Decagon is a great fit for enterprise contact centers with six-figure budgets. For SMB and mid-market teams, Chatsy is the saner pick. Honest comparison.
TL;DR:
- Decagon (founded 2023, raised over $135M from Sequoia, Bain, and a16z) builds custom AI agents for enterprise contact centers. Pricing is custom and typically lands in the $50K to $500K+ per year range.
- Implementation runs four to twelve weeks because Decagon trains models on each customer's data and wires deep integrations into Salesforce, Zendesk, Kustomer, and others.
- If you are a 1,000+ employee company with a real procurement process and a CX leader who can run a vendor evaluation, Decagon is one of the best products in the category. Bilt, Eventbrite, Notion, and Substack are public customers.
- If you are 5 to 500 employees, want to deploy in under an hour, and need transparent pricing under $500/month, Decagon is overkill. Chatsy is the saner pick. Free plan, $35 to $475 monthly tiers, model choice across GPT-5.2, Claude Opus 4.6, and Gemini 3 Pro.
Decagon is genuinely good. We are not going to pretend otherwise. The honest question is whether you are the right buyer.
This article exists because the SERP for "Decagon alternative" is dominated by competitor listicles from fin.ai, Cresta, and Assembled, all of which trash Decagon in service of their own pitch. That is not useful if you are actually trying to decide. Here is the honest version.
Decagon was founded in 2023 by Jesse Zhang and Ashwin Sreenivas, both ex-quant. They raised a $35M Series A from Andreessen Horowitz and a $65M Series B from Bain Capital Ventures, then a Series C reported by The Information at over $100M in late 2024 (Sequoia leading). Total funding now sits north of $135M, which puts them firmly in the enterprise-only zone of the AI customer support market.
Three things they do better than almost anyone:
Custom-trained agents per customer. Decagon does not give you a generic chatbot with a knowledge-base layer on top. They train an agent on your historical ticket data, your tone, your product taxonomy, and your specific escalation rules. The outputs are noticeably better than a generic RAG approach, especially in narrow verticals like fintech onboarding or marketplace dispute resolution.
Deep integrations with enterprise stacks. Salesforce Service Cloud, Zendesk, Kustomer, Gladly, and proprietary in-house ticketing systems all get first-class connector treatment. The Decagon agent can read account state, update CRM fields, kick off workflows, and write back ticket metadata in ways most chatbot products simply cannot.
A real implementation team. Every Decagon contract comes with solutions engineers who sit with your CX team for the first month. This is not self-serve. It is closer to how Palantir or early Snowflake sold: a vendor that lives inside your operations until the system works.
The customer logos back this up. Bilt Rewards uses Decagon for member support. Eventbrite uses them for organizer support. Notion uses them on parts of their support footprint, alongside Intercom. Substack has talked publicly about deploying Decagon for writer escalations.
This is the right product for a specific buyer.
The buyer profile breaks down fast outside that enterprise band. Three honest problems:
Pricing is opaque and expensive. Decagon does not publish pricing anywhere on decagon.com. Every reported contract size we have seen from public sources, podcast appearances by founders, and Reddit threads in r/CustomerSuccess (notably a March 2026 thread titled "anyone using Decagon, what's it actually cost?") puts annual contracts in the $50,000 to $500,000+ range. The floor is typically $5K to $8K per month for a single agent on a small enterprise account, and it scales from there.
Implementation is slow. Four to twelve weeks is normal. A 2025 case study published on the Decagon site for a fintech customer described "an eight-week implementation, with a four-week training phase followed by phased rollout." That is fine if you are replacing a 30-agent contact center. It is not fine if you are a five-person SaaS team trying to launch chatbot support next Tuesday.
SMB economics do not work. If your support volume is under 5,000 tickets per month and your team is under 20 agents, the deflection savings cannot pay back Decagon's contract value. The math only works at scale, which is exactly how Decagon's go-to-market is structured.
None of this is a flaw in Decagon. They built a product for a specific buyer and priced it accordingly. It is a flaw in the buyer-vendor match if you are not that buyer.
Chatsy is the opposite of Decagon on most of these axes, and that is deliberate.
Transparent pricing on the website. Plans run $0 (Free, 40 credits per month), $35/month (Hobby, 500 credits), $140/month (Standard, 4,000 credits, 3 seats), and $475/month (Pro, 15,000 credits, 5 seats). Enterprise is custom but starts well below Decagon's floor. Every paid plan includes AI agents, live chat, mailbox, knowledge base, and AI drafts. No per-resolution fees. No per-seat surcharge for adding a teammate within plan limits.
Self-serve setup in under an hour. You sign up, point Chatsy at your help center URL or upload your docs, pick a model, and embed the widget. Most customers go live the same day. There is no four-week training phase because Chatsy uses RAG over your content rather than fine-tuning a custom model per customer.
