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 Sierra AI at a glance
- How are Decagon vs Sierra different from the AI bots they replaced?
- How does an AI agent work when it isn't part of your helpdesk?
- How can I run Decagon or Sierra alongside the helpdesk I already have?
- Direct replies
- Copilot replies
- Voice
- Where each one plugs in
- Which is easier to set up: Decagon or Sierra?
- How do Decagon and Sierra differ in what they can be trained on?
- 'Static' content
- 'Dynamic' content
- Which has better answer quality, Decagon or Sierra?
- Is Decagon or Sierra easier to improve after launch?
- Which has more features: Decagon or Sierra?
- How easy is it to customize Decagon and Sierra?
- What about vendor lock-in?
- Do Decagon and Sierra have any other AI features?
- Tagging
- Agent translation
- Help center quick answers
- What about security, is Decagon more secure than Sierra?
- Which costs more, Decagon or Sierra?
- The pricing model
- The overall cost
- Conclusion - should I choose Decagon or Sierra?
- FAQs
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Decagon and Sierra are both enterprise AI agent platforms, and neither one is a helpdesk, so you keep paying for the desk underneath either way. Decagon says most of its customers choose per-conversation pricing, so you pay for the attempt, and it syncs support tickets with Zendesk, Salesforce and Intercom while pulling customer data from them. Sierra charges for the outcome and claims 40+ pre-built integrations without naming a single helpdesk publicly. Choose Decagon if you run one of the three desks it syncs with and want your own team operating the agent. Choose Sierra if voice, WhatsApp and a ChatGPT surface are what you need and you can sign without a published connector list.
You have both of these names on the same shortlist, and neither vendor publishes a rate.
Start with the best numbers anyone has. Vendr's observed contracts put Decagon's median at $432,750 a year. Sierra's quoted $200,000 to $350,000+ year one is a competitor and vendor blog estimate with no primary behind it.
I co-founded My AskAI, and we sell an AI agent in this market. Neither product below is ours.
We help 200+ ecommerce and SaaS businesses run AI customer service inside the helpdesk they already have, and our agents have resolved over 1,000,000 tickets.
Decagon AI vs Sierra AI at a glance
Decagon | Sierra | |
What it is | Enterprise AI agent platform, not a helpdesk | Enterprise AI agent platform, not a helpdesk |
Headline price | Vendr median $432,750/yr, observed contracts | Est. $200K-$350K+ year one, competitor blogs |
How you buy it | Sales-gated demo, no self-serve signup | Sales-gated form, no self-serve signup |
Best for | Teams already on Zendesk, Salesforce or Intercom | Voice-led, multi-channel enterprise deployments |
Named helpdesk connectors | Zendesk, Intercom, Salesforce; none named for Freshdesk, Gorgias, Front, HubSpot | None named publicly, 40+ claimed without a directory |
Watch out for | Chat, voice and email have product pages; no ChatGPT surface | Names no helpdesk vendor publicly |
Scorecard | 5 of 11, 1 tied | 5 of 11, 1 tied |
How are Decagon vs Sierra different from the AI bots they replaced?
TL;DR: Both replaced decision-tree bots with agents that take real actions in live systems. Decagon came out of stealth in 2024 and was valued at $4.5 billion in January 2026. Sierra launched the same year, and a $950 million round in May 2026 pushed it above $15 billion.
The old bots matched a question to a canned answer. Both of these agents look up the order, issue the refund and verify the identity. That is the shared story.
Decagon was founded in 2023 in San Francisco by Jesse Zhang and Ashwin Sreenivas, and came out of stealth in June 2024. Its core idea is the Agent Operating Procedure, a workflow written in plain language. Decagon's own words for it: "Combine the flexibility of natural language with the reliability of code to execute any workflow."
For the money I go to Sacra's profile of the company, which puts the total raised at around $481 million across five rounds. The latest was a $250 million Series D in January 2026 at a $4.5 billion valuation, led by Coatue and Index. The company is past 300 staff across San Francisco, New York and London.
Sierra was incorporated in February 2023 and launched in February 2024. The founders are Bret Taylor, who co-ran Salesforce and chairs the OpenAI board, and Clay Bavor, who spent 18 years at Google (a pedigree that turns up again in the G2 praise below). Its product is Agent OS.
Sierra has now raised over $1.5 billion (roughly three times Decagon's total). A $950 million round led by Tiger Global and GV closed in May 2026. TechCrunch reported it as "pushing its post-money valuation above $15 billion."
Sierra reported $100 million of ARR in late November 2025 and $150 million by early February 2026, both carried in the same TechCrunch report, and says more than 40% of the Fortune 50 are customers.
So Sierra is the bigger company. Decagon is at $4.5 billion, Sierra above $15 billion, and the funding gap runs the same way. Upstarts Media's piece on the race between them is the only editorial coverage most buyers find, though I now ignore its valuation figures, because the rounds above have overtaken them.
One commenter on Blind, which is a candidate and employee forum, framed it this way:
"Today, Sierra is the market leader in this category, but I believe Decagon is closing the gap very quickly."
Take that with a grain of salt. One person on a forum is not market data.
