Decagon and Sierra are both strong enterprise AI agent platforms. The question that decides between them is not which is better in the abstract, but which one fits the way a regulated support team actually buys, deploys, and gets audited.
This is a decision guide, not a ranking. Decagon and Sierra are two of the most credible AI customer service agent platforms in the market, and most teams that shortlist one shortlist the other. They overlap heavily on capability and differ most on pricing philosophy, deployment model, and how each was built. Below we walk through five evaluation criteria that matter in regulated support - guardrails, audit, workflows, pricing, and deployment - explain how Decagon and Sierra each fit those criteria, and close with the conditions under which a regulated buyer should also look at Lorikeet.
Decagon and Sierra both ship voice, chat, and email, both serve large enterprises, and both negotiate custom pricing rather than publishing rates.
The clearest difference is the pricing model: Sierra is known for outcome-based billing (you pay on full resolution), while Decagon offers per-conversation or per-resolution models and is selectable case by case.
Both lean on high-touch, embedded deployment. For a regulated buyer the question is who owns and can change the workflows after launch.
In regulated support (fintech, healthtech, insurance, gaming) the deciding criterion is usually whether the agent's behavior is provable before go-live and replayable after, not the headline resolution rate.
If the deciding factor is regulated-grade guardrails, audit trails, and the hardest tickets (KYC, disputes, transfers, claims), it is worth running Lorikeet alongside both.
Last updated: June 2026
Most comparison pages try to declare a winner. That framing fails for AI customer support, because the right answer depends on what you are buying for. A high-volume consumer brand that wants billing aligned to outcomes has a different ideal than a fintech whose compliance lead has veto power over launch. This guide is structured so you can match the criterion that matters most to your business against how Decagon and Sierra each behave, then decide. We try to be fair to both, and we name the points where a regulated buyer should keep looking.
Decagon and Sierra at a glance
Both companies emerged in the current wave of agentic AI support, both are well funded, and both target enterprise buyers with embedded deployment teams. They are genuine peers.
Sierra was founded by Bret Taylor and Clay Bavor and scaled quickly, reaching $100M ARR in 21 months per TechCrunch. Its signature is outcome-based pricing: customers pay when the AI fully resolves a case, and escalations to humans cost nothing. It ships voice, chat, and email, and leans on a branded persona approach to deployment with embedded Sierra staff.
Decagon is the high-end enterprise AI agent platform with named fintech and consumer customers and a white-glove implementation model. It offers per-conversation or per-resolution pricing that the customer can select, runs voice, chat, and email, and is backed by significant venture funding. Industry data suggests a median total contract value near $400,000 per year, with an annual platform fee plus per-interaction fees.
The honest summary: on raw capability they are close. The differences that decide a purchase show up in pricing structure, who controls the workflows after launch, and how each handles the requirements specific to regulated industries.
Side-by-side
A quick orientation before the criteria-by-criteria walkthrough. Treat published figures as directional, since neither vendor publishes rates and contract values vary by company size and ticket volume.
Pricing model · Decagon: per-conversation or per-resolution, customer-selectable, plus an annual platform fee · Sierra: outcome-based, pay only on full resolution, escalations free
Reported contract value · Decagon: median near $400,000/year · Sierra: reportedly $50,000 to $200,000/year
Channels · Decagon: voice, chat, email · Sierra: voice, chat, email
Deployment · Decagon: white-glove, embedded engineering · Sierra: high-touch, branded persona, embedded staff
Industry focus · Decagon: broad enterprise incl. fintech and consumer · Sierra: broad enterprise incl. financial services
Signature strength · Decagon: proven scale, selectable pricing · Sierra: incentive-aligned outcome billing
For a regulated buyer, the row that does not appear in most vendor decks is the one that matters most: how deep is the audit trail, and can your compliance team prove the guardrails before launch. That is where the criteria below focus.
The five criteria that decide regulated support
Generic CX buying guides start with deflection rate, response time, and CSAT. In a regulated business those are downstream of correctness and provability. The five lenses below are the ones a fintech, healthtech, insurer, or gaming operator should weigh, and we describe how Decagon and Sierra each fit each one.
1. Guardrails
In regulated support, a guardrail is anything that stops the agent from doing the wrong thing: scripted disclosures, dollar-threshold blocks, jurisdiction-specific responses, PII handling rules, and escalation triggers. The test is not whether guardrails exist at runtime - almost every serious vendor has them - but whether you can define them in your own policy language and prove they hold before launch.
How Decagon fits: Decagon's white-glove model means its team typically helps configure guardrails as part of deployment. For a buyer with limited internal engineering this is a benefit. The consideration for a regulated team is whether your compliance lead can read, change, and re-test those guardrails independently, rather than filing a request with the vendor.
