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
- Decagon AI vs Front AI at a glance
- How are Decagon and Front different from their old 'AI bots'?
- How does an AI customer service agent work in Decagon and in Front?
- How can I use each agent, customer-facing, copilot, or voice?
- Direct replies
- Copilot replies
- Voice
- Which is easier to set up: Decagon or Front?
- How do Decagon and Front differ in what they can be trained on?
- 'Static' content
- 'Dynamic' content
- Which has better answer quality, Decagon or Front?
- Is Decagon or Front easier to improve?
- Which has more features: Decagon or Front?
- How easy is it to customize Decagon and Front?
- What about vendor lock-in?
- Do they have any other AI features?
- Tagging
- Agent translation
- Quality assurance and CSAT
- What about security, is Decagon more secure than Front?
- Which costs more, Decagon or Front?
- The pricing model
- The overall cost
- Conclusion - should I choose Decagon or Front?
- FAQs
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Decagon (decagon.ai) takes six of the eleven scored categories to Front (front.com)'s three, with two tied. Decagon keeps its rate behind a sales process. Front's Autopilot charges $0.89 per resolution and lets you trial it first.
Decagon is a dedicated enterprise AI support agent, and Front is a shared inbox with AI added on top. Decagon bolts an AI layer onto a helpdesk you keep paying for (Salesforce, Zendesk or Intercom) and publishes no price at all, with Vendr putting the median contract at $432,750 a year. Front charges by the seat and then, through its Autopilot add-on, $0.89 for a conversation its AI resolves on its own, and asks you to move your support onto Front to get it.
Front puts its rates, its resolution rule and its language limits in a public help center. Decagon keeps its own documentation behind a login screen, so none of it is public.
Full disclosure: I co-founded My AskAI, and we sell an AI support agent that competes with both products below. Neither of the two is ours. Our agents have resolved over a million tickets for 200+ ecommerce and SaaS businesses.
Decagon AI vs Front AI at a glance
Decagon (decagon.ai) | Front (front.com) | |
What it is | Dedicated enterprise AI support agent | Shared inbox with AI bolted on |
Headline price | None published; Vendr median ~$432,750/yr (Aug 2026) | $25 to $105/seat/mo plus $0.89 per Autopilot resolution |
How you buy it | Sales-gated; demo only, no trial | Self-serve; 14-day platform trial, 30-day Autopilot trial |
Best for | Enterprises at high conversation volume needing action-taking | SMB and mid-market B2B already living in email |
Not a fit for | Freshdesk, Gorgias, HubSpot or Front ticketing teams | Non-English support; teams keeping their current helpdesk |
Biggest limitation | No public documentation, all of it login-gated | Autopilot supports one language officially |
Scorecard | 6 category wins of 11, 2 tied | 3 category wins of 11, 2 tied |
How are Decagon and Front different from their old 'AI bots'?
TL;DR: Decagon replaced scripted flows with Agent Operating Procedures, business logic written in plain English that compiles to code. Front retired its AI Answers chatbot and replaced it with Autopilot, which only auto-replies on the topics an admin has switched on.
Decagon was founded in 2023 by Jesse Zhang and Ashwin Sreenivas.
It sells one product: an AI agent for large support teams that sits on top of the helpdesk you already run.
Front was founded by Mathilde Collin and Laurent Perrin in 2013. Its about page records $204M in venture funding, and Dan O'Connell has succeeded Collin as CEO (Collin is now co-founder and executive chair).
The homepage says Front is trusted by 9,300+ companies. Front AI is a separate company from Front, a conversational-AI vendor based in Helsinki with offices across Finland, Sweden and Denmark.
Decagon and Front both read your content, reason over it, and act in your other systems. Our own agent does the same three things.
Decagon rebuilt the agent itself. Its Agent Operating Procedures are workflow definitions written in plain English that compile to code, so a support lead can describe a policy instead of drawing a decision tree.
AI Answers and AI Tagging are both retired (their help articles now carry "[legacy]" in the title). The current stack runs Topics, then Copilot, then Autopilot, and Autopilot answers a customer only on a topic an admin has switched on.
Both review well on G2:
Decagon | Front | |
Rating | 4.9 / 5 | 4.7 / 5 |
Reviews page |
We have a full guide to Decagon and a full guide to Front on the blog if you want either one on its own terms first.
How does an AI customer service agent work in Decagon and in Front?
TL;DR: Decagon runs several models together, with a supervisor model checking for made-up answers before a reply goes out. Front works through model vendors and grounds its replies in your knowledge sources and your own past conversations.
