Decagon vs Sierra (2026): Two AI Support Agents, Zero Published Prices

TL;DR: Neither vendor prints a price. We fetched decagon.ai/pricing and sierra.ai/pricing on July 12, 2026, and both returned HTTP 404. So the honest comparison is business model against business model. Decagon meters usage — third parties report roughly $0.99 per conversation plus a $50,000-a-year platform fee, so you pay even when the bot fails, but the bill is forecastable. Sierra meters outcomes — the vendor says you mostly pay only for resolved conversations, but "resolved" is whatever the contract says it is. Expect six figures a year either way. Every number on this page is labeled vendor claim, third-party report, or verified fetch.

Most " Decagon vs Sierra " pages are written by their competitors. Intercom's Fin, Quiq, eesel, and a half-dozen other AI-support vendors all publish " Decagon pricing" and " Sierra pricing" explainers, and every one of them ends with a pitch for the author's own product. That does not make their numbers wrong, but it makes them adversarial evidence, and we label them that way throughout. What no page in the top results does is start from the one thing we could verify directly: on July 12, 2026, neither company had a pricing page at all. Both URLs 404. Everything else — the $0.99 conversations, the $433K median contracts, the $150K starting quotes — is reporting, estimation, or marketing, and this page tells you which is which for every figure.

One more thing an honest comparison owes you up front: we have not run our hands-on suite on either product. Both are demo-gated with no trial and no self-serve signup, so there is nothing for an outside reviewer to test without becoming a six-figure customer. What follows is built from live fetches of both vendors' sites, dated funding coverage, procurement data as reported, the small public review base that exists, and competitor claims flagged as competitor claims.

Disclosure: we have no affiliate or business tie to Decagon , Sierra , Intercom, Zendesk, or Salesforce as of publication; if that changes, this line will say so. Neither vendor briefed us or gave us access. Our funding model is in our editorial policy .

Decagon vs Sierra at a glance

Negative checks and Fin's published rate verified July 12, 2026 by direct fetch. Decagon and Sierra dollar figures are third-party reports or estimates and are not vendor-confirmed; details and sources below.

Decagon: usage-metered, procedure-deep

Decagon sells AI support agents across chat, voice, and email, configured through what it calls Agent Operating Procedures — plain-English instructions that define how the agent handles each scenario, with A/B testing to compare procedure variants. That control surface is the recurring reason buyers pick it: it behaves less like a black-box bot and more like a system your CX team can actually program without engineers.

What it costs. Decagon publishes nothing — decagon.ai/pricing returned HTTP 404 on July 12, 2026, our direct check. What exists is third-party reporting: roughly $0.99 per conversation, or negotiated per-resolution rates for larger deals, layered on top of a platform fee reported around $50,000 per year (per Intercom's Fin, a competitor — unverified; eesel's own pricing explainer carried no dollar figures at our July 12, 2026 check, saying only that contracts typically start in six figures). Procurement data published by Vendr puts the median annual Decagon contract at $432,750, with a range from about $105,000 to $923,000 (vendr.com/marketplace/decagon-ai, fetched directly July 12, 2026). None of these numbers are vendor-confirmed. A single competitor blog also floats a "$0.50 per resolution negotiated rate"; one uncorroborated mention from a rival is not evidence, so we do not carry that number.

What the vendor claims it delivers. Decagon 's homepage (fetched July 12, 2026) claims Chime reached "70% chat and voice resolution," Duolingo "80% deflection," ClassPass "95% cost reduction," and Curology "65% cost reduction," alongside logos for Notion , Rippling, Cash App, and Riot Games. Every one of those is a vendor claim on the vendor's own site — no third party has audited any of them, and you should ask for the underlying definitions in any sales conversation (deflection and resolution are not the same metric).

What users report. The public review base is small — 18 reviews on G2 as of July 12, 2026, so treat every theme as low-sample. Reviewers praise easy implementation and a responsive vendor team. The recurring negatives: performance degradation during ticket-volume spikes, the need for a dedicated "bot manager" on staff to keep procedures tuned, and Agent Assist working only on Zendesk. Ticket Resolution is Decagon 's lowest G2 category score at 7.9/10 — a notable data point for a product whose entire economic argument is resolving tickets.

The money mechanics. Under per-conversation pricing you pay for every conversation the agent touches, including the ones it fails and hands to a human. That is the model's weakness and its strength at once: you fund failures, but you can also forecast the bill from ticket volume with simple arithmetic — something Sierra 's model cannot offer. Decagon closed a $250M Series D at a $4.5B valuation in January 2026, led by Coatue and Index (per CMSWire and other coverage — third-party), following a $131M Series C at $1.5B in June 2025 (Businesswire), so vendor-viability risk looks low for the medium term.

