PolyAI vs Cognigy: Honest Comparison (2026)

PolyAI is an enterprise customer-service voice AI platform focused on contact-centre automation; Cognigy is an enterprise customer-service voice AI platform with conversational AI across voice and digital channels.

Major takeaways

Who should pick PolyAI over Cognigy?
Teams that need a voice-first platform optimised for high-volume customer-service calls with pre-built telephony and minimal setup.

Who should pick Cognigy over PolyAI?
Teams that need a unified conversational AI platform spanning voice, chat, and messaging channels with flexible integration options.

How do their pricing models differ?
PolyAI reportedly uses per-minute pricing with bundled telephony; Cognigy uses platform licensing plus usage-based voice charges, with pricing not publicly listed for either.

If you care about X, pick Y

How we evaluated

This comparison draws from vendor documentation published on polyai.com and cognigy.com, review coverage on G2, TrustRadius, and Capterra, and third-party pricing data from Vendr. We examined build models, telephony options, language support, compliance statements, and integration capabilities as documented by each vendor. No test calls were placed. No agents were built. Performance claims are reported as vendor-stated figures. This article was last updated in October 2026.

What each platform does

PolyAI

PolyAI is an enterprise customer-service voice AI platform designed for contact centres handling high call volumes. The platform is built around pre-configured voice agents that handle inbound customer-service conversations across industries including hospitality, banking, and retail.

PolyAI reportedly provides bundled telephony infrastructure. Teams get phone numbers and carrier connectivity without separate setup. The platform focuses on natural conversation flow, interruption handling, and warm transfer to human agents when needed. PolyAI states it supports 50+ languages and deploys agents through a managed-service model with dedicated implementation support.

The platform is positioned for operations teams that want to deflect routine calls without building voice flows from scratch.

Cognigy

Cognigy is an enterprise conversational AI platform that spans voice, chat, messaging, and digital channels. The platform offers a visual flow builder called Cognigy.AI that lets teams design conversation logic without code, plus API access for developers who need programmatic control.

Cognigy's voice capabilities sit within a broader conversational AI suite that includes chatbots, WhatsApp, SMS, and web-chat integrations. The platform reportedly supports 100+ languages and allows teams to bring their own LLM or use Cognigy's hosted models. Cognigy is positioned for enterprise contact centres and customer-experience teams that need a unified conversational AI layer across multiple touchpoints.

The platform includes analytics, intent recognition, and integration with major CRM and contact-centre systems.

Feature comparison

Pricing and cost per minute

PolyAI pricing model

PolyAI does not publish pricing on its website. Based on third-party sources including Vendr, PolyAI reportedly uses a per-minute pricing model with bundled telephony. Estimated per-minute rates range from $0.10 to $0.25 depending on volume and contract terms, though these figures are unverified and likely vary by deployment.

The per-minute rate reportedly includes telephony, speech-to-text, text-to-speech, and LLM inference. Teams pay a single all-in rate per minute of conversation. PolyAI typically requires an annual contract with minimum monthly spend commitments.

Implementation and managed-service support are reportedly priced separately as professional services fees. Teams should request a detailed quote that breaks out per-minute rates, minimums, and any setup or support charges.

Cognigy pricing model

Cognigy does not publish pricing on its website. Based on third-party sources including Vendr, Cognigy reportedly uses a platform licensing model with usage-based voice charges. Platform licenses are estimated in the range of $50,000 to $150,000+ annually depending on features, channels, and user seats, though these figures are unverified.

Voice usage is reportedly billed separately on a per-minute basis, with estimated rates ranging from $0.05 to $0.15 per minute depending on volume and whether the customer brings their own telephony provider. The per-minute rate may or may not include speech-to-text, text-to-speech, and LLM costs depending on the configuration; teams using bring-your-own LLM models will incur separate inference charges.

Cognigy typically requires annual contracts with minimum spend commitments. Professional services for implementation and training are priced separately.