Both review well on G2:
Decagon | Sierra | |
Rating | 4.9 / 5 | 4.3 / 5 |
Reviews | 18 | 13 |
Reviews page |
Both samples are tiny, and Sierra's profile is unclaimed, with two or three of its 13 reviews written about an unrelated library system that shares the name. Decagon's 18 are all about the AI product, so I weight them a little higher.
The praise on each side is consistent. On Decagon, the review that stuck with me is from one manager: "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." On Sierra: "What I like best about Sierra is its strong focus on safe, supervised AI agents that can take real business actions while protecting brand integrity, and the founding team pedigree."
We have full guides to Decagon and to Sierra elsewhere on the blog if you want either on its own terms first.
How does an AI agent work when it isn't part of your helpdesk?
TL;DR: Both are layers above the helpdesk you keep running. They read your knowledge and your systems, answer the customer, then hand the conversation to a human in the tool you already have.
Both products answer the customer and then hand off. The queue your agents work in stays in your helpdesk.
Decagon runs one intelligence layer across its channels. Its homepage puts it plainly:
"Decagon unifies chat, voice, and email within a single intelligence layer, ensuring customer experiences stay consistent across every channel."
A conversation that starts in chat can carry on in voice, because the memory is attached to the customer and travels with them.
Underneath that sits the Unified Knowledge Graph. It ingests every source you connect, works out how the pieces relate, and serves one answer set to every channel and language (test that by editing one article and asking the voice agent about it).
Sierra's Agent OS connects to your CRM, order management, subscription platforms and data warehouses through its Agent SDK and Integration Library. Its persistent-memory layer launched in November 2025 and is now called Horizon, with a Context Engine alongside it. When a conversation needs a person, Sierra generates a summary and routes it to the right team.
The consequence is the same on both sides. You are running two systems: the agent layer, and whatever holds the human conversation. I have watched plenty of teams budget for only one of them.
In this market, lock-in is mostly a restatement of that same two-system problem.
How can I run Decagon or Sierra alongside the helpdesk I already have?
TL;DR: Decagon syncs support tickets with Zendesk, Salesforce and Intercom and pulls customer data from them, and can take actions in two of them. Sierra claims 40+ pre-built integrations and names no helpdesk publicly, so you cannot check its coverage before you sign.
Direct replies
Chat, voice and email are the channels Decagon puts a product page and a nav entry behind.
SMS runs too. Decagon's own blog posts describe it carrying context alongside chat and voice, and it appears in `llms.txt`, though it has no product page of its own.
Sierra runs more. Its homepage lists chat, SMS, WhatsApp, email, voice and ChatGPT, and it adds Apple Business Chat (seven channels in total). It cannot switch channel mid-conversation, so a chat that should become a call starts again.
Neither vendor lists Messenger, Instagram DM or X as a channel (worth checking, if social is where your customers actually turn up).
Copilot replies
Decagon's copilot is Agent Assist. It drafts responses, handles the mundane parts of the agent's job and learns from how your best people answer.
G2 reviewers report that it only runs in Zendesk. Their words: "Users are frustrated by access restrictions limiting 'Agent Assist' to Zendesk, affecting broader usability across departments." Decagon's own site states no such restriction anywhere, so this stands on the reviewers' word alone (get it in writing). Decagon has also moved most of its positioning to Duet.
Sierra's copilot is Live Assist. It gives real-time guidance, drafts replies and offers one-click actions for human reps, inside Sierra's own window. Agent Assist writes into the helpdesk your team already has open.
Voice
Both do voice, inbound and outbound, and voice is the hardest of the three channels to judge from a demo.
Decagon's voice product covers sub-second latency, interruption handling, branded caller IDs and configurable tone and speed, and connects to Amazon Connect, RingCentral and SIP trunking (check your telephony is one of those). Its voice relaunch post is dated September 2025, and outbound calling arrived in spring 2026. Its own voice page describes the outbound tile as "Unlock proactive engagement by initiating outbound calls for sales, support, and customer updates."
Sierra does inbound and outbound too, integrates with IVR, and adds Voice Sims for testing agents against background noise and interruptions before launch. Voice Personas landed in August 2026. The documented criticism is latency: routing across several models adds delay, and a few hundred milliseconds is audible on a phone call.
Ask for a live call on your own numbers before you sign.
Where each one plugs in
Decagon syncs support tickets with Zendesk, Salesforce and Intercom, and pulls customer data from them. It takes actions through Zendesk Sunshine and Salesforce only.
Kustomer, Confluence and Contentful are knowledge syncs, and they do not carry tickets. Freshdesk, Gorgias, Gladly, Front and HubSpot are absent from Decagon's integrations page.
Those integrations are direct API work. There is no Decagon app in the Zendesk Marketplace, the Intercom App Store or Salesforce AppExchange, so I plan that connection as engineering work. Decagon is on the AWS Marketplace and accredited on the Five9 CX Marketplace, with Wealthsimple, Chime and Oura named as joint customers.
Sierra states a count and leaves the connectors unnamed, so a procurement team has nothing to audit. Its Agent Studio page says: "Leverage 40+ pre-built integrations to instantly connect your agent with third-party knowledge bases, systems of record, and contact centers." It also offers a framework for building custom ones.
No helpdesk is named anywhere on the site. Zendesk, Salesforce, Intercom, Kustomer, Freshdesk, Gladly, ServiceNow, Genesys and Five9 appear on none of Sierra's product, industry, customer or home pages.
