Decagon Pricing and Plans : Costs, Features, and Best Alternative

Choosing the right AI agent platform isn’t just about features—it’s also about finding transparent, scalable pricing that matches your business needs. Decagon has emerged as a player in customer support automation, but its pricing model can be tricky to evaluate, especially for teams scaling fast.

In this guide, we’ll break down Decagon’s pricing and plans, highlight its pros and cons, and compare it to leading alternatives like pagergpt so that you can make a confident decision.

Decagon is a conversational AI platform purpose-built for customer support and automation. It positions itself as the next-generation enterprise AI agent platform, focusing on orchestration across large language models (LLMs), retrieval-augmented generation (RAG), and automation workflows.

Its core capabilities include:

• Agent Operating Procedures (AOPs): Structured workflows that let AI agents follow company-specific logic and business rules.

• AI-powered automation: Prebuilt and customizable flows for faster deployment and resolution.

• Customer engagement tools: Designed to handle high-volume conversations, escalate to humans when needed, and ensure consistent responses.

Decagon’s heavy emphasis on compliance, orchestration, and enterprise features makes it well-suited for mid-market and large enterprises that need secure, scalable conversational AI. While it can support startups and SMBs, its enterprise-first design means smaller businesses may find it complex or costly compared to lighter-weight alternatives.

Botpress is a modular platform for building and deploying autonomous AI agents using large language models (LLMs). Unlike many plug-and-play chatbot platforms, Botpress is designed with a developer-first mindset, offering deep customization, JavaScript-level control, and flexible integrations.

The platform combines a visual studio for agent design which lets non-technical teams build conversational flows—with advanced features like the LLMz autonomous engine for orchestration, memory handling, and logic. It supports multi-channel deployment across Slack, WhatsApp, Messenger, and web chat, and comes with enterprise-grade compliance (SOC 2, GDPR, RBAC, SSO).

Decagon doesn’t follow the traditional SaaS playbook of charging per user seat. Instead, it prices its AI agents as if they were virtual employees you pay for the work they perform, not for how many team members you have.

This means your costs scale directly with how much you rely on the AI. For some companies, that’s a fair model. For others, especially fast-scaling teams, it can introduce unpredictability.

Decagon offers two main pricing structures:

Per-conversation pricing

In this model, you pay every time the AI engages with a customer.

How it works:

• Every interaction counts, even if the AI doesn’t fully solve the issue and escalates to a human agent.

• For example, if your AI handles 10,000 conversations in a month, you’re billed for 10,000 conversations.

Per-resolution pricing

Here, you only pay when the AI successfully resolves a ticket without human help.

How it works:

• If the AI fully handles 6,000 out of 10,000 monthly tickets, you pay for those 6,000 resolutions only.

• You don’t pay for escalations or partial answers.

What shapes Decagon’s final price?

Beyond the base pricing model, several factors influence your bill:

• Ticket volume: The more tickets you handle, the higher the bill, though large enterprises often negotiate discounts.

• Workflow complexity: A basic Q&A bot is often more cost-effective than workflows for handling refunds, IT incidents, or HR tasks that require multiple steps.

• Onboarding fees: While not always highlighted, many enterprises face upfront implementation or training costs.

• Resolution rules: How Decagon defines “resolved” tickets can affect whether your costs feel fair.

Hidden costs and add-ons to look for

At a glance, Decagon’s pricing seems simple per conversation or per resolution but the contract details determine what you’ll really pay.

• Unresolved conversations still bill under per-conversation pricing

If your agent engages a user but escalates to a human, that interaction still counts as a billable attempt in the per-conversation model. This is explicitly described by third-party comparisons of Decagon’s pricing models. 

Definitions matter under per-resolution pricing

Decagon uses outcome-based (resolution) pricing, but you’ll need to pre-define what “resolved” means in your contract. Ambiguity here can change what gets billed. Decagon’s own glossary and independent writeups stress that resolution must be clearly scoped.

• Custom quotes = variability in the total cost

Decagon doesn’t publish list prices; most customers get bespoke quotes. Reviews and vendor analyses note that this lack of public pricing can make budgeting and comparisons difficult.

• Enterprise-level spend is common

Marketplaces and reviews indicate large, enterprise-scale contracts (with big ranges). Examples include a median annual contract value around ~$400k (Vendr) and reported ranges from ~$95k to ~$590k+ per year in third-party reviews. Your mileage will vary, but it’s a useful signal of scale.