Decagon is model-agnostic. It mixes OpenAI, Anthropic and Cohere with models of its own. It also runs a set of agents that review each other's work (a supervisor model checks the draft reply and catches invented answers first).
On the security page Decagon states zero-day retention with all of its model providers. It also states automatic redaction of personal data (names, emails and card numbers) before anything is sent onward.
Front builds no models itself:
"We do not train any models ourselves; we work through vendors for LLM use" Front, on its own Autopilot announcement thread
Its AI terms article names OpenAI, Microsoft and AWS as the partners behind that. Front grounds replies in the knowledge sources you connect, plus your historical and similar conversations, which are always in the mix. It picks up tone from how your team already writes.
Decagon's answer quality rides on how well its own models work together. With Front, the state of your topics and knowledge sources sets the ceiling. I find the second easier to fix, because you own the inputs.
Our own AI resolution rate benchmark study covers 195 rated deployments across 38 vendors. The median AI handling rate in it is 70%, on data as of May 2026.
Take that with a grain of salt: it is a directional aggregate across mixed products and setups, and the teams in it opted into being measured. It says nothing about what either vendor does on your queue.
How can I use each agent, customer-facing, copilot, or voice?
TL;DR: Front's Autopilot answers customers across email, chat, SMS, WhatsApp, Slack and custom channels. Decagon covers chat, voice and email, and its copilot also runs inside Front, Zendesk and Salesforce.
Direct replies
Front's Autopilot is the broader of the two on written channels: email, chat, SMS, WhatsApp, Slack and custom channels (Slack Connect included). Autopilot Resolve goes further and embeds self-service inside your own product, so a customer never leaves the app to ask a question.
Autopilot works on shared inboxes only (individual inboxes are out of scope). Front's documented workaround for a solo queue is a shared inbox that only one person can open.
Decagon covers chat, voice and email. SMS turns up once, in a machine-readable index on its site. Nothing else mentions it and there is no product page, so I read SMS as not yet live.
Copilot replies
Front's Copilot works on every Front conversation. That covers shared and individual inboxes, plus discussions and tasks. Front's own Copilot article puts it at $20 per seat per month on Starter and Professional (the Enterprise plan includes it at no extra cost) and caps drafting at 200 requests per user per day.
Decagon's copilot is Decagon Assist. Decagon calls it an AI copilot for the human representatives on your team, and the launch page is explicit about where it runs:
"Decagon Assist activates inside whatever system your team already uses, including Salesforce, Zendesk, and Front, so adopting it doesn't mean adopting a new interface on top of the ones reps have already onboarded." Decagon, on its Decagon Assist launch page
Decagon Assist runs inside Front as a copilot for your human agents. Decagon's autonomous customer-facing agent stays on Salesforce, Intercom and Zendesk.
Decagon draws that boundary itself, on its integrations page, where Front appears under no ticketing heading at all. So running the two together works at the copilot layer only.
Assist itself covers automated summaries, suggested responses, in-tool actions and an Ask AI mode (for questions outside a defined workflow). It adds real-time translation on chat, live transcription on voice, and an adoption analytics hub.
Voice
Decagon has a first-party voice agent (it also makes outbound calls). It covers 70+ languages, uses branded caller IDs, and answers the caller in well under half a second.
Front has no voice agent of its own. Phone support runs through its Dialpad, Aircall and RingCentral integrations. Front's own AI acquisition was Idiomatic in November 2024, which does customer-feedback analysis.
We have a separate roundup of agent-assist tools on the blog if the copilot layer is the part you are buying.
Which is easier to set up: Decagon or Front?
TL;DR: Front, decisively. You can start a 14-day Front trial without a card and run Autopilot free for 30 days up to 1,000 resolutions. Decagon has no self-serve signup and no trial at all, so you get a demo and roughly a six-week engagement with forward-deployed engineers.
Front starts with Topics. Front scans up to 10,000 conversations from your past 30 days and suggests a set of topics (generating that list takes one to two hours). An admin then reviews, renames and merges it.
After that it is a sequence of toggles. Enable Autopilot per inbox, connect a knowledge source, and read the sample drafts Front produces against what your teammates replied. Then switch auto-replies on topic by topic, and set the auto-generated rule and the AI disclosure line customers will see.
Front's own prerequisites article adds the catch. Front AI is not sold on its own (you have to already be a paying Front customer, and only company or workspace admins can turn it on).
Decagon has no self-serve signup and no free trial. You get a demo, then Agent Product Managers and Forward-Deployed Engineers embed with your team. Decagon's own account of the work runs about six weeks (from discovery to deployment, across six phases).