There is no connector directory, no `/integrations` page in its 636-URL sitemap, and no app in any helpdesk marketplace (Zendesk's, Intercom's or Salesforce's). So the accurate wording is narrower: you cannot verify the coverage before you sign. The integrations may all be there; you just have to take them on trust.
Which is easier to set up: Decagon or Sierra?
TL;DR: Decagon documents roughly six weeks to full deployment and runs a training program for customers. Sierra runs a 90-day onboarding as standard. Neither has self-serve signup, and both put a team on your account.
Decagon's onboarding is a six-phase engagement. Pre-kickoff audit, kickoff and success metrics, then two parallel tracks: content people turn your SOPs into AOPs while engineers wire up CRM access, auth and APIs. Then configuration, internal testing, and a gradual A/B'd launch.
Decagon puts Agent Product Managers and Forward-Deployed Engineers on your account for it (that 300-plus headcount from earlier, turning up in your kickoff calls). Its own blog documents about six weeks from discovery to full deployment, and Ashwin Sreenivas told OpenAI that core infrastructure can be up and running in days. One G2 reviewer reports implementation in under a week.
Existing customers also get Decagon University: a self-paced program with sandbox environments, live training and a CX Architect certification. I read that as a fair signal of who Decagon sells to.
Sierra's route is sales-led and CSM-guided: demo, discovery, scoped pilot, then a 90-day onboarding with a dedicated channel and personalized training. You build in Agent Studio if you want no-code, or the Agent SDK if you want engineers.
Sierra's published go-lives are faster than 90 days in places. Vivid Seats went live in four weeks, Next in six, Singtel in under ten. Those are Sierra's own picks, so I read them as best cases.
Nordstrom went from 1% to 100% of calls in a week and a half after launch, on the same customer stories page.
Its G2 themes are less comfortable, flagging a steep learning curve and difficult configuration. Ghostwriter, launched on 25 March 2026, is the answer to that: upload SOPs, transcripts, audio or a photo of a whiteboard, and it builds a working agent. Real-world feedback on it is not in yet.
Neither has self-serve signup, so the first step on either side is a call.
My AskAI installs into the helpdesk you already run, across Zendesk Support and Messaging, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot, and we go live the same day.
How do Decagon and Sierra differ in what they can be trained on?
TL;DR: Decagon publishes a named list of knowledge sources and helpdesk syncs. Sierra names the types of content it reads but not the connectors, so the list comes out on a sales call.
Source type | Decagon | Sierra |
Help center articles | ✅ Zendesk, Kustomer, Guru, Confluence, Contentful | ✅ connector not named |
Product documentation | ✅ | ✅ |
Past tickets and call transcripts | ✅ historical transcripts | ✅ transcripts and audio recordings |
SOPs and policies | ✅ converted into AOPs at onboarding | ✅ including whiteboard photos via Ghostwriter |
CRM records | ✅ Salesforce Customer 360 | ✅ via Horizon |
Order management and subscriptions | ✅ Shopify, Stripe | ✅ order and subscription platforms |
Data warehouse | — not named | ✅ via Horizon |
Confluence, Notion, Drive, SharePoint | ✅ Confluence, Contentful, Guru, Slack | — not publicly listed |
Custom and internal APIs | ✅ internal APIs, custom databases, MCP | ✅ Agent SDK and Integration Library |
Auto-drafted articles | ✅ Knowledge Suggestions, monthly | ✅ Expert Answers, from January 2026 |
Supported file types | — not publicly documented | — not publicly documented |
Sync frequency | — not documented | — not specified |
'Static' content
Both read the written stuff: help center articles, product docs, policies, SOPs.
Decagon names where it reads them from. Zendesk, Kustomer, Guru, Confluence and Contentful are on the page. Sierra's page stops at the content types: FAQs, policies and SOPs.
Ask for the connector list on the first call; it is the first thing I ask any vendor.
Decagon also drafts new articles for you. Knowledge Suggestions ranks the questions the agent could not answer by how much traffic they affect, then writes candidate articles from how your human agents actually solved them. It runs monthly, and you approve what goes live.
'Dynamic' content
This is CRM records, order state, live API calls and transcripts.
Decagon reads Salesforce Customer 360, Shopify, Stripe, internal business APIs and custom databases, and supports MCP (the open connector standard). Sierra reads CRM, order management, subscriptions and warehouses through Horizon, and anything else through the Agent SDK.
Decagon names the systems on the other end. Sierra gets as far as Horizon and the Agent SDK, and the rest comes out on a sales call.
Neither vendor documents its supported file types or its sync frequency, and neither lets you check. Decagon's docs site redirects every path to a login. Sierra's goes further and makes it contractual: the terms on the docs wall read "All Sierra Products are confidential, and you will not use or disclose them other than to evaluate the Sierra Products."
My AskAI publishes its connector list openly, and you can connect them yourself without getting on a call with us.
Which has better answer quality, Decagon or Sierra?
TL;DR: Both publish strong numbers that measure different things. Decagon's headline deflection figure is 80%, from a homepage stat tile and Duolingo's own result. Sierra's case studies run from 64% to 94% resolution, with most landing between 65% and 77%.
Start with what the two numbers count, because they do not count the same event.