Model choice across providers. GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, o3, DeepSeek V3.2, Llama 3.3 70B, and roughly 25 others. Cost varies by model (1 to 5 credits per response). Decagon, by contrast, runs its own custom stack and you do not choose the underlying model.
Built for SMB and mid-market. Most Chatsy customers are between 5 and 200 employees. The product, the pricing, and the documentation are all designed around that buyer.
The honest tradeoff: Chatsy does not custom-train a model on your historical ticket data. If you are a $500M revenue fintech with 100,000 monthly tickets and a clear pattern of complex multi-step disputes, the generic-RAG approach will hit a ceiling. Decagon will not. That is the tradeoff.
| Dimension | Decagon | Chatsy |
|---|---|---|
| Pricing | Custom, typically $50K-$500K+/year | $0 to $475/month transparent, custom Enterprise |
| Setup time | 4 to 12 weeks with solutions team | Under 1 hour, self-serve |
| AI model approach | Custom-trained per customer | RAG over your content, model choice (GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, plus 25+ others) |
| Self-serve signup | No, contact sales only | Yes, free plan available |
| Implementation team | Yes, included | No, self-serve docs and chat support |
| Best for company size | 1,000+ employees | 5 to 500 employees |
| Public customer profile | Bilt, Eventbrite, Notion, Substack | SMB SaaS, ecommerce, consultancies, agencies |
| Per-resolution fees | Included in contract | None, credit-based |
| CRM integrations | Deep: Salesforce, Zendesk, Kustomer, Gladly | Standard: Zapier, webhooks, helpdesk APIs |
| Multi-channel | Web chat, email, SMS, voice (with partners) | Web chat, email mailbox, knowledge base |
| Founded | 2023 (Jesse Zhang, Ashwin Sreenivas) | 2023 |
| Funding raised | $135M+ (a16z, Bain, Sequoia) | Bootstrapped |
Be honest about whether you are this buyer:
If three or more of those apply, Decagon is probably the right call. Run the eval.
Most teams reading this article:
If three or more of those apply, Chatsy is the right call.
We are going to be honest here because dishonest disclaimers are how SaaS marketing got into this mess:
If any of those are deal-breakers, do not buy Chatsy. Decagon, Salesforce Service Cloud, or Zendesk Suite Enterprise are better fits.
The most-cited Decagon competitors in 2026 are Sierra (Bret Taylor's company, also enterprise AI agents), Cresta (originally agent-assist, now AI agent platform), Ada (longer-running enterprise chatbot platform), and Forethought. On the mid-market side, Intercom Fin and Zendesk AI agents compete on lighter setup. On the SMB side, Chatsy, Chatbase, and Eesel AI compete on transparent pricing and self-serve setup. Fin.ai, Cresta, and Assembled all publish "Decagon alternative" listicles, which is itself a signal that Decagon's win rate in enterprise deals is high enough to attract that content.
Sierra was founded by Bret Taylor (ex-Salesforce co-CEO, ex-Twitter chair) and Clay Bavor (ex-Google VR lead) in 2023. Both Sierra and Decagon target enterprise AI agents with custom training and deep integrations. The practical difference: Sierra has stronger brand recognition and a bigger sales motion thanks to Taylor's network and customers like ADT, SoFi, and WeightWatchers. Decagon tends to win in fintech, marketplace, and consumer subscription verticals. Both price in the same range. The choice usually comes down to which team's solutions engineers your CX leader trusts more.
Decagon does not publicly disclose which foundation model they use, and the answer is "it depends." They run a multi-model stack with custom fine-tuning per customer. Reporting from Jesse Zhang's appearances on podcasts (notably the No Priors episode in 2024) suggests they use a mix of OpenAI, Anthropic, and open-source models depending on the task. The selling point is that you do not have to think about it. The drawback is that you cannot change it.
Probably not. The economics of Decagon assume you have a contact center of at least 25 to 50 agents and ticket volume above 5,000 per month. A 100-person company with 3 to 5 support agents and 1,000 to 3,000 tickets per month will not recoup a $60K+ annual contract through deflection savings. At that scale, Chatsy, Intercom Fin, or even self-hosted Rasa make more sense. Save Decagon for when you are 500+ employees with a CX team that has its own procurement budget.
Decagon is one of the best AI customer support products built since 2023. If you are an enterprise buyer with the budget and patience for a real implementation, you should evaluate them seriously.
If you are reading this because someone forwarded you a deck about AI support automation and you want to see what is actually available at your scale, start with Chatsy. The free plan is genuinely free, the paid tiers are on the pricing page, and you can be live with an AI agent on your site this afternoon. If you outgrow it, you can switch to Decagon or anyone else. There is no contract to escape.
Maven AGI is a serious mid-market play backed by Lux and M13. Chatsy is the saner pick for smaller teams. Honest comparison with pricing, fit, and tradeoffs.