What a month of calls might cost

Estimating monthly voice AI spend requires knowing your call volume, average call length, and the all-in per-minute rate for your configuration. The formula is:

Monthly cost = (average call length in minutes × calls per month × per-minute rate) + platform fees + telephony charges + overage fees

For example, if a team handles 10,000 inbound calls per month at an average length of 3 minutes per call, that's 30,000 minutes per month. Using an illustrative assumption of $0.15 per minute all-in, the usage charge would be $4,500 per month. If the platform charges a separate monthly license fee of $5,000, the total would be $9,500 per month.

These are illustrative figures only. Actual costs depend on negotiated rates, contract terms, and whether telephony, speech services, and LLM inference are bundled or billed separately. Teams should request a detailed cost breakdown from each vendor that specifies what the per-minute rate includes, any platform fees, and how overages are handled.

What users say

Public review volume for PolyAI and Cognigy is limited; the recurring themes below are drawn from the available G2, TrustRadius, and Capterra coverage.

PolyAI user themes

Reviewers on G2 and TrustRadius note that PolyAI's managed-service model reduces the technical lift for contact-centre teams. Users report that the platform handles high call volumes and interruptions well, with natural conversation flow that reportedly reduces customer frustration compared to traditional IVR systems.

Some reviewers cite the lack of self-service configuration options. Changes to agent behaviour require coordination with PolyAI's support team rather than in-platform editing. Pricing transparency is flagged as a concern, with multiple users reporting that per-minute costs and contract terms were not clear until late in the sales process.

Cognigy user themes

Reviewers on G2 and Capterra note that Cognigy's visual flow builder makes it accessible to non-technical teams. Users report that the platform's multi-channel capabilities allow them to deploy the same conversation logic across voice, chat, and messaging without rebuilding flows.

Some reviewers cite a steep learning curve for advanced features. Teams need dedicated training to take full advantage of the platform's integration and customisation options. Performance in high-latency network environments is flagged as a concern by some users, particularly for voice deployments in regions with limited infrastructure.

Strengths and gaps

What PolyAI does well

Bundled telephony infrastructure. PolyAI provides phone numbers and carrier connectivity as part of the platform, eliminating the need for teams to source and integrate separate telephony providers.

Interruption handling. The platform reportedly uses proprietary barge-in technology that allows callers to interrupt the agent mid-sentence without breaking conversation flow, which users cite as a key differentiator.

Managed-service model. PolyAI's implementation and support approach reduces the technical burden on contact-centre teams that lack in-house voice AI expertise.

Natural conversation flow. Reviewers note that PolyAI agents handle complex, multi-turn conversations more naturally than traditional IVR systems, reportedly improving customer satisfaction.

Where PolyAI has gaps

PolyAI requires coordination with the vendor's support team to adjust agent behaviour or conversation flows. Teams cannot make changes directly in the platform.

PolyAI is primarily built for inbound customer-service calls. Outbound campaign capabilities are reportedly limited compared to platforms designed for sales or appointment-setting use cases.

PolyAI does not publish pricing. Multiple third-party sources note that per-minute rates and contract terms are not disclosed until late in the sales process.

Teams that want to use custom TTS voices or bring their own LLM models reportedly have limited options compared to more developer-focused platforms.

What Cognigy does well

Multi-channel conversational AI. Cognigy supports voice, chat, messaging, and digital channels in one platform, allowing teams to deploy consistent conversation logic across touchpoints without rebuilding flows.

Visual flow builder with API access. The platform offers a low-code interface for operations teams plus API and webhook access for developers who need programmatic control.

Bring-your-own LLM support. Cognigy allows teams to integrate external LLM providers or use Cognigy's hosted models, giving flexibility for teams with specific model requirements or cost constraints.

Extensive integration library. The platform reportedly integrates with major CRM, contact-centre, and enterprise systems including Salesforce, ServiceNow, Microsoft Dynamics, and custom APIs.

