Decagon Pricing: How Much Does Decagon Cost in 2026?

For years, the world of tech has been conditioned to traditional SaaS pricing models. You pay per seat and the number changes depending on the number of seats, features, and add-ons. But in the world of agentic AI, Decagon believes that pricing should not be benchmarked against the number of users. Instead, they price their product based on the work their AI agents do.

The most important thing to know is: all Decagon AI pricing is fully custom, and we can’t give you an exact quote for a specific use case, type of business, or industry. The second most important thing is that there are two pricing models: per conversation and per resolution.

Let’s decipher how much Decagon costs in 2026.

Decagon’s pricing model has an annual platform fee

According to several sources, including Intercom, Decagon charges an annual platform fee of $50,000 per year, regardless of the pricing model you choose. This means that only one thing is certain, and that Decagon pricing starts at least $50,000 annually, and the rest depends on the model and your unique requirements.

PS. we also have a comparison between Decagon and Intercom on our blog.

Source

Per conversation pricing model

The per-conversation model is the simpler of the two Decagon pricing approaches. Instead of charging based on seats or software access, Decagon charges for every customer interaction that the AI agent handles.

In this setup, a conversation usually refers to a single customer support interaction across channels such as chat, email, or messaging apps. Every time a customer starts a new interaction with the AI agent, that interaction is counted and billed. Costs scale directly with the number of conversations.

Pricing varies depending on volume and contract terms, but public information and industry discussions suggest the cost is roughly around $0.99 per conversation. Large enterprise customers with higher volumes may negotiate lower per-conversation rates.

Here is how this model typically works in practice:

• A customer starts a conversation with your AI support agent
• The AI agent answers questions, retrieves information, or performs actions
• The conversation is logged as a single billable interaction

Unlike seat-based pricing, your costs increase only when customers actually interact with the AI agent. For companies with fluctuating support volumes, this can be easier to predict than hiring additional human agents or paying for unused software seats.

However, there are a few important details to keep in mind:

• The $50,000 annual platform fee still applies before conversation usage costs
• Enterprise contracts often include minimum conversation commitments
• Pricing may vary depending on channels, integrations, and deployment complexity

Because of the platform fee, the per-conversation model tends to make sense primarily for companies handling very large customer support volumes. For smaller teams or startups, the upfront commitment alone can already exceed the entire support software budget.

In short, Decagon’s per-conversation model replaces the traditional per-seat pricing with a usage-based cost tied directly to customer interactions, while still requiring a significant annual platform commitment.

Per resolution pricing model

The second option in Decagon’s pricing model focuses on outcomes rather than activity. Instead of charging for every conversation that starts, the platform charges only when the AI agent successfully resolves a customer issue.

A resolution typically means the AI agent handled the entire request without handing the case off to a human support agent. If the customer inquiry is fully solved by the system, that interaction becomes a billable resolution.

This model shifts the focus from raw interaction volume to the actual effectiveness of the AI agent. In theory, companies only pay when the system completes meaningful work and removes the need for human intervention.

The question remains on what an outcome is, as it’s quite subjective and up for negotiation.

Here is how it usually works in practice:

• A customer reaches out through chat, email, or another support channel
• The AI agent follows predefined agent operating procedures (AOPs) to understand the request
• The system provides a solution and closes the request without human escalation
• The case is counted as a successful resolution and becomes billable

While exact pricing is not publicly disclosed, industry sources suggest that per-resolution pricing is typically higher than per-conversation pricing, since a resolved case represents a completed support task rather than a simple interaction.

And as our CEO has said on one occasion, “Outcome-based pricing only works if ‘resolution’ is consistently defined and measurable across every workflow and channel, which requires strong orchestration and observability so enterprises can scale AI agents without turning each new use case into a separate negotiation.”

The advantage of this structure is that companies pay for outcomes that matter to the business. If the AI agent successfully handles a large percentage of support requests, it can reduce workload for human agents and help teams maintain strong customer satisfaction without expanding their support staff.