Decagon leads on deflection, and its own definition is the stricter of the two. From its glossary:
"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."
Its headline deflection figure is 80%, which comes from a stat tile on the homepage and separately from Duolingo's own result. It is not published as a cross-customer average, so I do not read it as one. Decagon also publishes a 93% agent quality score.
The per-customer figures are where I look. Substack over 90% resolution, Flashfood 90%+, Duolingo 80% deflection, Chime 70%+ across chat and voice, Fourthwall 70% deflection, NG.CASH 70% up from 13%, Valon 50%+ on voice, Rippling 38% to over 50%. All of them are Decagon's own case studies, so they are the wins the vendor chose to publish.
Sierra publishes per-customer resolution rates instead. Funnel Leasing 94%, Ramp 90%, Airtable 80%, Wilson 77%, Pendulum over 75%, Casper 74%, ScottsMiracle-Gro 65%, AOL 64%.
The 64% to 94% band is an envelope we derived from those case studies. Sierra does not publish it as a range.
Now the field. Our own benchmark corpus of 195 rated deployments across 38 vendors puts the median AI-handling rate at 70%, with a P75 of 80%. Split by metric, resolution runs a median of 72.5% across 108 deployments and deflection a median of 70% across 17, with a P75 of 82.5%.
Read each headline against its own metric and neither is extraordinary. Decagon's 80% deflection sits between the deflection median and its upper quartile. Sierra's cluster of 65% to 77% resolution sits right on the resolution median.
We publish three caveats with that corpus on the research page. It works as an aggregate only, and the metrics are not apples-to-apples, because every vendor defines its own.
Published figures are also self-selected wins, so the true field average sits below the median.
Independent corroboration is thin on both sides. Decagon's G2 Ticket Resolution score of 7.9 out of 10 is its lowest category.
Neither vendor has a presence on Capterra.
Decagon is the vendor arguing against its own headline metric (deflection, the number on its own homepage). From its own blog: "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."
That is the right instinct, and it is why buyers keep asking strangers. A thread on r/customerexperience titled Need honest feedback on Decagon / Sierra / Fin AI is one place they end up, and Decagon vs Sierra AI on Blind is another.
Is Decagon or Sierra easier to improve after launch?
TL;DR: Decagon hands the controls to your team, with AOPs, versioning, regression testing and auto-drafted articles. With Sierra, several reviewers report that changing the agent's logic means going back to Sierra.
Decagon's improvement stack is the deeper of the two on paper. You refine AOPs in plain language, with a copilot that turns rough notes into workflows. Every edit is tracked as a versioned commit, with staging and production workspaces, diff review, rollback, audit logs and release gates.
Around that sit Simulations against AI-generated mock personas, unit tests, regression tests over your own historical transcripts, and Experiments that A/B changes on live traffic against CSAT, deflection or resolution. Watchtower scores conversations continuously against your rubric, QA Hub gives your people a place to review them, and Duet Autopilot proposes improvements for a human to approve (put a named owner on that approval queue before launch).
The counterweight comes from Decagon's own reviewers: "Decagon is still a new product, and lacks maturity in some of its features. For instance, regression testing only recently became available, and they are still building out guardrails that are necessary for the long-term quality of our chatbots."
Sierra's tooling is comparable on the list. Explorer researches your conversations, Experiments runs multivariate tests, Monitors surfaces sentiment anomalies, Expert Answers drafts articles, Voice Sims tests calls, and Workspaces gives GitHub-style snapshots with instant rollback.
A year in, what you feel is who holds the tools. Sierra's G2 reviewers report that the platform does not allow client customization, which leaves Sierra's team holding the edits.
Agent Studio 2.0 in November 2025 and Ghostwriter in March 2026 both aim squarely at that criticism. There is not yet enough real-world evidence to say whether they have moved it.
Which has more features: Decagon or Sierra?
TL;DR: Sierra has the broader platform, with seven channels, a developer SDK, persistent memory and a ChatGPT surface. Decagon's set is narrower and organized around AOPs and the operating layer that surrounds them.
Feature | Decagon | Sierra |
Channels | ✅ chat, voice, email, SMS (SMS has no product page) | ✅ chat, voice, email, SMS, WhatsApp, ChatGPT, Apple Business Chat |
No-code agent builder | ✅ AOPs and AOP Copilot | ✅ Agent Studio |
Developer SDK | — AOPs compile into code, docs gated | ✅ Agent SDK with CI/CD and multi-agent orchestration |
Persistent memory | ✅ user and cross-channel memory | ✅ Horizon, plus Context Engine |
Human copilot | ✅ Agent Assist, reported Zendesk-only | ✅ Live Assist, inside Sierra |
Always-on QA | ✅ Watchtower and QA Hub | ✅ Monitors and Observability |
A/B testing on live traffic | ✅ Experiments | ✅ Experiments |
Versioning and rollback | ✅ commit-level | ✅ snapshot-level Workspaces |
Auto-drafted articles | ✅ Knowledge Suggestions | ✅ Expert Answers |
Self-improving loop | ✅ Duet Autopilot, with approval gate | ✅ Ghostwriter builds and optimizes agents |
Acting in systems with no API | ✅ Browser Actions | Not documented |
Rich in-chat objects | Not documented | ✅ Visual Attachments |
Contact center and telephony | ✅ Amazon Connect, RingCentral, SIP, Five9 | ✅ IVR and contact-center handoff |
Cloud marketplace | ✅ AWS Marketplace | Not documented |
Secure agent sandboxes | ✅ containerized Browser Actions sandbox | ✅ Agency |
MCP | ✅ supported as a knowledge route | ✅ MCP Gateway |
Sierra's breadth claim rests on reach, and I think it holds. Seven channels, 59 languages, an SDK your engineers can own, and an agent your customers can reach inside ChatGPT.