Where Cognigy has gaps

Reviewers note that teams need dedicated training to take full advantage of Cognigy's customisation and integration options, particularly for complex multi-channel deployments.

Some users report latency or audio-quality issues in high-volume or low-bandwidth environments, particularly when using bring-your-own telephony configurations.

Cognigy's platform licensing plus usage-based voice charges reportedly make it difficult for teams to estimate total cost of ownership without detailed usage forecasts and vendor guidance.

Teams that bring their own carrier or phone numbers reportedly face additional integration work compared to platforms with bundled telephony.

Which use cases fit each

Pick PolyAI if you...

Run a contact centre handling high volumes of inbound customer-service calls and want to deflect routine inquiries without building voice flows from scratch.

Need bundled telephony infrastructure and prefer a managed-service model that reduces the technical burden on your operations team.

Want natural conversation flow and interruption handling over self-service configuration flexibility.

Want a voice-first platform optimised for customer service rather than a multi-channel conversational AI suite.

Pick Cognigy if you...

Need a unified conversational AI platform that spans voice, chat, messaging, and digital channels with consistent conversation logic across touchpoints.

Want a visual flow builder for operations teams plus API access for developers who need programmatic control over conversation design.

Require bring-your-own LLM support or extensive integration options for custom enterprise workflows.

Have the technical resources to handle telephony setup and platform configuration in exchange for greater flexibility.

Note: if the calls you're automating are inbound sales conversations that need to sit alongside an outbound AI SDR in one platform, teams in that position typically also evaluate 11x's Julian.

Verdict

PolyAI is the stronger fit for contact-centre teams that need a voice-first platform optimised for high-volume inbound customer-service calls. The bundled telephony infrastructure and managed-service model reduce the technical lift for operations teams that lack in-house voice AI expertise. The platform's interruption handling and natural conversation flow reportedly improve customer satisfaction compared to traditional IVR systems. PolyAI's gaps are primarily around self-service configuration and outbound calling capabilities, which matter less for teams focused on inbound customer-service deflection.

Cognigy is the better starting point for enterprise teams that need a unified conversational AI platform spanning voice, chat, messaging, and digital channels. The visual flow builder makes the platform accessible to non-technical teams, while API access and bring-your-own LLM support give developers the flexibility to build custom workflows. Cognigy's multi-channel capabilities allow teams to deploy consistent conversation logic across touchpoints without rebuilding flows. The platform's gaps are primarily around learning curve and telephony setup burden, which matter less for teams with dedicated technical resources.

Practical next steps: request detailed pricing breakdowns from both vendors that specify what the per-minute rate includes, any platform fees, and how overages are handled. Build a short test flow on each platform if possible, or request a demo that shows the specific use case you're automating. Confirm compliance documentation (SOC 2, HIPAA, GDPR) directly with each vendor if your use case requires it. Ask about contract terms, minimum spend commitments, and cancellation policies before signing.

Frequently asked questions

Is PolyAI cheaper than Cognigy?

Neither platform publishes pricing, making direct cost comparison difficult. PolyAI reportedly uses per-minute pricing with bundled telephony, estimated at $0.10–$0.25 per minute depending on volume. Cognigy reportedly uses platform licensing plus usage-based voice charges, with platform fees estimated at $50,000–$150,000+ annually and voice usage at $0.05–$0.15 per minute. Total cost depends on call volume, contract terms, and whether telephony and LLM inference are bundled or billed separately. Teams should request detailed quotes from both vendors.

Which is easier to build with if I can't code?

Cognigy offers a visual flow builder that lets non-technical teams design conversation logic without code. PolyAI uses a managed-service model where the vendor's team configures agents based on customer requirements, meaning teams don't build flows directly. For teams that want self-service control over conversation design, Cognigy is the more accessible option. For teams that prefer to hand off implementation to the vendor, PolyAI's managed-service approach reduces the learning curve.