Sierra: outcome-metered, Fortune-500-shaped

Sierra , co-founded by Bret Taylor — ex-Salesforce co-CEO and OpenAI board chair — sells AI agents positioned above deflection: not just answering tickets but saving cancellations and completing purchases. Its pricing philosophy is the company's loudest differentiator, stated on its own homepage and in a December 10, 2024 blog post by Elliot Greenwald (both fetched July 12, 2026): outcome-based pricing, "pay for a job well done," where "if the conversation is unresolved, in most cases, there's no charge." Billable outcomes include resolved conversations, saved cancellations, and purchases or upsells the agent completes.

What it costs. In dollars — nobody outside a Sierra contract knows. sierra.ai/pricing returned HTTP 404 on July 12, 2026, and Sierra has never published a per-resolution rate. Third-party estimates (Quiq's competitor blog, updated February 10, 2026, and Fin's — both adversarial sources, both unverified) put contracts at roughly $150,000 per year to start, setup and integration at $50,000–$200,000, and realistic year-one totals at $200,000–$350,000 or more. Because every rate is negotiated, two customers with similar volume can pay materially different prices for the same outcome — there is no rate card to anchor against.

What the vendor claims it delivers. Sierra 's homepage logos (verified July 12, 2026) include Rocket Mortgage, SiriusXM, ADT, SoFi, Discord, DIRECTV, Vanguard, Wayfair, Nubank, DocuSign, and Ramp. TechCrunch reported on May 4, 2026 that Sierra raised a $950M Series E led by Tiger Global and GV at a valuation it put above $15 billion — same-day coverage from Axios and CMSWire specified $15.8B — and that more than 40% of the Fortune 50 are customers, with ARR crossing $100M in late 2025 and $150M in early 2026, roughly two years after the product's February 2024 launch. All of that is third-party reporting of company-provided figures: an exceptionally fast enterprise-software revenue ramp, and also not an audit of customer results.

What users report. Here we have to be blunt: we found no substantial independent user-review base for Sierra as of July 12, 2026. Its buyers are large enterprises operating under NDAs, and they do not write G2 reviews. What exists is third-party reviewer commentary (Quiq, MyAskAI, Macha — all competitors or competitor-adjacent), and their consistent themes are worth knowing even discounted for bias: disputes over what the contract counts as a "resolution," invoice unpredictability when conversation volume spikes, and the largest reported setup fees in the category. Auditing what you were billed for becomes a recurring finance task, because the incentive runs the other way — the vendor is paid more the more conversations get classified as resolved.

The money mechanics. Outcome pricing is genuinely aligned in one direction: a bot that fails costs you (mostly) nothing. But it replaces a forecasting problem with a definitional one. "No further help requested after the bot's last answer" can mean the customer was helped — or gave up. If you cannot staff contract review at signing and invoice review every month, you are not equipped to buy this model; the alignment only works if someone on your side checks the ledger.

The real comparison: two ways to charge for the same job

Strip the logos away and the choice is a pricing-theory question. Both products wrap frontier LLMs — the same class of models whose raw token prices we track live on our LLM API price tracker — in support-specific tooling: integrations, procedures, guardrails, analytics. A resolved support conversation consumes a few cents of tokens; both vendors meter it at a dollar or more. What you are actually buying is the wrapper, the accountability, and the deployment help, and the two companies have made opposite bets on how to charge for it.

Decagon's bet: predictability. Usage-based billing means your bill scales with volume you can already forecast from your ticket history. CFOs can model it. The cost is philosophical and real: you pay full rate for the conversation where the bot flailed, apologized twice, and escalated to the human agent you are also paying.

Sierra's bet: alignment. Outcome billing means the vendor eats the failures. The cost is contractual: "resolution" is a negotiated definition, the vendor classifies the outcomes, and reviewers — biased ones, but consistently — report that the classification becomes a monthly argument. You also pay for revenue events (saves, upsells) that arguably your product, not the bot, deserves some credit for.

The math that decides it. At enterprise volume the gap compounds fast. A 100,000-conversation month at a reported ~$0.99 per conversation is ~$99,000 before the platform fee; the same month billed per-outcome at a hypothetical negotiated rate depends entirely on resolution rate and the rate you negotiated — which is exactly why Sierra publishes neither. And both numbers sit an order of magnitude above the raw model cost of serving those conversations yourself. If your volume is high enough that the metered margin exceeds an engineering team's salary, the third option is real: our build vs buy analysis for AI agents works that math end to end, and our cheapest LLM API breakdown sets the cost floor for the build path. If, at the other extreme, six-figure quotes made you flinch, you are probably not the buyer for either vendor — an AI receptionist at $30–$300 a month is the SMB-scale version of this same product category.

The escape hatches: options with a printed price

Because neither headline vendor will name a number in public, the strongest negotiating tool is a competitor that does.

