Fin vs Decagon: Performance Data, Pricing, and Architecture Compared [2026]

Understanding how Fin and Decagon differ across architecture, ownership, workflows, reliability, and long-term scalability.

AI agents are rewriting how customer service operates. Two of the most talked-about solutions today are Fin by Intercom and Decagon.

Both aim to automate complex support queries — but they take fundamentally different approaches in architecture, ownership, reliability, and operational maturity.

Below is the clearest, most actionable comparison for customer service leaders evaluating these platforms.

Fin vs. Decagon: Focused Comparison Table

Resolution Rate vs. Deflection Rate: Why the Metric Matters

When comparing AI agent performance, the distinction between resolution rate and deflection rate is critical.

Resolution rate measures the percentage of conversations where the customer's issue was genuinely solved end to end, with no human follow-up required. Fin only counts conversations as resolved when the customer's problem is actually fixed.

Deflection rate measures the percentage of conversations that never reach a human agent. This includes abandoned conversations, customers who gave up, partial answers that technically addressed the question without solving the underlying issue, and frustrated users who stopped responding.

A 70% deflection rate may include conversations where the customer left unsatisfied. A 70% resolution rate means 70% of customers had their problem solved.

Decagon's own pricing blog acknowledges this ambiguity, noting that their primary pricing model charges per conversation regardless of outcome. Their glossary entry on resolution-based pricing states that "defining what a resolution is can be tricky" and that "gray areas can lead to billing disagreements."

Fin's 67% average resolution rate across 7,000+ customers reflects genuine positive resolutions. Over 20% of Fin customers achieve above 80% resolution rates. The rate improves approximately 1% every month through the Fin Flywheel, a continuous improvement loop that identifies content gaps and optimizes performance automatically.

Pricing: Transparent Outcome-Based vs. Opaque Platform Fee + Usage

Pricing structure reveals a fundamental difference in how each platform aligns cost with value.

Fin: $0.99 per resolution, no platform fee

Fin charges $0.99 per resolved conversation. You pay only when a customer's issue is genuinely resolved. There is no annual platform fee, no minimum commitment for self-serve customers, and no seat charges for Fin usage. Pricing is published and transparent.

Fin works with your existing helpdesk (including Zendesk, Salesforce, Freshdesk, and others) or with Intercom's native helpdesk for the deepest integration.

Decagon: $50,000 annual platform fee + per-conversation or per-resolution charges

Decagon does not publish pricing. Based on publicly available data and third-party analyses, the pricing structure includes:

• A $50,000 annual platform fee before any usage costs
• Per-conversation pricing (the model chosen by the vast majority of Decagon customers, per Decagon's own blog) at approximately $0.99 per conversation, regardless of whether the issue was resolved
• Per-resolution pricing available as an alternative, with at least one reported rate of $0.50 per resolution
• Median annual contracts around $400,000 according to marketplace data
• Custom enterprise quotes that vary by volume, channels, and integrations

The critical difference: Fin's per-resolution model means you never pay for failed interactions. Decagon's per-conversation model (their default) means you pay even when the AI fails and the conversation escalates to a human agent.

Cost Comparison at Scale

With Fin, you pay for 6,700 resolutions out of 10,000 conversations. With Decagon's per-conversation model, you pay for all 10,000 conversations, including the ones that escalate to humans

Architecture: Layered System vs. Monolithic AOPs

Fin and Decagon take structurally different approaches to how AI agents are built, tested, and maintained.

Fin: Layered, Modular Architecture

Fin separates three distinct layers:

• Prompts and Guidance for behavioral control and tone
• Procedures for deterministic, multi-step workflows
• Code and Data Connectors for backend system integration

This separation means each layer can be debugged, tested, and updated independently. A change to a refund workflow does not risk breaking how Fin handles product questions. Teams iterate on one component without side effects across the system.

Fin is powered by the Fin AI Engine, a patented, proprietary architecture that includes custom-trained models (fin-cx-retrieval and fin-cx-reranker) built specifically for customer service. This is purpose-built retrieval, not a wrapper around generic LLMs.

Decagon: Monolithic Agent Operating Procedures

Decagon bundles prompts, logic, actions, and rules into single Agent Operating Procedures (AOPs). AOPs are powerful for defining structured conversational flows, but the monolithic structure has trade-offs:

• Harder to debug when something breaks, because all logic lives in one file
• More brittle as complexity grows, because changes to one part can affect the entire procedure
• Often requires coordination with Decagon's team for advanced AOP modifications

Decagon's AOP Copilot (launched September 2025) helps convert SOPs into production-ready AOPs, and AOP Templates provide starting points for common use cases. These tools lower the barrier to entry, but the underlying architecture remains monolithic.

Self-Managed vs. Vendor-Dependent

Fin is fully self-managed. CX teams, support ops managers, and non-technical staff configure workflows, update knowledge sources, adjust guidance, create Procedures, run Simulations, and deploy changes instantly. No engineering resources required. No vendor tickets. No waiting.

Decagon's model involves vendor collaboration for advanced capabilities. Deployments typically take approximately 6 weeks from discovery to launch, with dedicated Agent Product Managers and Forward-Deployed Engineers from Decagon working directly with your team. Advanced AOP changes, complex integrations, and workflow modifications often require coordination with Decagon's team.