Can I bring my own phone numbers or carrier to each?

PolyAI provides bundled telephony infrastructure, meaning phone numbers and carrier connectivity are included as part of the platform. Bring-your-own carrier options are reportedly limited. Cognigy supports bring-your-own telephony through SIP trunk integration, allowing teams to use existing phone numbers and carrier relationships. Cognigy also offers its own telephony service for teams that prefer a bundled option. Teams with existing telephony infrastructure should confirm integration requirements with each vendor.

Which handles outbound calling campaigns better?

Cognigy reportedly supports outbound campaigns with more solid configuration options than PolyAI. PolyAI is primarily built for inbound customer-service calls, and outbound capabilities are reportedly limited. For teams that need to automate outbound appointment reminders, confirmations, or follow-ups, Cognigy is the stronger fit. For teams focused exclusively on inbound customer-service deflection, PolyAI's inbound-first design is more aligned with the use case.

Are PolyAI and Cognigy HIPAA compliant?

PolyAI states it is SOC 2 Type II certified and GDPR compliant as published on its website. HIPAA compliance is not explicitly stated in public documentation; teams handling protected health information should request a Business Associate Agreement and compliance documentation directly from PolyAI. Cognigy states it is SOC 2 Type II and ISO 27001 certified and GDPR compliant. HIPAA compliance is not explicitly stated in public documentation; teams should request compliance documentation and a BAA from Cognigy if required for their use case.

Can either transfer a call to a human?

Both platforms support warm transfer to human agents. PolyAI reportedly includes transfer logic as part of its pre-built agents, allowing calls to be routed to human agents based on conversation context or explicit customer requests. Cognigy allows teams to configure transfer rules in the visual flow builder, with integration to major contact-centre systems including Genesys, Five9, and Twilio. Teams should confirm that transfer functionality works with their existing contact-centre infrastructure before deploying either platform.

What do users complain about most?

For PolyAI, reviewers cite limited self-service configuration options and pricing opacity. Teams that want to adjust agent behaviour reportedly need to coordinate with PolyAI's support team rather than making changes directly. Multiple users note that per-minute costs and contract terms were not disclosed until late in the sales process. For Cognigy, reviewers cite a steep learning curve for advanced features and voice performance variability in high-latency environments. Teams reportedly need dedicated training to take full advantage of the platform's customisation options.

How long does implementation take for each?

PolyAI's managed-service model reportedly includes dedicated implementation support, with timelines varying based on use-case complexity and integration requirements. Typical deployments range from 4 to 12 weeks according to third-party sources. Cognigy's implementation timeline depends on whether teams use the visual flow builder or require custom API integrations; simple deployments can reportedly go live in 2 to 4 weeks, while complex multi-channel implementations may take 8 to 16 weeks. Teams should request implementation timelines and resource requirements from each vendor during the evaluation process.

Can I test either platform before committing?

PolyAI does not offer a self-service trial; the vendor typically provides a demo and proof-of-concept engagement as part of the sales process. Cognigy offers a demo environment for evaluation, though access to the full platform typically requires coordination with the vendor's sales team. Teams that want to test conversation flows or integration requirements should request a pilot or proof-of-concept engagement from each vendor before signing an annual contract.

How does 11x's Julian compare to PolyAI and Cognigy?

Julian is 11x's AI phone agent for inbound speed-to-lead and qualification, sold as part of the 11x platform alongside Alice (outbound AI SDR). Julian is a packaged sales agent rather than a general-purpose voice-building platform, so teams building custom voice products for customer service or multi-channel deployments typically compare it less directly to PolyAI or Cognigy. Julian is positioned for revenue teams that want inbound voice automation integrated with outbound prospecting in one system. Teams focused exclusively on contact-centre automation or multi-channel conversational AI are more likely to evaluate PolyAI or Cognigy.

Last updated: October 2026.