Co-founder Ashwin Sreenivas has said core infrastructure can be running in days, and one reviewer reports much the same:
"Implementation was very quick (<1 week)" A verified reviewer on Decagon's G2 page
Decagon University also runs sandboxes, live trainings and an AI CX Architect certification. I rate that highly for a vendor that offers no trial, because it is the one part of the rollout you can inspect before you sign.
The cost of a demo-only evaluation shows up in word-on-the-street. In an October 2025 r/customerexperience thread, a commenter who had spent real time with Decagon came away impressed, and every touchpoint he had was a sales call, so he could not say how it would behave in production.
On Front you can watch what the AI does with your own tickets before you pay for it.
My AskAI is a third route here. We built it to install into six helpdesks: Zendesk, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot. It opens on a 30-day free trial with no credit card, and can be answering real tickets the same day.
How do Decagon and Front differ in what they can be trained on?
TL;DR: Decagon builds one knowledge graph across help centers, CRM records, past transcripts and live business APIs. Front reads its own knowledge base, a public website crawl capped at about 3,000 pages, Notion, Google Drive and SharePoint, plus your past conversations.
Source type | Decagon | Front |
Native help center | ✅ Syncs from Zendesk, Kustomer, Guru, Confluence, Contentful | ✅ Front knowledge base |
Public website crawl | — Not documented publicly | ✅ Capped at about 3,000 pages |
Past conversations and tickets | ✅ Past transcripts | ✅ Always on |
Notion | — Not documented publicly | ✅ |
Google Drive | — Not documented publicly | ✅ |
SharePoint | — Not documented publicly | ✅ |
Confluence | ✅ Knowledge sync | — Not listed as a source |
Salesforce and CRM records | ✅ Salesforce Customer 360 | — Not listed as a source |
Shopify and Stripe data | ✅ | — Not listed as a source |
Live internal APIs | ✅ Custom APIs plus MCP | — Not listed as a source |
Direct file upload | — Not documented publicly | ❌ Attachments are read in-conversation, not stored as knowledge |
Per-agent source scoping | — Not documented publicly | ❌ Knowledge is workspace-wide |
'Static' content
Decagon builds a unified knowledge graph (its way of connecting the entities across everything you give it). One answer can then draw on several sources at once.
It covers help-center articles synced from Zendesk, Kustomer, Guru, Confluence and Contentful. On top of that sit product docs, past transcripts, internal process documents, Slack and custom databases. The integrations page keeps those knowledge feeds in a separate list from ticketing.
Decagon publishes no list of the file types it accepts, and none of its documentation sits outside the login (its docs site exists, and every page of it redirects to a sign-in).
Front's static side has four sources. Its own knowledge base is native, and edits reach the AI within minutes. A public website crawl covers external URLs, with a cap of about 3,000 pages and a manual re-sync (no more than once every 24 hours).
Front cannot crawl sites behind a password. Notion, Google Drive and SharePoint come in as third-party apps. Alongside those sit Notes, which are short facts an admin curates by hand, plus attachment reading (Autopilot and Copilot read PDFs and images a customer sends).
'Dynamic' content
Decagon reads Salesforce Customer 360, live business APIs for real-time order and account data, and Shopify and Stripe. It also supports MCP, a standard way of plugging an AI agent into other software.
Front's dynamic input is its own conversation history, which is always on. There is no equivalent live-systems layer in its documented knowledge sources.
On knowledge, we take a third route with My AskAI. Its knowledge base connects to Google Drive, Notion, OneDrive, Dropbox, SharePoint, Confluence, the Salesforce connector for knowledge pages and Shopify.
It reads the Zendesk, Intercom and Freshdesk knowledge bases including articles behind a login, accepts direct PDF and DOCX uploads, and lets you mark any source internal-only. If you have no help center yet, we can draft starter knowledge from your historic tickets and go from there.
Which has better answer quality, Decagon or Front?
TL;DR: Decagon publishes named-customer results on its case-study index, and Front publishes an up-to-70% claim with no benchmark behind it. Both publish a rule for when a conversation counts as resolved, and Front bills against its rule.
Neither headline number is comparable to the other.
Decagon's headline deflection figure is 80%. That figure is a stat tile on its homepage (it comes from Duolingo). It is one customer's result, and Decagon publishes no base-wide average.
Its case studies name six customers: Chime, Notion, Duolingo, Rippling, Curology and Substack. Only three of them carry a number on that index. Chime is at 70% chat and voice resolution, Duolingo at an 80% deflection rate, and Rippling at a 32% increase in deflection (the other three get a story and no figure).