Native Helpdesk vs. Point Solution

Fin operates within a complete customer service platform. AI resolution, human agent workflows, inbox management, knowledge management, ticketing, and reporting all exist in one system. When Fin cannot resolve an issue, it escalates to a human agent with full conversation context, within the same platform.

Decagon has no native helpdesk. Customers must maintain a separate platform (Zendesk, Salesforce, or equivalent) for human agent workflows. This creates:

• Higher total cost of ownership (Decagon fees plus helpdesk subscription)
• Handoff friction when conversations move from AI to human agents across systems
• Fragmented reporting across two platforms
• No unified view of AI and human performance

Decagon's Agent Assist (copilot for human agents) is restricted to Zendesk only. If your team uses Salesforce, Intercom, Freshdesk, or another helpdesk, your human agents do not get AI assistance from Decagon.

Fin works natively with Intercom's helpdesk and integrates with Zendesk, Salesforce, Freshdesk, and other platforms through native integrations.

Channel Coverage

Both platforms support voice, chat, and email. Fin extends coverage to Slack, Discord, and a broader set of social channels.

Security and Compliance

Fin holds ISO 42001 certification for AI governance, the first international standard specifically addressing responsible AI development. This certification is rare among AI agent vendors and provides an additional layer of assurance for enterprises deploying AI at scale.

Fin's hallucination rate of approximately 0.01% is achieved through multi-model resilience across OpenAI, Anthropic, Google, and Intercom's own proprietary models, with automatic switching when any provider degrades.

Customer Migration: Function Health Switched from Decagon to Fin

Function Health, a healthcare company, migrated from Decagon to Fin in a deal worth $1.3M in total contract value, covering 600,000 annual Fin resolutions and 176 Copilot seats. The key drivers for switching:

• HIPAA compliance: Intercom's established HIPAA attestation met their healthcare requirements.
• Unified platform: Seeing the full picture of users within a single system rather than maintaining separate tools.
• Pace of innovation: Intercom's shipping velocity and product roadmap
• Deep insights: Having Intercom as the primary CRM provided richer customer intelligence.

Fin is now live on 100% of Function Health's mobile conversations.

Full Comparison Table

Why Fin Wins for Modern Support Operations

Fin is built for teams that want to move fast, own their AI strategy, and pay only for results. The combination of independently verified superior resolution rates, transparent outcome-based pricing, a layered architecture that non-technical teams can manage, and a native helpdesk that eliminates tool fragmentation creates a structural advantage that Decagon's approach cannot replicate without rebuilding from the ground up.

Decagon serves a specific use case well: large enterprises that prefer a vendor-led, structured implementation model with strong engineering support. If your team has dedicated engineering resources and prefers an outsourced configuration approach, Decagon's hands-on model can work.

For every other team, the data points in one direction. Fin resolves more queries, costs less per resolution, deploys faster, gives your team full control, and operates within a complete platform.

"Fin fundamentally changed our support strategy. It helped us scale instantly, resolve over 50% of conversations, and save more than 1,700 hours in the first month." - Isabel Larrow, Product Support Operations Lead, Anthropic

Frequently Asked Questions

What is the resolution rate difference between Fin and Decagon?

In an independent head-to-head evaluation conducted by Vanta, Fin achieved a 73% resolution rate compared to Decagon's 49%. Fin resolved 1.5x more customer queries. Fin's average resolution rate across 7,000+ customers is 67% and improves approximately 1% every month.

How much does Decagon AI cost?

Decagon does not publish pricing. Based on publicly available data, Decagon charges a $50,000 annual platform fee plus per-conversation or per-resolution usage fees. Median annual contracts run approximately $400,000. Fin charges $0.99 per resolution with no platform fee.

Is Decagon HIPAA compliant?

Decagon offers HIPAA options with Business Associate Agreements (BAAs) for healthcare clients. Intercom holds full HIPAA compliance. Function Health, a healthcare company, migrated from Decagon to Fin citing compliance requirements and unified platform visibility as key factors.

Does Decagon have a native helpdesk?

No. Decagon operates as an AI layer that requires a separate helpdesk platform such as Zendesk or Salesforce for human agent workflows. Fin works within Intercom's native helpdesk or integrates with existing helpdesks including Zendesk, Salesforce, and Freshdesk.

Can I try Fin before committing?

Yes. Fin offers a free trial with no credit card required. Decagon has no self-serve signup, no public trial, and no public documentation. Evaluation requires going through their sales process. Fin is also backed by the Fin Million Dollar Guarantee: if you are not satisfied within 90 days, you receive up to $1M of your Fin spend back.

Which AI agent is better for enterprise?

Fin serves 7,000+ customers including enterprise brands like Anthropic, Lightspeed, and Function Health. Fin holds ISO 42001 AI governance certification, SOC 2 Type II, ISO 27001, and HIPAA compliance.

Independent head-to-head tests consistently show Fin outperforming Decagon on resolution rate. For a detailed evaluation framework, see the AI Agent Blueprint.