It has also shipped fast this summer. Horizon in July, described by Sierra as its most significant expansion since launch.
MCP Gateway followed a week later, then Agency secure sandboxes, Context Engine and Voice Personas (four more in the weeks after Horizon). Sierra also acquired Takeoff in July, and announced partnerships with SoftBank Corp and Plaid.
Decagon's set goes deep around the agent. Versioning, QA, autopilot, and the one thing on this table Sierra does not document at all.
That is Browser Actions, announced on 5 August 2026:
"Today, we're introducing Browser Actions, a new capability that lets your Decagon agent access and complete tasks inside systems where there isn't a traditional integration available."
It runs, in Decagon's words, "in a secure, containerized, and fully audited sandbox".
If you have a legacy claims system or a partner portal with no API, that is the feature to shortlist on (nothing else here replaces it).
How easy is it to customize Decagon and Sierra?
TL;DR: Decagon's AOPs let non-technical teams write workflows in plain language, though advanced work still needs developers. Sierra is more vendor-dependent: its SDK gives engineers real control, and a team without engineers waits on Sierra.
Decagon's bet is that your CX lead should be able to write the workflow. AOPs are written in plain language, and the AOP Copilot turns a rough SOP into a production-ready one in seconds. In seconds is Decagon's claim, and I have not timed it myself.
Reviewers back the outcome. One senior CX program manager, a year into production, wrote: "One of the key advantages is its ability to create deterministic workflows, reducing risk while ensuring consistent and accurate responses."
There are limits. Advanced customization still needs engineers, and Decagon's admin side is thin: user roles are basic and the audit logs are shallow enough that reviewers flag them when tracing activity.
The Agent SDK lets developers tune creativity against determinism per workflow, which is real control if you have the team (and dead weight if you do not). Without engineers, the recurring complaint is dependency, and the G2 dislike is blunt: "Does not allow for client customization as competitors."
Agent Studio 2.0 and Ghostwriter are the response. Whether they close it is a question for buyers who have run Sierra since March.
What about vendor lock-in?
TL;DR: Neither one replaces your helpdesk, so you pay for both. Decagon syncs with three helpdesks and acts inside two of them. Sierra claims 40+ pre-built integrations, names no helpdesk publicly, and still splits your conversation data across two systems.
Start with the bill you keep paying. Decagon has no standalone helpdesk and needs Zendesk or Salesforce for human handoff.
Sierra sits above your systems and relies on them for ticketing and the agent inbox. Whichever you sign, the desk underneath stays on the invoice (Zendesk, Salesforce or whatever you run today).
Then ask what happens if the desk changes. Decagon's ticket and customer-data syncs cover Zendesk, Salesforce and Intercom, and actions run through Zendesk Sunshine and Salesforce only.
Move to Freshdesk, Gorgias, Front, Gladly or HubSpot and Decagon names no connector to move to, though its integrations page advertises MCP and a self-serve API for connecting any system or custom endpoint it does not list. Reviewers also report that Agent Assist itself only runs in Zendesk, which would narrow it further, though Decagon's own site states no such restriction.
The 40+ pre-built integrations are a claim you cannot check: no connector directory, no `/integrations` page in a 636-URL sitemap, no marketplace app, and no helpdesk named anywhere on the site. Live Assist (Sierra's copilot) now reaches human-handled conversations, which narrows the old split, but the bot side and the human side still live in different places.
Both sell multi-year enterprise agreements on annual terms. Vendr records Decagon's payment terms as Monthly, Net 30 or Net 60 with annual contracting, and Sacra describes Sierra's implementation and optimization services as bundled into multi-year deals. Get the exit terms drafted before anyone gets excited about the pilot.
And you make that decision blind, which is the part I find hardest to defend for either of them. Decagon's public knowledge surface is a 242-URL glossary and an `llms.txt`; its documentation redirects every path to a login.
Sierra's documentation is behind a credential wall with a confidentiality clause attached, and it publishes no `llms.txt` at all.
There is a third route. We built My AskAI to run natively inside Zendesk Support and Messaging, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot, so switching helpdesk means reconnecting the agent, and the AI follows you.
Do Decagon and Sierra have any other AI features?
TL;DR: Both handle multilingual support from a single knowledge base. Sierra covers 59 languages and adds a ChatGPT channel. Decagon publishes 70+ languages with automatic detection, and its extras cluster around knowledge.
Tagging
Sierra ships tagging inside Insights. Its Insights page promises "automated conversation tagging and categorization", so the labels are produced by the analytics layer itself.
Decagon sells no named tagging product, and its evidence sits in the deployments instead. Rippling worked with Decagon's engineers to "identify 75+ tags, apply the appropriate routing, and identify scenarios where escalation was required", and the case study records "an immediate 7% improvement in customer conversation routing". Substack tags historical conversations to track contact drivers and escalate feature requests.