Front claims Autopilot can resolve up to 70% of requests. That line sits on both its homepage and its AI product page. It has never published a competitive auto-resolution benchmark.
Its customer stories lead on hours saved, response time and ROI instead (very few of them cite Autopilot closing a ticket on its own). Branch Insurance reports 482% ROI, Uber Freight replies to email 50% faster, and Central Storage & Warehouse unified five warehouses onto one team.
Both vendors publish a resolution definition, and those are comparable. Front's sits in the Autopilot pricing article:
"A conversation is resolved if there is no follow-up message from a teammate within 24 hours for Resolve, SMS, WhatsApp, and Custom Channels, or within 72 hours for email and portal." Front, in its Autopilot pricing article
Decagon's own standard is stricter on paper. A contact counts as deflected only when the underlying issue was resolved (a customer who gives up before reaching a human does not count).
Front's definition has a rate attached to it, so a conversation that meets it becomes a $0.89 line on the invoice. Nothing attaches money to Decagon's 80% tile. I ask enterprise vendors to define a resolution and then show me the invoice line it produces.
Decagon publishes more of its own numbers, though, across six named customers. Decagon's weakest category on G2 is Ticket Resolution at 7.9 out of 10, so put that one on the demo agenda.
Is Decagon or Front easier to improve?
TL;DR: Decagon, by a wide margin. It versions every agent change the way a software team versions code, with staging, diff review and rollback. Front's improvement loop is curating topics, refreshing knowledge and trashing facts you do not want reused.
Decagon's agent versioning is the strongest governance either vendor offers. Every procedure, tool and guideline edit is a versioned commit (production and staging workspaces are separate). Changes go through diff review, a bad deploy can be rolled back, and branch protections control who can push what.
Duet Autopilot turns production signals into proposed updates (they validate themselves against a test loop before a human approves them). Watchtower reviews every conversation as an always-on QA layer.
Knowledge Suggestions mines the conversations where the agent could not find an answer. It ranks them by coverage impact and drafts articles from real human resolutions (behind a publish gate). The QA Hub collects the results.
Front's loop is supervised curation, and Front is direct about it:
"Direct discarding does not explicitly “train” the AI, but improvements come from updated knowledge and messages you send." Front, on its Autopilot announcement thread
In the AI replies hub an admin can trash a fact drawn from a past conversation. It then appears struck through and stays out of future replies, reversibly.
Tone matching needs no setup. Topic management lets you merge duplicates and review a similarity score (Front puts topic accuracy at about 85%). Smart QA grades conversations as an indirect quality loop, and Front Academy carries the training material.
Front documents no simulation, sandbox or replay-past-tickets capability anywhere in its help center as of August 2026. A Front admin finds out how the AI behaves by switching it on against live conversations.
Decagon runs end-to-end simulated conversations, unit tests, AI-generated mock customers and regression testing against past transcripts (all of that happens before anything reaches a customer).
Which has more features: Decagon or Front?
TL;DR: Decagon's set goes deep around the agent, including browser actions that log into systems with no API. Front's set spans more of customer operations, with Autopilot the one piece that resolves without a human.
Feature | Decagon | Front |
Autonomous customer-facing resolution | ✅ Chat, voice and email | ✅ Autopilot, on shared inboxes only |
Copilot for human agents | ✅ Assist, hosted in Salesforce, Zendesk and Front | ✅ Copilot, on every Front conversation |
First-party voice agent | ✅ Inbound and outbound calls | ❌ Phone runs through Dialpad, Aircall and RingCentral |
Acting in systems that expose no API | ✅ Browser Actions | — Not documented publicly |
Multi-step workflows written in plain English | ✅ Agent Operating Procedures | ✅ Playbooks |
Open-ended discovery, retention and expansion | ✅ Guided Discovery | — Not documented publicly |
Self-service embedded in your own product | — Not documented publicly | ✅ Autopilot Resolve |
Versioned agent changes with rollback | ✅ Staging, diff review and branch protections | — Not documented publicly |
Simulation and regression testing before launch | ✅ Simulated conversations, unit tests, mock customers | — Not documented in Front's help center |
Auto-drafted knowledge articles | ✅ Knowledge Suggestions | — Not documented publicly |
Open developer API and integration library | — Direct API builds only | ✅ 100+ integrations and an open API |
Two of Decagon's additions change a buying decision. Browser Actions lets the agent log in and click through web systems that offer no way for software to connect to them directly.
That covers internal claims systems, partner portals, vendor dashboards and legacy on-premise tools. No helpdesk-native agent limited to its vendor's connector list can reach those systems.
