PolyAI Pricing

PolyAI pricing depends on the product selected, connected call minutes, integration requirements, and enterprise deployment scope. Its managed Agentic Dialog Platform uses per-minute pricing without a published standard rate, while its Dialog-RSN-1 API has a publicly listed rate of $0.02 per connected minute.

For organizations forecasting voice AI costs, call duration and volume are important inputs. Research on streaming speech recognition also highlights how conversational pauses and brief listener responses can affect recognition accuracy, making reliability an important consideration alongside pricing.

This guide explains PolyAI's published pricing, what each offering includes, and the factors affecting enterprise customer service costs in 2026.

Key Takeaways

• PolyAI's managed Agentic Dialog Platform uses per-minute pricing, but does not publish a standard enterprise rate
• Dialog-RSN-1 API costs $0.02 per connected minute, billed per second, with separate text token charges
• API pricing does not include telephony and orchestration, making it different from the managed enterprise platform
• PolyAI supports voice, chat, and messaging, with AI agents connected to customer service systems and business workflows
• Enterprise platform pricing includes operational services, such as performance improvements, maintenance, and support
• Total cost depends on usage and deployment scope, including call duration, integrations, additional services, and ongoing administration

PolyAI Pricing and Approach

PolyAI offers an enterprise platform for building, deploying, and managing AI agents across customer communication channels.

Its official pricing information distinguishes between the managed Agentic Dialog Platform and direct access to its Dialog-RSN-1 model through an API.

PolyAI Agentic Dialog Platform Pricing

PolyAI's managed platform uses per-minute pricing for ongoing voice agent usage. However, the company does not publish a standard per-minute rate for enterprise deployments.

Its published platform offering includes:

• Voice agent usage: Billing based on connected call time
• Performance improvements: Ongoing optimization and maintenance
• Support: Customer support and emergency assistance
• Security: Enterprise security controls and regular testing
• Integrations: Pre-built connections and API-based integrations
• Monitoring: Visibility into agent activity and performance
• Testing: Tools for evaluating agent behavior before and after deployment

The final enterprise price depends on the commercial agreement and deployment requirements.

PolyAI Dialog-RSN-1 API Pricing

PolyAI separately offers Dialog-RSN-1, an audio-native conversational model designed for real-time voice interactions.

Its published API pricing includes:

• Connected audio: $0.02 per connected minute, billed per second
• Input text tokens: $2.00 per million tokens
• Cached input tokens: $0.20 per million tokens
• Output text tokens: $5.00 per million tokens
• Concurrency: A standard allowance for concurrent API calls, with expanded capacity subject to additional arrangements

These rates apply to the model-only API offering.

Important distinction: The $0.02 rate is not an all-inclusive price for PolyAI's managed contact center platform. The API does not include telephony infrastructure or agent orchestration, and applicable text token charges are separate.

What Is Included in Enterprise Platform Pricing?

PolyAI's pricing page identifies ongoing maintenance, performance improvements, security, monitoring, and support as part of its managed offering.

These services are relevant because the total cost of operating an AI agent includes more than inference and call-processing expenses.

However, the public information does not establish a universal implementation fee or a complete standard enterprise contract price.

Factors That Affect PolyAI Pricing

PolyAI's total cost depends on how its AI agents are deployed and used within a customer service operation.

Call Volume and Duration

Because the managed platform uses per-minute pricing, the number and duration of customer conversations influence usage costs.

Important factors include:

• Monthly call volume: The number of conversations handled by AI agents
• Average call duration: Time spent on authentication, inquiries, transactions, and troubleshooting
• Seasonal demand: Changes in volume during holidays, promotions, or service disruptions
• Call complexity: Multi-step conversations requiring additional information or system actions
• Human transfers: How interactions are handled when an AI agent escalates a request

Call-volume forecasts can help estimate usage, but actual expenses depend on the applicable contract rate and billing terms.

Communication Channels

PolyAI began with voice-focused conversational AI and now supports additional digital channels through its Agentic Dialog Platform.

The company's published materials describe support for:

• Voice
• Web chat
• SMS and messaging
• WhatsApp
• Other supported digital experiences

Channel requirements affect the integrations, workflows, and services involved in an enterprise deployment.

PolyAI does not publish one universal price covering every possible channel configuration.

Integration Complexity

PolyAI provides pre-built integrations and APIs for connecting AI agents with existing business applications.