Intercom Fin publishes $0.99 per resolution on fin.ai/pricing — verified by direct fetch on July 12, 2026, the only printed price among the head-to-head options here. Intercom states there are no integration, setup, or platform fees on non-Intercom helpdesks, defines a resolution as no further help requested after Fin's last answer, and prices "qualifications" (lead-capture events) at $9.99 each. Fin runs on Zendesk and Salesforce helpdesks, not just Intercom. The caveats: Fin's own " Decagon pricing" and " Sierra pricing" pages and TCO calculators are competitor marketing — we used them here only with that label — and Fin's enterprise workflow depth (voice, complex multi-step procedures) is thinner than either Decagon 's or Sierra 's. Its highest value in this comparison may be as the benchmark you put on the table: "Fin charges $0.99 per resolution with no platform fee; walk me through why your effective rate is higher."

Zendesk AI Agents meter resolutions on top of Suite seats — around $1.50 per committed resolution or $2.00 pay-as-you-go per third-party reports (Voiceflow, eesel, Corepiper). On May 18, 2026 Zendesk shifted to a three-tier Verified Resolution model: only resolutions its AI verifies as resolved are billed, while merely assisted or contained conversations are free — a step toward Sierra -style outcome billing, so spot-verify current rates and definitions on zendesk.com before budgeting. If Zendesk already runs your support org, this is the no-new-vendor path. The reported catch: resolutions above your committed volume auto-bill at the full per-resolution rate with no prior notification (third-party report — verify against your contract), and you keep paying for seats underneath.

Salesforce Agentforce prints its rates: $2 per conversation, or a Flex Credits model where a standard Agentforce action costs 20 Flex Credits — about $0.10 — and a Voice action 30 credits (salesforce.com/agentforce/pricing, verified July 12, 2026; the page 403s automated fetchers but loads with a browser user-agent). Note it is per-conversation, Decagon -style: failures bill. Its case is organizational, not economic — agents that inherit your existing Service Cloud data and permission model.

Building your own stops being exotic at exactly the scale where Decagon and Sierra quotes start. The token cost of a support conversation is cents; the metered price is a dollar or two; the difference funds the vendor's margin, and above some volume it funds an engineering team instead. The build-vs-buy guide prices the crossover honestly — including the part vendors are for: when your homegrown bot promises a refund policy that does not exist, there is no vendor to share the liability.

Pick by situation

If you are genuinely between the two headline vendors, run both sales processes in parallel and make each quote against the other and against Fin's printed $0.99. In a category with no rate card, your best leverage is the competitor's number — and both vendors know their prices are negotiable, because negotiation is the only way anyone has ever bought them.

Where both fall short

Zero price transparency, by design. Two companies with a combined $20B in private-market valuation each decided you cannot see a price. Both /pricing URLs 404 (verified July 12, 2026). No self-serve tier, no trial, no rate card. Every dollar figure in the public record comes from competitors, procurement leaks, or estimates. That is not an accident of early-stage websites; it is a sales strategy, and it costs you weeks of discovery calls just to learn whether you can afford the conversation.

Every headline stat is a vendor claim. Chime's 70% resolution, ClassPass's 95% cost reduction, Duolingo's 80% deflection, Sierra 's Fortune-50 penetration — all sourced to vendor homepages or company-provided figures in funding coverage. We found no independent audit of any deployment metric for either company. Sierra additionally has effectively no public user-review corpus at all; the buyer experience is locked behind enterprise NDAs.

Six figures before value. Third-party reporting puts realistic year-one commitments at ~$105K minimum and a ~$433K median for Decagon (per Vendr procurement data, fetched July 12, 2026) and $200K–$350K+ for Sierra (competitor estimates, unverified). Both are bets you cannot pilot cheaply.

The category's own track record urges caution. Klarna — once the loudest AI-first support story — publicly reversed course in May 2025, with CEO Sebastian Siemiatkowski conceding "we focused too much on efficiency and cost… the result was lower quality" and rehiring humans (reported by Bloomberg, via Forbes and eMarketer). And Moffatt v. Air Canada (British Columbia Civil Resolution Tribunal, February 2024) held the airline liable for its chatbot's invented bereavement-fare policy — $812.02, small in dollars, precedent-sized in implication: your AI agent's promises legally bind you, whichever vendor's logo is on it. The FTC's "Operation AI Comply" sweep (September 2024) made the same point from the regulator's side — there is no AI exemption from consumer-protection law. None of this says don't buy; it says the contract's accuracy, liability, and escalation clauses matter as much as the per-unit rate.

And our own limit, restated: we have not run our hands-on suite on either product — both are demo-gated with no trial. When that changes, this page will carry the results and a changelog entry.

All guides in this topic

• Build vs buy for AI agents: the worked math — the per-resolution vs per-token crossover, priced end to end
• Live LLM API price tracker — what the models under these products actually cost per million tokens
• The cheapest LLM API right now — the cost floor for the build path
• AI receptionists — the $30–$300/month version of this category for small businesses
• Claude vs ChatGPT — the same verify-everything method applied to the consumer AI decision

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