How Sierra fits: Sierra similarly deploys with embedded staff and a persona-led configuration. Its guardrail story is tied to that high-touch model. The same question applies: how much of the guardrail surface is owned by your team versus the vendor's, and can you run the bad paths yourself before go-live.
What to ask either vendor: can my compliance team write a guardrail in plain language, run it against a test suite, and read the pass and fail report before we launch? If the answer routes through the vendor's services team, factor that into your change-management timeline.
2. Audit
An audit trail in regulated support is a timestamped, replayable record of every tool call, prompt, and reasoning step the AI made on a given ticket. It is the artifact your compliance team uses during a regulator examination, and the single most important fintech and healthtech-specific capability. A transcript is not an audit trail.
How Decagon fits: Decagon operates production deployments at scale and maintains logging suitable for enterprise governance. As with any vendor, confirm the depth: can you replay the full reasoning chain plus tool calls for any ticket from 90 days ago, in order, not just a sampled summary.
How Sierra fits: Sierra's enterprise posture includes the logging large customers expect. The same depth question applies. Ask to see an end-to-end audit trail for a real decision the AI made last week, with every tool call and the reasoning between them.
For both, the standard a regulated buyer should hold is replayability at the reasoning-step level, not a conversation export. When a KYC unlock or a claim decision goes wrong, you need to point at the exact step where it went wrong.
3. Workflows
Most regulated tickets are not single questions. They are sequences: verify identity, check why a transfer failed, refund a fee, update an address, and escalate if a threshold is crossed. The platform has to chain several tool calls in the right order, keep state, and recover when one tool errors mid-chain.
How Decagon fits: Decagon is built for multi-step resolution and supports action-taking across integrated systems, which is why it shows up in enterprise fintech shortlists. The deployment model means much of the initial workflow build is done with Decagon's team.
How Sierra fits: Sierra similarly supports agentic, multi-step resolution and integrates with enterprise systems. Its configuration is persona-led and high-touch.
What to ask either vendor: what happens when a core system returns a 5xx error mid-chain - retry, escalate, or roll back? And after launch, can our team author and edit workflows in plain language without a vendor services engagement for every change? The answer to the second question shapes how fast you can respond to a new regulation or product change.
4. Pricing
This is the clearest structural difference between the two.
How Sierra fits: Sierra is the standard-bearer for outcome-based pricing - you pay only when the AI fully resolves a case, and escalations cost nothing. The appeal is incentive alignment and budget predictability. Enterprise contracts are reportedly in the $50,000 to $200,000 per year range, with the per-resolution rate negotiated case by case. The consideration for regulated buyers: any model that pays only on full resolution can create a quiet bias toward easy tickets, and in regulated support the hard tickets (disputes, KYC, claims) are the ones that matter most.
How Decagon fits: Decagon offers per-conversation or per-resolution pricing that the customer selects, plus an annual platform fee. Industry data suggests a median total contract value near $400,000 per year, which places it at the premium end. The per-conversation option can be attractive when you want to pay for engagement regardless of outcome, but it also means you pay on conversations that do not resolve.
Neither publishes rates, so both require a sales conversation. Model your own ticket mix against each structure: outcome-only pricing rewards a vendor differently than per-conversation pricing, and the right choice depends on how hard your tickets are and how often they fully resolve.
5. Deployment
Both Decagon and Sierra use a high-touch, embedded deployment model with vendor staff involved during launch. For an enterprise without a large internal AI team, that hand-holding is genuinely useful and shortens time to first production tickets.
The regulated-buyer consideration: embedded engineering is sold as a feature, and for many teams it is. The honest read is that it can also reflect how hard the platform is to configure alone. The question to settle before signing is ownership: after the launch period ends, can your team independently change workflows, guardrails, and integrations, or does every material change require the vendor's services team? In a regulated business where rules change and you need to move quickly, post-launch self-sufficiency is worth more than launch-period speed.
When Decagon is the right fit
Decagon tends to fit large fintech and consumer enterprises with substantial support budgets that want a top-of-market platform and are comfortable with a premium total contract value and a white-glove deployment. If you value being able to choose between per-conversation and per-resolution pricing, want a vendor with proven scale across millions of interactions, and have the budget for a six-figure annual commitment, Decagon belongs on the shortlist. It is a strong choice when breadth, scale, and a selectable pricing model matter more than owning every configuration yourself.
When Sierra is the right fit
Sierra tends to fit enterprises that want billing aligned to outcomes and predictability in the support line item, and that find a branded persona deployment appealing. If your priority is paying only for fully resolved cases, you have the procurement appetite for a $50,000 to $200,000 annual spend, and your ticket mix skews toward cases that fully resolve, Sierra is a credible pick. It is strongest where outcome alignment is the dominant buying criterion and the ticket base is not heavily weighted toward the hardest regulated cases.