Guided Discovery handles open-ended discovery, retention and expansion conversations (it replaces one predefined path per intent).
Front's AI suite spans Topics, Copilot, Smart QA, Smart CSAT, Compose, Summarize, Translate, and Autopilot with Playbooks and Resolve. Of those, Playbooks runs multi-step workflows and Resolve embeds self-service in your own product.
Underneath the AI sits the platform (omnichannel shared inboxes, ticketing, live chat, a knowledge base, 100+ integrations and an open developer API). Most of Front's feature count comes from that platform.
How easy is it to customize Decagon and Front?
TL;DR: Decagon lets you write the agent's behavior in plain English and version every change. Front gives you rules, topic-level toggles and editable AI disclosure text, and the model itself is a closed box you cannot correct.
Decagon's Agent Operating Procedures define behavior in plain English and then compile to code. Guidelines, tools, routing rules, escalation paths and model configuration are all customer-configurable.
Threaded comments inside a procedure (an Agent Operating Procedure, in Decagon's own naming) let CX, legal and operations review a change in place before it lands. All of it sits under version control.
Front gives you dials around the agent. You get per-topic auto-reply toggles, the rule Front generates for you, regional AI disclosure text you can edit, Playbooks for multi-step work, and Copilot enablement per shared inbox.
Knowledge is scoped to the workspace (so you cannot give two agents separate source scopes).
When the AI gets a conversation wrong, the fix goes into the knowledge or the topics behind the reply. Front trains nothing, and discarding a reply does not train it either.
What about vendor lock-in?
TL;DR: Front's AI cannot be bought without moving your support onto Front. Decagon's agent needs Zendesk, Salesforce or Intercom underneath it, and it is not in any helpdesk app store.
Front AI cannot be bought on its own (its own prerequisites article says you must already be a paying Front customer). It cannot run as an overlay on Zendesk, Intercom, Freshdesk, Gorgias or HubSpot.
Buying Front's AI is a helpdesk migration, and a finance team should budget it as one.
Decagon's ticketing and customer data run through Salesforce, Intercom or Zendesk (Freshdesk, Gorgias, HubSpot and Front are all absent from that list). A separate helpdesk is required for human handoff, so you are paying two vendors for one support motion.
There is no Zendesk Marketplace, Intercom App Store or Salesforce AppExchange listing either. Every integration is a direct API build your team owns the maintenance of.
Decagon is transactable on AWS Marketplace and on Google Cloud (and it is accredited on Five9 CX). So the absence is specific to the helpdesk app stores buyers install from.
There is a third route on lock-in. Zendesk, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot all take My AskAI as an installed app, so a move between any of those six keeps the agent you already trained.
Do they have any other AI features?
TL;DR: Front has the larger set of secondary AI, with Smart QA scorecards, Smart CSAT prediction, Compose, Summarize and Translate. The last three come with the plan, and the first two are per-seat add-ons that Enterprise includes. Decagon's secondary AI is quality and observability: Watchtower, Trace View and an adoption analytics hub.
Feature | Decagon | Front |
Auto-tagging | ✅ Intent tags | ❌ AI Tagging is retired and no longer sold |
Agent-facing translation | ✅ Real-time chat translation in Assist | ✅ Translate across email, SMS, WhatsApp, Messenger and Front Chat |
Customer-facing multilingual | ✅ 70+ languages on voice | ❌ Autopilot is officially English only |
Automated QA scorecards | ✅ Watchtower reviews every conversation | ✅ Smart QA at $20/seat/mo, included on Enterprise |
CSAT prediction | — Not documented publicly | ✅ Smart CSAT at $10/seat/mo, included on Enterprise |
Conversation summarization | ✅ Automated summaries in Assist | ✅ Summarize |
Voice-of-customer analytics | — Not documented publicly | ✅ Topics |
Full decision observability | ✅ Trace View | — Not documented as a feature |
Tagging
Front's AI Tagging is retired. Its help article is titled "AI Tagging [legacy]" and the feature is no longer available to buy (a team joining Front today does not get it). Decagon's equivalent is intent tags, which classify what the contact was about.
Agent translation
Decagon Assist adds real-time translation on chat and live transcription on voice. Translation is there to stop a language gap escalating a case.
Front's Translate covers email plus SMS, WhatsApp, Messenger, Front Chat and custom channels (it works for the agent writing the reply).
Quality assurance and CSAT
Smart QA grades conversations against AI scorecards at $20 per seat per month. Smart CSAT predicts satisfaction at $10 per seat per month, which is useful when only a small slice of customers ever fill in a survey. Both rates apply on Starter and Professional (the Enterprise plan bundles the pair in).
