Decagon uses custom pricing — no rates are publicly listed. Before requesting a quote, model your baseline: current cost per ticket, investigation rate, agent time per investigated ticket, and total monthly support cost. Without a baseline number, you have no reference point to evaluate whether a custom quote represents value.
What Decagon does
Based on public positioning, Decagon builds AI agents for enterprise customer support. The company is aimed at enterprise and mid-market B2B teams that want AI to participate directly in support operations rather than sit only as a writing assistant beside an agent.
That puts Decagon in the enterprise AI support segment, where buyers are usually comparing complete AI agent deployments, not just copilot features or light triage tools. The pitch is broader than simple chatbot deflection: the product is framed around AI handling customer conversations end to end as part of the support workflow.
That is the useful planning takeaway for a buyer. If you are reviewing Decagon, you are probably not looking for a small add-on. You are reviewing a bigger operating-model decision about how much customer support work you want AI to own directly, how much still belongs with human agents, and where investigation-heavy tickets fit.
Note: For current customer references and case studies, visit decagon.ai directly.
Why Decagon pricing is custom
Enterprise AI support software is rarely priced from one simple public rate card. Scope changes from customer to customer, and the quote usually reflects the size and shape of the deployment. Ticket volume matters, but it is only one input.
Pricing can also change with the number of AI agents deployed, the level of integration work needed, whether the team needs custom API connections or proprietary data pipelines, and how much managed-service help is bundled into the deal. Contract structure matters too. Annual commitments, multi-year agreements, and pilot arrangements can all change commercial terms.
That is why custom pricing is normal here. It is not unusual for enterprise AI software; it is a consequence of bespoke scoping. The implication for buyers is practical: the quote only means something if you already know your own baseline. Without that number, a custom proposal can sound reasonable while still being above the value ceiling your support operation can actually capture.
5 things to model before a Decagon pricing call
Baseline your current cost per ticket
Total monthly support cost ÷ monthly tickets = cost per ticket today. Include agent salaries, tooling subscriptions, and management overhead. Most B2B support orgs run $10-$40 per ticket depending on product complexity and ticket mix.
Separate deflectable from investigation-heavy tickets
Count what percentage of tickets can be answered from knowledge-base content versus what percentage needs active system queries. Your investigation rate drives the value of any AI agent more than raw ticket volume.
Price your investigation time
Average minutes per investigated ticket × agent hourly rate = current investigation cost per ticket. Example: a $45/hour agent rate and a 30-minute average investigation equals $22.50 per investigated ticket.
Model your savings ceiling
If AI cuts investigation time from 30 minutes to 10 minutes, savings = 20 minutes × $45/hour ÷ 60 = $15 per investigated ticket. Multiply that by monthly investigation count. That is your ROI ceiling on investigations alone.
Benchmark against alternatives before the call
Before the Decagon call, run the same math for per-resolution pricing, per-investigation pricing, and per-seat pricing. Intercom Fin at $0.99 per resolution, Altor's usage-based investigation model, and Zendesk or Kustomer seat pricing give you a floor and ceiling for evaluating a custom quote.
Questions to ask on a Decagon pricing call
Every enterprise AI support vendor should be able to answer clear billing and scope questions before you move into procurement. Use the call to pin down how pricing works in practice, not just how the product is positioned.
- How is billing structured — per ticket, per resolution, per agent, or custom?
- What counts as a handled or resolved conversation for billing?
- What happens to tickets the AI cannot resolve — are they counted in billing?
- Is there a minimum volume commitment or contract floor?
- What does the implementation and onboarding scope include?
- How does pricing change as volume grows or shrinks?
- Is there a pilot or proof-of-concept pricing option?
These questions are not Decagon-specific. They are the base checklist for any enterprise AI support purchase.
How Decagon compares on pricing structure
Without public Decagon rates, the best comparison is structural. Ask what variable each vendor bills against, what buyer profile it suits best, and which numbers are public enough to model before a call.
| Vendor | Pricing model | Published starting point | Best for |
|---|---|---|---|
| Decagon | Custom enterprise | Not published | Enterprise AI agent deployment |
| Intercom Fin | Per resolution | $0.99/resolution | Teams on Intercom, high deflection |
| Altor | Per investigation | Usage-based (contact for quote) | B2B technical support, investigation-heavy |
| Kustomer IQ | Per agent | $89/agent/month | DTC/ecommerce on Kustomer |
| Zendesk AI add-on | Per agent | ~$50/agent/month | Teams on Zendesk |
Note: Decagon's pricing is not listed because it is not publicly available. All other figures are from published vendor pages.
Decagon alternatives if the quote doesn't fit
If Decagon's quote is above the savings ceiling from Step 4, compare alternatives at lower or narrower price points. Some tools are built for deflection, some for investigation, and some for seat-based AI add-ons inside an existing help desk.
The key question is fit, not just price. Decagon, Altor, and Intercom Fin solve different versions of the support cost problem. If you want a side-by-side view for mid-market buyers, see the full comparison below.
Build your baseline before the sales call
Once you know your current cost per ticket and investigation savings ceiling, custom pricing becomes much easier to judge.
FAQ
How much does Decagon cost?
Decagon does not publish pricing. All pricing is custom and requires a sales conversation. Before requesting a quote, build a baseline model of your current cost per ticket, investigation rate, and monthly support spend. That gives you a concrete number to evaluate any vendor quote against.
Is Decagon cheaper than Intercom Fin?
There is no published data to compare directly. Intercom Fin charges $0.99 per resolution — a number you can model. Decagon pricing is custom. To compare, you need a Decagon quote and your own baseline model. Use the AI Support ROI Calculator to model the Fin side first.
What companies use Decagon?
Based on public information, Decagon serves enterprise and mid-market B2B tech companies. For current customer references, visit decagon.ai directly.
What is the difference between Decagon and Altor?
Based on public positioning: Decagon deploys AI agents for customer support broadly. Altor is focused on investigation — querying production systems such as ClickHouse, Stripe, Linear, and GitHub to diagnose technical support tickets. They serve different primary use cases. For pricing: Decagon is custom; Altor is usage-based per investigation.