Regal vs DECAGON

1. Enterprise Ready, Voice First
2. Real-Time Personalization Driven by Unified Customer Data
3. Pricing Models Designed to Fit Your Business

Regal was built from the ground up to handle complex, high-volume voice interactions - making it a strong fit for enterprises where phone calls are a primary channel for revenue and customer engagement. Whether you’re automating bookings, qualifying leads, or recovering payments, Regal offers a voice-first platform that prioritizes speed, performance, and flexibility.
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Decagon began with a focus on automating support through email and has since expanded into voice. Its voice capabilities are now growing, particularly for teams that already use Decagon for digital support workflows. 
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While both platforms support voice, their origins and approach to scaling voice interactions differ giving enterprise teams flexibility based on their specific channel priorities.

Regal integrates with over 40 enterprise systems to centralize customer data into a real-time, unified profile. This allows AI agents to personalize conversations based on past behavior, lifecycle stage, and engagement signals without custom development. Regal empowers business users to launch and iterate quickly, making data-driven personalization accessible across every touchpoint.

Decagon supports deep integrations and offers configuration through Agent Operating Procedures (AOPs), giving teams granular control over how workflows interact with backend systems enabling teams to piece together a comprehensive, real-time view of the customer journey.

Regal is built for real-time, personalized voice conversations so the conversations are charged per minute. You only pay for the time the AI agent is actively on the call. This model supports a wide range of use cases and allows users to flex and A/B test easily while taking advantage of the quickly declining cost of LLMs and AI technology. Regal’s per-minute model makes it easy to experiment with new use cases and adapt as your needs evolve without being locked into rigid pricing structures.

Decagon offers both per-conversation and per-resolution pricing. With per-conversation pricing, you pay a flat fee for every attemptvwhether the call lasts 10 seconds or 10 minutes, and regardless of whether the issue is resolved. Their per-resolution model charges only when the AI fully handles the task without escalation. This requires clearly predefining what counts as a successful outcome and agreeing on pricing structures for each type of interaction. This can add operational complexity and make it harder to experiment with new use cases.

As AI costs continue to decline, brands locked into fixed per-resolution and per-conversation pricing may find they’re paying more than necessary for each conversation without benefiting from efficiency gains.