Topics doubles as customer-feedback analysis. Front also bought Idiomatic in November 2024 (its founder Kevin Yang now leads Front's AI roadmap).
Decagon's secondary set is three tools: Watchtower reviewing every conversation, Trace View for replaying a case and explaining why the agent routed it the way it did, and the adoption analytics hub inside Assist. All three exist to make the agent's work checkable.
Front's secondary AI is not one billing model but three. Topics, Compose, Translate and Summarize come with the plan (each capped at 200 actions per teammate per day).
Copilot, Smart QA and Smart CSAT are per-seat add-ons on Starter and Professional that the Enterprise plan includes, and Autopilot bills per conversation rather than per seat. On G2 the AI praise for Front lands on this half of the product:
"I find the AI very helpful for composing emails, allowing me to sound more professional, friendly, or empathetic." Cari, Customer Service Rep, on Front's G2 page
What about security, is Decagon more secure than Front?
TL;DR: Both hold SOC 2 Type II and ISO 27001, and a reviewer will clear either one. Decagon adds PCI DSS 4.0.1 and a stated EU AI Act position, and Front documents US and EU data hosting.
Decagon runs a formal trust center built on Vanta. It carries SOC 2 Type II reports for two consecutive periods (plus a SIG Lite and an information security overview). Its ISO 27001:2022 certificate was issued on January 22, 2026.
The security page also lists PCI DSS 4.0.1, a stated position on the EU AI Act and GDPR. It adds automatic redaction of personal data before it reaches a model, and zero-day retention with every model provider. Every document sits behind a "Request access" control, so you have to ask for each one.
Front's security page carries the certifications in its own words:
"We’re proud to be SOC 2 Type II and ISO 27001 certified, ensuring that client data is processed in a secure manner." Front, on its security page
Alongside that it publishes a request path for the SOC 2 report and the ISO certificate, plus GDPR readiness with a DPA. Data hosting is regional (either the US or the EU).
The technical controls run to AES-256 encryption at rest, TLS 1.2 in transit, AWS hosting with a per-customer identifier, two-factor authentication and SAML single sign-on. Front also publishes email authentication records and runs a HackerOne bug bounty. Its prerequisites article adds zero data retention on its AI requests (deletion requests are sent automatically after each one).
So a security reviewer gets dated documents behind a request gate from Decagon, and a security page plus a request path from Front. Both are credible, and neither should stall an approval on its own.
Which costs more, Decagon or Front?
TL;DR: Front publishes every number, from $25 to $105 per seat per month plus $0.05, $0.39 or $0.89 per conversation depending on how far the AI takes it. Decagon publishes nothing, and the only public signal is a Vendr median in the low six figures.
The pricing model
Decagon | Front | |
What you pay for | Undisclosed; annual enterprise contract | Seats, plus AI add-ons, plus Autopilot conversations |
Published rate | ❌ None | |
Free trial | ❌ None | ✅ 14 days on the platform, 30 days on Autopilot |
Front bills seats and AI separately. Base plans run $25, $65 and $105 per seat per month on the pricing page.
On top of that sit the add-ons. Copilot is $20 per seat per month, Smart QA is $20 and Smart CSAT is $10.
All three rates apply on Starter and Professional only, because the $105 Enterprise tier bundles the lot in (its plan card reads "AI Copilot, QA, and CSAT included"). Those same rates appear in Front's own in-product billing screen, inside its Copilot help article.
Autopilot then bills per conversation at three automation levels. Triage costs $0.05, a handoff costs $0.39, and a full resolution costs $0.89, all published in Front's own Autopilot pricing article. The same page sets out the billing rule:
"With our outcome-based pricing for Autopilot, you only pay when Autopilot delivers the outcome. Conversations will only be charged once for the highest automation level achieved." Front, in its Autopilot pricing article
The unit is a conversation.
A conversation Autopilot only triages still bills $0.05, even though nothing was resolved. The starting-at figure on the pricing page is that triage floor, so $0.89 is the number to model against a per-resolution competitor (which bills nothing on the triage-only conversations).
Decagon publishes none of this. It has no pricing page at all, no pricing link in its navigation or footer, no self-serve signup and no free trial.
The only public cost signal is Vendr. Its median for a Decagon contract is $432,750 a year (a range of $105,000 to $923,183, on Vendr data as of August 2026). That figure describes what companies signed, with no unit rate underneath it.
A buyer shopping this tier went to r/customerexperience to ask peers for real-world feedback on Decagon, and drew 30 replies. With no rate on Decagon's own site, that thread is where the price conversation happens.