• SLA/support tiers may affect price

Outcome/usage models are often paired with enterprise SLAs (response times, uptime) and premium support; while Decagon doesn’t list public fees, industry coverage of its model places it squarely in this outcome-based, enterprise tiering. Confirm SLA scope in your quote.

Decagon positions its AI agents as autonomous workers, designed to fully resolve customer tickets and reduce reliance on human agents. Instead of listing seat-based tiers, Decagon bundles a set of enterprise-ready capabilities that scale with usage.

Here are the standout features that define Decagon’s offering:

• AI Agents

Purpose-built agents that act like digital teammates, handling conversations end to end and escalating only when necessary.

• Agent Operating Procedures (AOPs)

Structured playbooks that tell agents exactly how to operate within your business context. This ensures consistent and compliant responses.

• AOP Copilot

A design companion that helps teams create, test, and refine their AOPs, reducing setup time and making automation easier to scale.

• Watchtower

A real-time monitoring and observability system for agent behavior. It gives businesses visibility into how agents make decisions, providing trust and oversight.

• Agent Assist

When agents can’t resolve a ticket fully, they act as copilots for human teammates—suggesting responses, surfacing knowledge, and reducing handle times.

• Experiments

Tools to test and optimize workflows, enabling businesses to measure resolution rates and continuously improve automation performance.

• Integrations

Prebuilt connections to popular helpdesks (Zendesk, Freshdesk), CRMs (Salesforce), e-commerce platforms (Shopify), and ITSM tools (Jira, ServiceNow).

• Analytics & Reporting 

Metrics for resolution rate, escalation rate, deflection, and customer satisfaction (CSAT), allowing teams to track ROI and performance.

• Enterprise-grade compliance & deployment

SOC 2, ISO 27001, and GDPR compliance, along with multi-region deployment and dedicated enterprise support.

Like any outcome-based model, Decagon’s approach has both strengths and trade-offs. Instead of charging per seat, it focuses on conversations and resolutions — which can be powerful for enterprises, but tricky for smaller teams.

Pros

• Pay for value, not seats

Traditional SaaS tools charge per user, which often feels misaligned with actual usage. Decagon’s pricing reflects the work its AI agents perform, making it easier for enterprises to tie spend directly to output.

• Outcome-based flexibility

The per-resolution model means you only pay when the AI fully handles a ticket. For teams with high automation rates, this can drive clear ROI.

• Scales with business needs

Whether you handle 10,000 or 1 million tickets, the pricing grows in proportion to your conversation or resolution volume — no artificial limits on seats or agent licenses.

Cons

•  Ambiguity in “resolution” definitions

Since resolution criteria are defined in contracts, costs can vary depending on what counts as “solved.” Without careful scoping, you may end up paying for partial answers.

• Paying for failed attempts

In the per-conversation model, unresolved or escalated interactions are still billed — meaning you may pay for work that doesn’t reduce agent workload.

• Opaque and enterprise-heavy

Decagon doesn’t list public pricing, so every company has to go through a sales process for a custom quote. This lack of transparency can frustrate SMBs and startups looking for simple, self-serve options.

• High total cost of ownership

Enterprise contracts often involve professional services, onboarding fees, and premium SLAs. Combined with usage-based billing, costs can escalate faster than expected.

​​One of the biggest differences between Decagon and pagergpt lies in how they price their platforms.

Decagon uses an outcome-based model : You’re billed either per conversation or per resolution. While this may appeal to enterprises comfortable with custom contracts, it often leaves teams with uncertain budgets. For example, you could end up paying for escalated conversations in the per-conversation model, or face ambiguity around what qualifies as a “resolved” ticket in the per-resolution model.

pagergpt takes a simpler approach. Pricing is predictable and session-based:

• A session is a conversation where a user can ask multiple questions until it naturally ends.

• Plans scale based on session volume, so you know exactly what you’re paying for each month.

• There are no surprises — failed resolutions or escalations don’t inflate your bill.

pagergpt Pricing (as of 2025)

• Magic Plan – Free forever

  • 100+ sessions per month

  • 1 chatbot, unlimited admins/agents

  • Includes branding removal, lead capture, live chat inbox, website/file/app training

• Business Plan – $349/month

  • 1,000 sessions per month

  • 2 chatbots, 5 admins/agents

  • Includes live chat inbox, integrations, onboarding support, advanced training sources

• Enterprise Plan – Custom pricing

  • Flexible sessions and chatbot numbers

  • Enterprise-grade security (ISO 27001, SOC 2, GDPR)

  • RBAC, MFA, SSO (coming soon)

  • Dedicated customer success manager and priority support

Why pagergpt’s pricing feels more flexible

• Free and paid tiers: Start with the Magic Plan (100+ sessions free) before moving to Business or Enterprise plans as you grow.

