currently unpredictable when your content and your customer are in different languages, so test that case specifically.
Decagon claims any language from a single knowledge base, with Rituals Cosmetics running 15 in one deployment. It publishes no per-language benchmarks, so I press for results in your second-biggest language.
How do they compare on security and compliance?
Fin holds more: SOC 2, ISO 27001:2022, ISO 27018, ISO 27701, ISO/IEC 42001:2023, HIPAA, HDS, AIUC-1, CSA STAR, CSA STAR for AI, the EU AI Act, GDPR and CCPA, with US, EU and Australian data residency. Decagon holds SOC 2 Type II, ISO 27001:2022, HIPAA, GDPR, CCPA, the EU AI Act and PCI DSS 4.0.1.
The one Fin does not have is PCI DSS. Both trust centers gate their documents behind a request, so I get that request in early.
Does each one offer a free trial?
Fin does: 14 days, unlimited outcomes, no credit card and no seat minimums. Decagon does not.
There is no self-serve signup, no free plan and no published price, so the route in is a demo and a scoped deployment.