Its official integration directory includes telephony and contact center systems such as Genesys, NICE, Cisco, Five9, Amazon Connect, and Twilio.

It also supports CRM, knowledge management, and other enterprise applications.

The cost of an implementation can depend on authentication, field mappings, data access, workflow actions, and testing requirements.

The Impact of Voice AI on Call Center Pricing

Voice automation is a central capability of PolyAI's platform. Its agents use natural-language understanding, speech processing, and configured workflows to manage customer interactions.

PolyAI Voice AI Capabilities

PolyAI's documented capabilities include:

• Natural-language voice conversations
• Real-time interruption handling
• Caller authentication
• Appointment scheduling and reservations
• Order and account inquiries
• Troubleshooting workflows
• Human-agent transfers with context
• Multilingual interactions

These capabilities allow PolyAI agents to handle both informational inquiries and configured transactional processes.

Telephony Integration and Infrastructure

PolyAI connects to existing telephony systems through supported integrations and APIs.

For example, Session Initiation Protocol (SIP) is a standard used to establish and manage real-time communication sessions. The IETF's SIP specification describes how such connections are initiated and controlled.

PolyAI's published materials describe SIP connectivity and integration with existing contact center infrastructure.

Relevant implementation requirements include telephony compatibility, call routing, authentication, escalation procedures, and access to backend systems.

Implementation and Deployment

PolyAI provides agent-building tools for configuring, testing, and managing conversational AI agents.

Its current platform includes tools for:

• Configuring agent workflows
• Connecting enterprise knowledge and business systems
• Testing conversations
• Managing agent instructions and guardrails
• Monitoring customer interactions
• Updating agents as business requirements change

PolyAI's official materials describe enterprise deployments within weeks, but implementation timelines depend on integration complexity and workflow requirements. No single timeline should be treated as guaranteed for every deployment.

A broader explanation of the AI pilot-to-production process provides context on the technical and operational work involved in production deployment.

PolyAI Security and Compliance

PolyAI publishes enterprise security and data protection information covering its conversational AI platform.

Its official materials identify:

• ISO/IEC 27001 certification
• SOC 2 Type II assurance
• PCI DSS-related controls
• HIPAA-related safeguards
• GDPR-related privacy controls
• Data protection and access management
• Security testing and monitoring
• Configurable guardrails for AI conversations

The exact regulatory obligations applicable to a deployment depend on the information being processed and the jurisdictions involved.

AI Transparency and Customer Interactions

Organizations deploying conversational AI may also need to disclose when customers are interacting with an automated system.

For example, the European Commission's AI transparency guidance explains disclosure obligations for applicable AI systems interacting directly with people in the European Union.

For PolyAI deployments, relevant considerations include customer notification, data handling, identity verification, and appropriate escalation procedures.

Evaluating PolyAI's Customer Results and ROI

PolyAI publishes customer case studies describing voice automation across utilities, hospitality, retail, and other enterprise sectors.

Documented Customer Applications

Pacific Gas and Electric (PG&E): PolyAI's published case study describes a voice agent supporting outage inquiries, billing questions, caller authentication, and routing. The deployment connects with existing contact center and business systems.

Fogo de Chão: PolyAI's published case study describes a multilingual voice agent handling restaurant reservations, guest inquiries, and loyalty program interactions. The implementation includes an OpenTable integration.

These are PolyAI-reported customer applications. They illustrate supported use cases but do not establish independently verified performance outcomes for other deployments.

Interpreting PolyAI's ROI Research

PolyAI commissioned Forrester Consulting to conduct a Total Economic Impact study of its platform.

The study modeled the potential financial effects of voice automation using information collected from PolyAI customers and a composite enterprise organization.

Because the study was commissioned by PolyAI and used modeled assumptions, its financial projections should not be treated as independently verified returns or guaranteed savings.

Metrics for Evaluating Voice Automation

A pricing evaluation should distinguish how many conversations the AI handles from how many customer requests it completes.

Useful measures include:

• Call containment: Calls handled without transfer to a human representative
• Autonomous resolution: Customer requests completed without human intervention
• Average handling time: Time required to process an inquiry
• Human escalation rate: Interactions transferred to support representatives
• Customer satisfaction: Customer feedback on automated interactions
• Cost per resolution: Total relevant costs divided by completed customer requests

The distinction between deflection and autonomous resolution matters because a contained call may still leave the underlying customer issue unresolved.