However, the same limitations still apply:

• The $50,000 annual platform fee still applies before usage costs
• Definitions of what qualifies as a resolution are usually set in enterprise contracts
• Pricing may vary depending on workflow complexity and integration requirements

For large companies with well-documented support workflows and clear agent operating procedures, the per-resolution approach can make it easier to measure the value the AI system provides. Businesses that rely heavily on automated support often prefer this model because it ties spending directly to completed support outcomes rather than raw conversation volume.

Why neither Decagon AI pricing model works for most businesses

On paper, Decagon’s pricing sounds logical. Pay for conversations or pay for outcomes. But in practice, both models introduce challenges that make them difficult for many companies to adopt.

The biggest issue is the entry cost. Before a single conversation happens or a single issue gets solved, you must pay the $50,000 annual platform fee. That requirement alone removes most startups, mid-sized companies, and even many larger SaaS teams from the equation. Essentially, Decagon is built for enterprise systems and large budgets.

Then there is the unpredictability of outcome-based pricing.

While paying for a resolved conversation sounds fair in theory, the definition of what actually counts as a resolution can become complicated.

If a customer asks several follow-up questions, changes topics, or needs clarification, it may not be clear whether the interaction counts as one resolution or multiple events. In enterprise contracts, these definitions are usually negotiated, which adds another layer of complexity.

Another challenge is the level of preparation required to get value from the system. A conversational AI platform like Decagon does not magically understand a company’s support processes. Teams still need well-documented workflows, knowledge bases, and clearly defined agent operating procedures. Without those foundations, even the most advanced system can struggle to consistently reach a resolved conversation.

The way interactions are structured also affects the customer experience. Some companies worry that pushing automation too aggressively to control costs can create friction. Customers may get stuck in automated loops before reaching a human agent, especially when the system relies heavily on predefined logic and natural language instructions.

For these reasons, Decagon’s pricing structure tends to make the most sense for very large enterprises with massive support volumes and mature support operations. For most businesses, the combination of high platform fees and complex usage-based pricing makes it a difficult solution to justify.

Get predictable AI agent pricing with Quiq instead

If Decagon’s pricing feels difficult to predict, you are not alone. Many companies struggle with the combination of a large platform fee and complex usage models tied to conversations or resolutions.

Quiq takes a different approach.

Instead of focusing entirely on outcome-based pricing, Quiq offers a usage model that is easier to plan around while still aligning costs with the value the system provides. Companies typically purchase annual usage pools based on conversation volume, which creates clearer budgeting and avoids unexpected spikes in cost when support demand suddenly increases.

In other words, you know roughly how much you will spend ahead of time.

Typical investment levels also make the platform more accessible for companies evaluating enterprise AI support solutions. Proof of concept deployments usually fall between $40,000 and $75,000 annually, while full enterprise deployments scale into the low six or seven figures, depending on volume and features.

But pricing is only part of the difference.

Quiq is built as a full customer journey platform rather than just a conversational AI tool. We combine AI agents that handle customer requests directly with AI assistants that help human agents respond faster when an issue needs escalation. This creates a continuous experience where context moves across channels and between AI and humans without forcing customers to repeat themselves.

Teams can also train and control the system using natural language instructions through process guides that reflect their own support workflows and policies. This makes it possible to build AI agents that follow the same procedures human agents would normally use when resolving customer issues.

The goal is simple: reliable resolution without sacrificing customer experience, where AI fully resolves problems without costing you a fortune.

Instead of chasing a single resolved conversation metric, Quiq focuses on maintaining context, executing real actions inside backend systems, and escalating to human agents when needed. The result is an AI system that resolves customer problems while still preserving the quality of the interaction.

For companies evaluating Decagon or similar platforms, that combination of clearer pricing and operational flexibility often makes Quiq the easier solution to deploy and scale. You also get AI-driven analytics to understand what is happening under the hood, without writing code or spending hours on setup, making Quiq easy for non-technical teams.

Get a free demo of Quiq to see how we can help you create better customer experiences without decimating your budget.

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