The overall cost
Take a team of 20 agents handling 10,000 conversations a month, with Autopilot touching all of them (500 conversations per agent per month).
On Front's top plan the seats cost 20 × $105, so $2,100 a month. Copilot, Smart QA and Smart CSAT are all included at that tier, so they add nothing to this line.
Now the Autopilot line. Half of those conversations resolved outright is 5,000 × $0.89, or $4,450; 3,000 handoffs at $0.39 is $1,170; the remaining 2,000 triaged at $0.05 add $100. Autopilot comes to $5,720, so the whole bill lands at $2,100 plus $5,720, or $7,820 a month.
There is no way to build Decagon's side of that sum, because it publishes no per-unit rate to multiply.
The only defensible figure is the Vendr median (around $36,000 a month before any negotiation). That number carries a wide range and describes whole contracts. Ask Decagon for a per-unit rate on the first call.
Trials are part of the cost too. Front gives you 14 days on the platform with no card, plus 30 days of Autopilot free up to 1,000 resolutions. Decagon's evaluation runs on demos and a six-week engagement.
We price this a third way. Flat monthly plans start at $199 a month and work out at roughly $0.10 per ticket. The rates are on our pricing page, and the 30-day free trial unlocks every feature, covers unlimited tickets and needs no credit card.
If you want either model in full, our teardowns cover how Decagon prices its contracts and what Front's per-conversation rates add up to.
Conclusion - should I choose Decagon or Front?
TL;DR: Decagon ends at 79 out of 110 against Front's 74. Decagon wins on agent depth, and Front wins on everything you can verify before you sign.
If you need an AI agent running at enterprise volume, in systems that expose no API, Decagon is the choice, and a six-week engagement is what it costs to get there. If you want to see the rate and run a trial first, take Front, and budget for moving your support onto it (Front's AI is not sold standalone).
The 11 categories are modes, ease of setup, training and integrations, answer quality, improving, features, price, customization, lock-in, other AI features and security. Evidence comes from each vendor's own documentation and pricing pages, dated product-update posts, published customer case studies, G2 reviews and procurement data on Vendr.
Decagon | Front | ||
Modes | 8/10 | 8/10 | Tie — Front leads on written-channel breadth, and Decagon has first-party voice plus a copilot hosted in three systems |
Ease of setup | 4/10 | 9/10 | Front — a 14-day no-card trial and a 30-day Autopilot trial against no trial and a six-week engagement |
Training/Integrations | 9/10 | 6/10 | Decagon — one knowledge graph, live business APIs, CRM records, custom APIs and MCP against a 3,000-page crawl cap and no file upload |
Answer quality | 8/10 | 6/10 | Decagon — published figures from named customers against an "up to 70%" claim with no published benchmark |
Improving | 9/10 | 5/10 | Decagon — versioned agent changes with staging, diff review and rollback against supervised curation and no documented sandbox |
Features | 9/10 | 7/10 | Decagon — deeper agent capability, including browser actions into systems with no API |
Price | 3/10 | 8/10 | Front — every rate published and computable against no published price and a $432,750 median |
Customization | 9/10 | 5/10 | Decagon — behavior written in plain English and versioned against topic toggles over a model you cannot correct |
Lock-in | 6/10 | 4/10 | Decagon — narrower, at three helpdesks underneath, against a full migration onto Front |
Other AI features | 6/10 | 8/10 | Front — Smart QA, Smart CSAT, Compose, Summarize, Translate and Topics analytics |
Security | 8/10 | 8/10 | Tie — both hold SOC 2 Type II and ISO 27001. Decagon adds PCI DSS and an EU AI Act position, and Front's certificates are request-gated on its own security page |
Total | 79/110 | 74/110 | Decagon takes six, Front takes three, two tied |
Decagon is the stronger agent. That covers what it learns from, how it improves, what it can be made to do, and how much of its behavior you control.
Front gives you more to check before you sign: the rates, the trial, and what the secondary AI gives everyone on the team.
I put more weight on the second. A price you cannot compute blocks the purchase before the product is ever tested.
For a team that has shipped a bad deploy in front of customers, Decagon's staging, diff review and rollback are the reason to sign. An unquotable annual contract stops the deal in any approval process that needs a number before it moves.
Choose Decagon if:
- You run enterprise conversation volume and the agent has to act in systems that expose no API
- You already run Zendesk, Salesforce or Intercom and are adding an AI layer on top of the desk you keep
- You need governance over agent changes, with staging, diff review and rollback, because a bad deploy is a real risk
- You need voice, including outbound calls in 70+ languages
Choose Front if:
- You want to see the price, run a trial and read the documented limits before you commit
- Your team lives in email and shared inboxes, and you were weighing up a platform move anyway
- Your support runs in English
- You want QA scorecards, CSAT prediction and summarization in the same tool as the inbox
My AskAI is the third route. It charges a flat rate per ticket, with the price on the page and a 30-day trial, and it works in Zendesk, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot.