For PolyAI, these performance measures should be evaluated separately from usage-based billing.

Calculating PolyAI's Total Cost of Ownership

PolyAI's total cost of ownership depends on the selected product, contractual terms, deployment requirements, and ongoing usage.

Main Cost Components

Enterprise teams should account for:

• Platform usage: Connected minutes billed under the applicable enterprise arrangement
• API usage: Model minutes and text tokens when using Dialog-RSN-1 directly
• Telephony: Infrastructure and services not included in model-only API access
• Integrations: Connections to contact center, CRM, and business applications
• Implementation: Agent configuration, workflow development, and testing
• Operations: Monitoring, maintenance, and ongoing workflow updates

PolyAI's managed platform includes operational services described in its public pricing information, while direct API usage requires a different implementation arrangement.

Forecasting Costs for Different Call Volumes

For a per-minute enterprise contract, a basic usage estimate is:

Monthly connected call minutes × Contracted rate per minute

For API-only usage, the calculation must also account for text tokens and separately provided telephony or orchestration services.

Seasonal peaks and changes in average handling time can affect both models.

Measuring Total Value

The financial value of voice automation depends on the actual work completed by AI agents.

Relevant outcomes include reduced human handling of routine inquiries, improved availability during busy periods, and additional capacity for support teams to manage complex requests.

A structured AI agent evaluation process can help connect deployment requirements and measurable outcomes with total operating costs.

Making the Right PolyAI Pricing Decision

PolyAI provides two different commercial approaches: a managed enterprise platform with per-minute pricing and a model-only API with published usage rates.

The managed platform includes services for running and improving agents, while the API offering is designed for organizations building on their own voice infrastructure.

The main pricing considerations are:

• Whether the managed platform or model-only API meets the deployment requirements
• How connected call minutes and text tokens are billed
• Which telephony and integration services are included
• What implementation and ongoing operational work is required
• Whether voice, chat, and messaging capabilities support the intended use cases
• How call containment, autonomous resolution, and customer satisfaction are measured

Maven AGI: Purpose-Built for Autonomous Resolution

Maven AGI provides an enterprise AI agent platform focused on autonomous customer resolution while supporting existing human service teams.

Its overlay architecture connects with established helpdesk, CRM, knowledge, and business systems without requiring infrastructure replacement or data migration.

Key Strengths

• Autonomous Resolution: Completes eligible customer requests through approved actions and connected enterprise workflows
• Unified Reasoning: Uses a shared reasoning engine across chat, voice, email, web, and other supported channels
• Maven Voice: Supports real-time customer calls, interruption handling, workflow execution, and contextual human escalation
• Agent Designer: Enables CX teams to configure, test, monitor, and refine AI agent behavior
• Human-Agent Support: Provides contextual escalation and copilot assistance to help representatives manage complex customer needs
• Enterprise Governance: Includes security controls, monitoring, testing, and independent compliance assessments

Maven AGI's published customer stories include K1x, Mastermind, and Papaya, with applications involving technical support, routine inquiry handling, and customer resolution workflows.

The platform extends the capabilities of existing customer service teams while maintaining human involvement for complex and sensitive interactions.

Request a Maven AGI demo to explore autonomous resolution within an existing support environment.

Frequently Asked Questions

How much does PolyAI cost in 2026?

PolyAI's managed Agentic Dialog Platform uses per-minute pricing but does not publish a standard enterprise rate. Its Dialog-RSN-1 API has a published rate of $0.02 per connected minute, billed per second, with separate text token charges.

Does PolyAI charge per minute or per conversation?

PolyAI describes connected-time billing for its voice offerings. The managed platform uses per-minute pricing, while Dialog-RSN-1 API usage is billed per connected minute with per-second accounting. The API also has separate text token rates.

What is included in PolyAI's enterprise pricing?

PolyAI's managed offering includes voice agent usage alongside maintenance, performance improvements, monitoring, security controls, and support. The exact scope and commercial terms depend on the enterprise agreement.

Does PolyAI support channels other than voice?

Yes. PolyAI supports voice, web chat, SMS, and messaging through its Agentic Dialog Platform. Available channels and integrations depend on the selected deployment configuration.

What factors affect PolyAI's total cost of ownership?

The main factors include connected call minutes, product selection, applicable token charges, telephony infrastructure, integrations, implementation requirements, and ongoing operational work. The managed platform and model-only API have different included services and cost structures.