Sierra documents tagging as a feature you can read about before you sign. On Decagon's side the published proof is a taxonomy built alongside the vendor's engineering team, which I count as setup effort on your side.
Agent translation
Decagon publishes support for "70+ languages with automatic detection and language switching." One English policy update takes effect everywhere, because a single knowledge source serves every language. Rituals Cosmetics runs 15 languages on one deployment.
Sierra publishes 59, and describes them as "tuned across 59 languages". Each one is evaluated end to end for accuracy, latency, rhythm and tone, with native speakers vetting the result (Sierra's own account), and brands can have custom voices per region. Sierra's agents can also switch language mid-conversation.
Casper's VP of Operations, Marc Butakis, put the practical version of it in Sierra's case study:
"With the AI agent, we effectively have 24/7 availability and engage in any language—something we couldn't do before."
Neither vendor publishes per-language quality benchmarks, so you are testing this yourself in a pilot.
Help center quick answers
Both turn conversations back into knowledge.
Decagon's Knowledge Suggestions drafts articles monthly from the questions the agent could not answer, grouped by topic, with a publishing control so nothing goes live without approval. Sierra's Expert Answers, from January 2026, generates grounded articles from resolved conversations and from what your contact-center experts already know.
Sierra publishes an agent into ChatGPT with one click, and supports Visual Attachments like maps, forms and charts in chat (the ChatGPT surface is the one I get asked about most). Decagon lets you query your own conversation data in plain language, and gives you a trace of what the agent did and why.
What about security, is Decagon more secure than Sierra?
TL;DR: Sierra holds the longer list, ten certifications including the AI-specific ISO 42001 and FedRAMP High, and it publishes its subprocessors, residency and pen-test cadence. Decagon's edge is narrower: it names its encryption standards and its zero-day retention policy, where Sierra states the practice without the algorithm or the window.
Decagon | Sierra | |
SOC 2 | ✅ Type II, two reports on file | ✅ badge reads SOC 2; trust center names a Type II report (2026) |
ISO 27001 | ✅ 27001:2022, certificate published | ✅ |
ISO 42001 | ❌ confirmed absent | ✅ |
HIPAA | ✅ BAAs on enterprise | ✅ |
GDPR | ✅ | ✅ |
CCPA | ✅ | ✅ |
CSA STAR | — not listed | ✅ Level One, which is self-assessment |
PCI DSS | ✅ 4.0.1 | ✅ 4.0.1 |
EU AI Act | ✅ transparency statement published | ✅ |
FedRAMP | — not listed | ✅ High, June 2026 |
Encryption at rest | ✅ AES-256 named | — practice stated, algorithm not named |
Encryption in transit | ✅ TLS 1.2+ named | — practice stated, version not named |
Zero-day LLM retention | ✅ published, all providers | — procedures published, no window |
Subprocessor list | ✅ four named | ✅ 14 named |
Pen-test cadence | ✅ report on file, access-gated | ✅ at least annual, with remediation SLAs |
Data residency | — gated | ✅ published per subprocessor |
The verdict here is a split.
Sierra holds more certifications: ten, including ISO 42001 and FedRAMP High, certified in June 2026 with Knox Systems. Its CSA STAR entry is Level One, which is self-assessed, so I read it as a lower grade of evidence than the rest of the column.
Decagon publishes less breadth and more detail. Its security page names the standards:
"Data is encrypted automatically at every stage: AES-256 for storage and backups, and TLS 1.2+ for all network transmission."
And on model providers:
"Decagon enforces zero-day retention with all AI providers like OpenAI and Anthropic, ensuring no conversation data is stored or used for training."
Sierra states that it encrypts data at rest and in transit without naming an algorithm, a TLS version or a retention window.
Sierra publishes more of the operational detail Decagon keeps back: 14 subprocessors, data residency per subprocessor, and a pen-test cadence of at least annually with an SLA-bound remediation plan (the part a security reviewer reads first). Decagon's remaining weakness shows up in the admin panel. Reviewers report basic user roles and audit logs that lack depth when you need to trace who did what, so I check the role model against the approval chain you already run.
In December 2025 a coordinated bad actor tried to jailbreak more than a dozen Sierra customer agents, and Gap's agent responded to off-scope topics because a guardrail was misconfigured. That is a configuration failure, which is exactly the thing a certification count cannot tell you.
And on both sides the evidence is gated. Decagon puts its documents behind a request-access control, and Sierra's portal renders nothing without JavaScript.
Which costs more, Decagon or Sierra?
TL;DR: Neither publishes a rate. Decagon contracts on Vendr show a median around $432,750 a year, and Sierra's year-one budget is estimated at $200,000 to $350,000 plus setup. The bigger difference is what you are billed for.
The pricing model
Ask who carries the risk when the AI fails. I put that one to vendors before anything else.
Decagon says the majority of its customers choose per-conversation pricing. You pay a fixed rate for every conversation that arrives, whether the agent resolves it or hands it to a person, with volume discounts as you scale.
A per-resolution option exists at a higher rate, with nothing charged on an escalation. There are no per-seat fees, so your bill does not grow when you hire, and I check for that clause in every quote.