• Transparent inclusions: Features like training on websites, documents, and apps; live chat inbox; integrations; and branding removal are built into plans, not hidden behind add-ons.

• Scalable: As your business grows, you can easily upgrade to higher tiers without renegotiating contracts.

👉 Check pagergpt’s pricing plans to see how they compare with Decagon’s outcome-based model.

Decagon vs pagergpt: Pricing-based comparison

Both Decagon and pagergpt help businesses automate support, but they take very different approaches. Let’s break it down category by category.

1. Pricing model

Decagon charges per conversation or per resolution. While this sounds flexible, it often makes forecasting difficult — especially when escalations or vague “resolution” definitions affect costs. Enterprises with high budgets can absorb this, but SMBs and mid-market teams struggle to predict spend.

pagergpt, on the other hand, uses session-based pricing. One session equals a complete customer interaction, and pricing scales in transparent tiers (from the free Magic Plan to Business and Enterprise).

Why pagergpt wins: Predictable, transparent, and flexible pricing that lets businesses scale without financial surprises.

Workflow design

Decagon relies on Agent Operating Procedures (AOPs) and AOP Copilot to structure workflows. These are powerful, but require setup effort and technical oversight to get right.

pagergpt makes this simpler with its no-code/low-code Agent Studio. Teams can design and launch AI agents in minutes without needing specialized training.

Why pagergpt wins: Faster time-to-value and easier adoption for non-technical users.

Monitoring and optimization

Decagon offers Watchtower for real-time observability and Experiments for A/B testing. These are valuable for enterprises that need to closely audit and fine-tune performance.

pagergpt includes AI analytics dashboards by default — tracking resolution rates, sentiment, CSAT, and performance metrics straight out of the box.

Why pagergpt wins: Actionable insights are built-in, with no extra contract layers required.

Human + AI collaboration

Decagon provides Agent Assist, where AI supports human agents by suggesting responses during escalations.

pagergpt goes further with a Shared live inbox, where AI and human agents collaborate seamlessly in real time. This creates a unified experience for both support teams and customers.

Why pagergpt wins: Stronger hybrid support model that blends AI efficiency with human oversight.

Integrations and coverage

Decagon integrates with CRMs, helpdesks, and IT systems like Salesforce, Zendesk, Jira, and Shopify — covering standard enterprise needs.

pagergpt extends beyond customer support, connecting with HR tools (Workday, BambooHR), IT platforms (ServiceNow, Jira Service Management), and communication apps (Slack, Teams, WhatsApp, Messenger).

Why pagergpt wins: Broader coverage across departments — making it useful not only for CX teams but also HR, IT, and operations.

Security and compliance

Decagon meets enterprise compliance requirements with SOC 2, ISO 27001, and GDPR certifications.

pagergpt matches those and adds HIPAA readiness, RBAC, MFA, and enterprise-grade governance controls.

Why pagergpt wins: More comprehensive compliance, opening the door for use in regulated industries like healthcare and finance.

Final verdict:

• Decagon: Best suited for large enterprises that want outcome-based pricing and advanced observability.

• pagergpt: The better choice for SMBs, mid-market, and enterprises that value predictable session-based pricing, faster deployment, broader integrations, and stronger compliance.

👉 Explore pagergpt’s features and see why it’s the smarter alternative to Decagon.

Decagon and pagergpt both promise to transform how businesses handle support, but they approach the problem in very different ways. Decagon leans into an enterprise-first model, treating AI agents like virtual employees and charging per conversation or resolution. For global companies with big budgets and complex oversight needs, that model makes sense. But it also comes with hidden variables — escalations, ambiguous “resolutions,” and custom contracts that make costs harder to predict.

pagergpt takes a different path. Its session-based pricing means you always know what you’re paying for. A session is a complete customer interaction, no fine print attached. Combine that with a no-code Agent Studio, broad integrations across CX, HR, and IT, and enterprise-grade compliance, and you get a platform that’s not only easier to adopt but also easier to scale.

So which is right for you? If you’re a large enterprise chasing deep observability and you don’t mind contract-heavy pricing, Decagon could fit. But if you want predictability, flexibility, and faster ROI, pagergpt is the clear choice, whether you’re an SMB getting started or an enterprise that wants automation without the complexity.

👉 Ready to see the difference? Try pagergpt free today or book a demo to explore how AI Agents can transform your support experience.