Decagon pricing

About

Decagon builds enterprise AI customer support agents — it calls the product an “AI concierge” that lets companies build, optimize and scale agents handling customer interactions across voice, chat and email through a single intelligence layer. The platform targets high-volume consumer-facing brands; named customers include Chime, Duolingo, ClassPass, Curology, Rippling, Hunter Douglas, Faire and Riot Games, across retail, travel & hospitality, technology, financial services, health & wellness, media and telecommunications.

Decagon is private and well-funded: it raised 131M USD at a 1.5B USD valuation in June 2025, then a 250M USD Series D in January 2026 led by Coatue Management and Index Ventures at a 4.5B USD valuation. Sacra estimated roughly 35M USD annualized revenue in October 2025, up from about 10M at the end of 2024.

For the most current information, visit Decagon.

Pricing summary : How Decagon’s pricing model works

Decagon is sales-only — there is no public price list. Every pricing call-to-action on the site is “Get a demo,” and contracts are negotiated as custom enterprise annual deals. What is documented (in Decagon’s own glossary and across third-party reviews) is the structure of those deals: Decagon supports two commercial models.

1. Per-conversation — a flat rate for each incoming customer inquiry the AI handles, charged regardless of whether the issue is resolved, with volume discounts as contact volume grows.
2. Per-resolution (outcome-based) — a higher per-unit price, but charged only when the AI fully resolves a conversation from start to finish without human help. In Decagon’s words: “You pay only when the AI resolves a conversation from start to finish. You don’t pay if the AI fails and the case is passed to a human.”

What makes this different: Decagon is one of the few vendors to publish a defense of outcome-based pricing while still quoting privately. Its glossary frames resolution-based pricing as “value aligned with cost” and an incentive that ties Decagon’s revenue directly to AI resolution rates — but it also openly flags the trade-offs (billing fluctuates month to month, and “what counts as a resolution” is genuinely contested for abandoned chats and partial answers).

Pricing by product

Sales motions across products: Decagon is entirely sales-led — no rate card is published. Third-party analyses (Featurebase, eesel, myAskAI) estimate enterprise deals starting around 95K USD per year — an unconfirmed estimate, not an official Decagon figure.

Hidden costs : What Decagon users actually pay

Because Decagon does not publish rates, the real cost drivers are contractual rather than list-price line items:

• Annual commitment / minimums — enterprise deals are annual; expect a floor commitment regardless of monthly volume.
• Resolution definition — under the per-resolution model, how a resolution is counted (abandoned chats, partial answers, deflections) materially changes the bill. This is the single most important negotiation point.
• Voice vs. digital — voice resolution typically carries different (higher) economics than chat/email.
• Implementation & integration — connecting to a helpdesk, knowledge base and backend systems is part of the enterprise engagement.

Want to estimate your own Decagon bill? Use the Decagon pricing calculator to model per-conversation vs. per-resolution scenarios.

Pricing evolution : Decagon pricing history and changes

Cadence

Tracked range: 2024–present. Decagon has never published a public rate card.

Notable changes

• 2025-06-23 — 131M USD Series C at a 1.5B USD valuation.
• 2026-01 — 250M USD Series D at a 4.5B USD valuation (Coatue, Index Ventures).

What’s unique : Decagon’s distinctive pricing mechanics

1. Choice of meter. Few competitors offer both per-conversation and per-resolution as explicit options — Decagon lets buyers pick predictability (per-conversation) or strict value alignment (per-resolution).

2. Published pricing philosophy. Decagon’s glossary openly argues for resolution-based pricing and names its weaknesses, which is unusual transparency for a vendor that still keeps actual numbers behind sales.

3. Consumer-scale design. The customer base (Chime, Duolingo, Riot Games) signals pricing tuned for very high inbound volume, where per-resolution unit economics compound quickly.

Strengths & weaknesses

Billing UX : Decagon billing controls and transparency

• Billing controls — Enterprise contracts; usage reconciled against the agreed meter (conversations or resolutions). Not self-serve.
• Usage visibility — Decagon provides admin analytics on deflection and resolution rates, which double as the billing basis; buyers should confirm how resolutions are attributed.
• Payment options — Invoiced annual enterprise billing; no public self-serve checkout.

Strategic wins : Why Decagon’s pricing decisions worked

1. Riding the outcome-based wave

By offering per-resolution pricing, Decagon aligns with the dominant 2024–25 shift in AI support away from per-seat licenses. See how AI companies are shifting from per-user licenses and the outcome-based pricing revolution.

2. Selling the philosophy, not just the product

Publishing a glossary that argues for resolution pricing turns Decagon’s billing model into a sales asset — it educates buyers and frames the conversation on Decagon’s terms.

3. Targeting volume-rich verticals

Consumer brands with massive ticket volume make per-resolution economics attractive: small per-unit prices times millions of resolutions yield large, scalable contracts. See choosing the right usage metric.

Areas to improve : Gaps in Decagon’s pricing approach

1. Opacity

No public rates at all forces every prospect into a sales cycle and makes budgeting hard — a friction point covered in bill shock and cost unpredictability.

2. Resolution ambiguity

Decagon itself admits “what counts as a resolution” is fuzzy. Without a published, auditable definition, buyers carry contract risk.

3. Forecastability

Per-resolution billing fluctuates with monthly volume, complicating finance planning versus a flat subscription. For frameworks on this, see the introduction to usage-based pricing.

Key takeaways

1. Decagon is sales-only. No rate card; everything is quoted.
2. Two meters. Per-conversation (usage) or per-resolution (outcome) — buyers choose.
3. Outcome alignment is the pitch. Pay only when the AI resolves; nothing on escalation.
4. Definitions matter. The resolution definition is the key negotiation lever.
5. Built for volume. Economics favor high-throughput consumer support.

UBP implications

1. Outcome-based pricing is going mainstream in AI support — Decagon offering it as a first-class option confirms the trend.
2. The metric is the contract. When you bill per resolution, defining the unit is the entire game.
3. Transparency lags adoption. Even outcome-aligned vendors keep numbers private, slowing buyer trust — a gap UBP practitioners should close.

Sources

• Decagon official website (accessed 2026-06-11)
• Decagon — What is resolution-based pricing? (accessed 2026-06-11)
• BusinessWire — Decagon raises 131M at 1.5B valuation (accessed 2026-06-11)
• Sacra — Decagon revenue, valuation & funding (accessed 2026-06-11)

Bottom line

Decagon sells enterprise AI customer support agents on a sales-only basis with two documented commercial models — per-conversation usage pricing and per-resolution outcome pricing. It publishes no rates, but champions resolution-based billing publicly. With a 4.5B USD valuation and consumer-scale logos, its economics are tuned for high-volume support. Browse the pricing blueprint for fully-researched company profiles.

Want to compare Decagon against other customer-service AI companies? Browse the pricing blueprint.