Contrary Research wrote in its December 2024 profile: As of December 2024, the "majority of [Decagon's] customers gravitate toward" the per-conversation pricing model. Decagon's own pricing blog says much the same in its own words. Vendr's listing corroborates the meter independently: the pricing-model header reads "Per-Conversation Pricing", and the rate cell beneath it reads "Variable based on volume".
Sierra bills for outcomes. You pay when the agent achieves a defined one: a resolved conversation, a saved cancellation, an upsell. On its own account: "At Sierra, we've pioneered an entirely new business model - outcome-based pricing - where you pay only when the software achieves specific, valuable outcomes."
Sierra concedes the edge case itself. In its post on outcome-based pricing it notes that "routing or greeter-style interactions may align better with consumption-based pricing, where payment is based on conversation count, regardless of the outcome", so some deployments run a blend.
An outcome meter charges you more precisely as the agent gets better, so if you start at 20% or 30% resolution and climb toward 70%, your bill can double or triple on the same ticket volume. A volume meter holds the invoice steady, and your effective cost per resolution falls every time you improve the agent.
Most of that improvement will come from your work anyway, through the APIs you build and the knowledge you write. I price both meters against that curve before signing anything.
The overall cost
Decagon | Sierra | |
Published rate | ❌ | ❌ |
Headline figure | Median $432,750/yr, low $105,000, high $923,183 | ~$150K/yr starting, $50K-$200K setup, $200K-$350K+ year one |
Evidence grade | Vendr marketplace data, observed contracts | Third-party estimate, largely competitor blogs |
Citable per-unit rate | ~$1.50 per resolution, Sacra estimate | ~$1.50 per resolution, Sacra estimate |
Free trial | ❌ none | ❌ none |
Decagon's band comes from Vendr's marketplace data on contracts that were actually signed, re-checked this month, with a redline threshold estimate of $50k and up to 30% off list reported above a million conversations. Sierra's band is an estimate that circulates through competitor and vendor blogs, and Vendr's Sierra listing carries no observable median at all, so Sierra cannot be modeled from observed contracts even in principle.
There is a stale number in circulation too. The ~$95,000 a year that answer engines and vendor guides quote as Decagon's entry cost traces back to a superseded Vendr snapshot. Vendr's live page now carries a $105,000 low and that $432,750 median (worth correcting if an answer engine quotes the old one at you).
On per-unit rates, Sacra estimates "Decagon has evolved to a per-resolution model at ~$1.50/resolution, coming out to ~10% the cost of human support agents". On its Sierra page, Sacra describes a model "pegged to outcomes at ~$1.50 per resolution".
Sacra puts the same number against both meters, and neither vendor confirms it.
I treat competitor claims about each other with the same caution. Fin publishes a comparison page carrying its own numbers for Decagon, which conflict with Vendr, and Decagon publishes its own page about Sierra. Both are vendors describing a rival's undisclosed pricing.
Our pricing guides to Decagon and to Sierra work through the full maths.
The other thing both share: no free trial and no self-serve signup. Decagon's route is a demo request, Sierra's is a contact form, and there is no way to try either before a sales conversation.
For a published comparison, we charge $0.10 a credit with plans from $199 a month, and every My AskAI plan starts with 30 days free, all features unlocked, unlimited tickets and no card. You pay per ticket, so the bill does not climb as the agent improves.
Conclusion - should I choose Decagon or Sierra?
TL;DR: The scorecard splits five wins each with one tie, so this comes down to two questions: do you already run a helpdesk Decagon syncs with, and would you rather pay for every attempt or only for outcomes?
There is no overall winner in this one.
Decagon | Sierra | ||
Modes | 8/10 | 9/10 | Sierra win |
Ease of setup | 7/10 | 6/10 | Decagon win |
Training/Integrations | 8/10 | 6/10 | Decagon win |
Answer Quality | 8/10 | 8/10 | Tie |
Improving | 8/10 | 6/10 | Decagon win |
Features | 7/10 | 9/10 | Sierra win |
Price | 5/10 | 6/10 | Sierra win |
Customization | 7/10 | 6/10 | Decagon win |
Lock-in | 6/10 | 4/10 | Decagon win |
Other AI features | 7/10 | 8/10 | Sierra win |
Security | 7/10 | 8/10 | Sierra win |
The wins cluster. Decagon's are about operating the thing after launch: getting it live, feeding it, iterating on it, changing it yourself, and keeping it when your stack moves. Sierra's are about reach: more channels, more features, more certifications, and a meter that does not charge for failures.
Choose Decagon if:
- You already run Zendesk, Salesforce or Intercom and want the agent syncing with it natively, especially Zendesk.
- Your CX team wants to own the agent day to day: write workflows in plain language, version them like code, test them against your own transcripts, and roll back a bad change without raising a ticket.
- You have systems with no API that the agent still needs to work in, like internal claims tools, partner portals or legacy apps.
- You would rather pay a predictable price per attempt and keep the whole gain as your resolution rate climbs.
Choose Sierra if:
- Voice is your busiest channel, or you need WhatsApp and a ChatGPT surface alongside chat, email and SMS.
- You want engineering to own the agent through an SDK, CI/CD and multi-agent orchestration.