If you want either vendor's wider field, we have an alternatives roundup for Decagon and an alternatives roundup for Front on the blog. We also compare My AskAI against Decagon and against Front directly. There is a Zendesk AI against Intercom Fin comparison too, if your shortlist runs through the native helpdesk agents instead.
Good luck with your evaluation.
FAQs
Can Decagon replace Front, or can Front's AI replace Decagon?
Neither one replaces the other cleanly, because they are different purchases. Decagon Assist, its copilot, activates inside Front for your human agents, while its integration list puts the autonomous customer-facing agent on Salesforce, Intercom and Zendesk only. Front's AI cannot be deployed at all without moving your support onto Front, so if you want an agent that stays with you across desks, our own agent, My AskAI, installs into Zendesk, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot.
What can Decagon and Front AI each be trained on?
Decagon builds one knowledge graph across help-center articles synced from Zendesk, Kustomer, Guru, Confluence and Contentful, plus product docs, past transcripts, Salesforce Customer 360, internal business APIs, Shopify and Stripe, and custom databases. Front reads four primary sources: its own knowledge base, a public website crawl capped at about 3,000 pages (manual re-sync, once a day at most), third-party apps covering Notion, Google Drive and SharePoint, and your historical conversations. Front also lets admins add short curated notes, and Autopilot and Copilot read PDF and image attachments in a conversation.
How long does setup take, and do we need developers?
Front is a no-code admin job: topic generation takes one to two hours over your last 30 days of conversations, and everything after that is toggles, a knowledge source and a rule. Front's prerequisites article adds two conditions (you must already be a paying Front customer, and only company or workspace admins can enable it). Decagon runs about six weeks from discovery to deployment with embedded staff, and its advanced integrations are direct API builds, so developer time is real on that side.
How do Decagon and Front AI pricing models differ, and what might we actually pay?
Front's rates are all published, and the only public number for Decagon is a third-party contract median.
Decagon | Front | |
Pricing model | Undisclosed annual enterprise contract | Per seat, plus AI add-ons, plus per-conversation Autopilot |
Published rate | ❌ None | |
20 agents, 10,000 conversations | Not computable; only a Vendr contract median exists | About $7,820/mo at 50% resolution |
Free trial | ❌ None | ✅ 14 days platform, 30 days Autopilot up to 1,000 resolutions |
Front's worked example above is 20 seats at $105 ($2,100), with Copilot, Smart QA and Smart CSAT included at that tier, plus Autopilot at 5,000 resolutions, 3,000 handoffs and 2,000 triages ($5,720). Every one of those rates is published in Front's Autopilot pricing article and on its pricing page. Decagon's column cannot be filled in, because there is no published per-unit rate to multiply and the Vendr figure describes whole contracts.
Can Decagon and Front handle multilingual support and agent translation?
Front's own Autopilot FAQ states: "Only English is officially supported at this time. While it is possible to use this feature with other languages, unexpected results may occur." Decagon covers 70+ languages, serves every language from one knowledge source, and adds real-time chat translation (inside Assist).
Does either one handle phone support?
Only Decagon, natively. Its first-party voice agent takes inbound calls and makes outbound ones, covers 70+ languages, uses branded caller IDs, and answers in well under half a second. Front has no voice agent of its own and reaches phone through its Dialpad, Aircall and RingCentral integrations instead.
How do Decagon and Front compare on security and compliance?
Both hold SOC 2 Type II and ISO 27001, so most reviewers will clear either one. Decagon's security page adds PCI DSS 4.0.1, a stated EU AI Act position, data redaction before a model sees it, and zero-day model-provider retention, all behind a request control. Front's security page adds US or EU data hosting, AES-256 at rest, TLS 1.2 in transit, SAML single sign-on and a HackerOne bug bounty, and its prerequisites article adds zero data retention on AI requests.
Will I be locked in to one helpdesk?
With Front, yes, by design: its AI cannot be bought standalone, so adopting it means running your support on Front. With Decagon, partly, because the agent needs Salesforce, Intercom or Zendesk underneath it for handoff, has no helpdesk app-store listing, and every integration is a direct API build you maintain. If portability across desks is the requirement, My AskAI keeps one trained agent across six helpdesks: Zendesk, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot.