When a regulated buyer should consider Lorikeet instead
Lorikeet is an AI customer support platform built specifically for complex and regulated businesses - fintech, financial services, healthcare and healthtech, insurance, and gaming. Roughly 80% of its customers are US financial institutions and fintechs. It is worth running alongside Decagon and Sierra when the deciding factor is the regulated-support criteria above rather than general enterprise CX.
Consider Lorikeet when these conditions hold:
Guardrails are your gating stakeholder. Lorikeet's approach is defence in depth: pre-launch adversarial simulations and red-teaming, inbound message checks, outbound guardrails, and 100% post-facto QA through its Coach agent. The intent is that your compliance team can sign off before launch rather than review after. Compliance features are designed to support your obligations, not to certify them on your behalf.
Audit and QA are continuous, not sampled. Coach runs automated quality assurance on 100% of tickets with root-cause analysis and resolution verification - the AI evaluating the AI - and is also deployable standalone at roughly $0.25–$0.30 per ticket.
Your team needs to own the workflows. Lorikeet combines natural-language workflows with deterministic structured workflows in a single interaction, all configurable in plain English, so your team can change behavior after launch without a vendor services ticket for every edit.
Channels include voice with low latency. Lorikeet runs chat, email, voice (sub-1-second latency), SMS, and WhatsApp on one engine, plus outbound re-engagement with compliance controls such as do-not-call and call-hour rules.
You want transparent, outcome-anchored pricing without an outcome-only bias. Lorikeet charges roughly $0.80–$0.95 per chat, email, or SMS resolution and roughly $1.20–$1.50 per voice resolution, the customer defines what counts as a resolution, and escalations are not charged. Against a human baseline of roughly $1.25 to $4 per handled ticket, the model is designed to price the hard tickets honestly rather than steer toward easy ones.
Lorikeet is the narrower choice: it is purpose-built for regulated industries rather than being a general-purpose enterprise agent, so a buyer whose needs are broad consumer CX may find Decagon or Sierra a more natural fit. Where Lorikeet earns its place on the shortlist is depth on the regulated tickets that matter - KYC unlocks, disputes, transfers, claims - and provability before go-live. It holds SOC 2, is BAA-ready for HIPAA, is GDPR-aligned, supports data residency in the US, AU, and UK, and maintains contractual no-train agreements with its model providers. The fair caveat: Lorikeet does not chase the broadest set of consumer-CX use cases, and a team buying primarily for high-volume retail or general SaaS support may find a generalist platform a closer match.
Where each can fall short
A fair decision guide names the friction, not just the fit. None of these are disqualifiers; they are the trade-offs to price in.
Decagon. The premium total contract value puts it out of reach for smaller teams, and the white-glove model can mean your team is dependent on the vendor for configuration changes well past launch. If post-launch self-service is a hard requirement, confirm exactly how much you can change without a services engagement.
Sierra. Outcome-only pricing is elegant, but it carries a structural incentive that matters in regulated support: a vendor paid only on full resolution is rewarded for the tickets that resolve cleanly. In fintech, healthtech, and insurance the cases that decide your risk are the messy ones, so confirm how Sierra handles partial resolutions and the hard 20% that escalate. The branded-persona deployment is also more consumer-brand oriented than compliance-first.
Lorikeet. It is deliberately specialized for regulated industries, so its breadth across general consumer CX is narrower than the two generalists. It is also a younger company than the largest incumbents in the broader category, which some procurement teams weigh. Its depth on regulated workflows, audit, and QA is the reason it earns a place on a regulated shortlist despite the narrower surface area.
How to run the decision
Demos are built to look good. Make the decision on the same hard cases across all three vendors. Bring your hardest tickets - a KYC unlock that failed, a disputed transaction, a claim that needs a threshold check - and ask each vendor to run them in your stack against your guardrails. Then compare on the five criteria in order of what matters most to your business.
Ask for an end-to-end audit trail of a real decision the AI made last week, with every tool call and the reasoning between them.
Ask whether your compliance team can write and re-test a guardrail before go-live and read the pass and fail report.
Ask what happens when a core system returns a 5xx error mid-chain.
Ask who owns and can change the workflows after the launch period ends.
Model your real ticket mix against each pricing structure, paying attention to cost on the hard 20% of tickets that do not fully resolve.
Decagon and Sierra are both strong, and for many enterprises either is the right answer. For a regulated buyer whose toughest stakeholder is the compliance lead, the same questions that separate Decagon from Sierra are also the questions that make it worth running Lorikeet alongside them.