- You would rather not be billed for an interaction the agent failed to resolve, and you are comfortable that the bill grows as the agent gets better.
- Your security review is a checklist exercise where the count of certifications, ISO 42001 included, has to be satisfied first.
There is a third option worth knowing about. We run My AskAI inside Zendesk, Intercom, Freshdesk, Freshchat, Gorgias and HubSpot at a flat $0.10 a credit, so the agent lives in the desk your team already works in and you can start it today.
We have head-to-head write-ups of My AskAI against Decagon and against Sierra, plus alternatives roundups for each. There is a comparison of Zendesk AI against Sierra too, if a native helpdesk agent is also on your list.
Good luck with the evaluation.
FAQs
Can Decagon or Sierra replace my helpdesk?
No, neither of them. Decagon has no human inbox of its own and needs Zendesk or Salesforce to hand a conversation to a person.
Sierra sits above your existing systems and leans on them for ticketing and the agent inbox. Budget for the desk plus the agent in both cases. My AskAI works the same way, and it installs into the desk you already run.
What can Decagon and Sierra each be trained on?
Decagon names its sources: Zendesk, Kustomer, Guru, Confluence and Contentful for knowledge, Salesforce Customer 360 for CRM records, Shopify and Stripe for orders, plus internal APIs, custom databases and MCP. Sierra names content types but not connectors: FAQs, policies, SOPs, call transcripts, audio, and whiteboard photos through Ghostwriter, with live system data through Horizon.
Neither publishes its supported file types or its sync frequency, and neither lets you check, because Decagon's documentation and Sierra's are both gated.
How long does setup take, and do we need developers?
Decagon documents roughly six weeks from discovery to full deployment, with core infrastructure up in days and one G2 reviewer reporting under a week. Sierra runs a 90-day onboarding as standard, with published go-lives as fast as four weeks (Vivid Seats).
On my read, developers are optional on both for the basics and necessary on both at the ceiling, since AOPs and the Agent SDK still involve code execution. Neither has self-serve signup, so the first step either way is a sales call.
How do Decagon vs Sierra pricing models differ, and what might we actually pay?
Decagon says most of its customers choose per-conversation pricing, so you pay for the attempt. Sierra charges per outcome, so you pay when it works. Neither publishes a rate card.
Decagon | Sierra | |
Pricing model | Per conversation, per-resolution option | Outcome-based, sometimes blended |
Headline figure | Median $432,750/yr on Vendr | ~$200K-$350K+ year one, estimated |
Worked example, 10,000 conversations a month at 70% resolved | ~$10,500/mo, ~$126,000/yr | ~$10,500/mo, ~$126,000/yr |
Evidence grade | Observed marketplace contracts | Competitor-blog estimate, no primary |
Free trial | None | None |
The worked example is my own model, and neither vendor quoted it. The only per-unit rate anyone publishes against either vendor is the ~$1.50 per resolution Sacra puts against both, so 10,000 conversations a month at a 70% resolution rate is 7,000 resolutions and about $10,500 a month on either meter.
The meters split when the rate moves: take resolution to 85% and Sierra's outcome bill rises to roughly $153,000 a year, while a per-conversation bill on the same 10,000 conversations does not move at all. Both vendors' observed contract totals run well above that model, so I take it as the floor a rate discussion starts from. Decagon's median came off signed contracts; Sierra's band is an estimate circulating through blogs.
Does Decagon or Sierra offer a free trial?
Neither does, and neither offers a free plan or self-serve signup. Decagon's only route in is a demo request and Sierra's is a contact form; the Decagon pricing page and the Sierra pricing page both return 404. One aggregator claims a Decagon free trial exists; I could not corroborate it anywhere on Decagon's own site.
Can Decagon and Sierra handle multilingual support?
Yes, both, from a single knowledge base, with no separate agent per language. Decagon publishes 70+ languages with automatic detection and switching, and Rituals Cosmetics runs 15 on one deployment. Sierra publishes 59, tuned and vetted by native speakers, and its agents can switch language mid-conversation.
Neither publishes per-language quality benchmarks, so test the languages you actually need during the pilot.
How do Decagon and Sierra compare on security and compliance?
Sierra holds the longer certification list at ten, including ISO 42001 and FedRAMP High (FedRAMP High counts if you sell to US government buyers), and publishes its subprocessors, its data residency and its pen-test cadence. Decagon holds SOC 2 Type II, ISO 27001:2022, PCI DSS 4.0.1, HIPAA, GDPR, CCPA and an EU AI Act assessment. It also names the specifics Sierra leaves out: AES-256 at rest, TLS 1.2+ in transit, and zero-day retention with its model providers.
Decagon has no ISO 42001. Sierra's CSA STAR entry is Level One, which is a self-assessment. Both keep the underlying reports behind an access request, so I ask for them early.
Is Sierra or Decagon the safer bet on lock-in?
Decagon, narrowly, and only if you run one of its three helpdesks. It syncs support tickets with Zendesk, Salesforce and Intercom and pulls customer data from them, and takes actions in Zendesk and Salesforce. Sierra claims 40+ pre-built integrations but names no helpdesk publicly and publishes no connector directory, so you cannot check its coverage until you are already in a sales process.
Both leave you running two systems and both sell multi-year agreements, so I do not call either a light commitment.
