Cresta vs Decagon AI
Two of the most-asked-about agents in the enterprise space. Here's how they actually stack up.
Cresta
Real-time AI coaching and post-call analytics for contact center agents
Enterprise
Read full review →Decagon AI
AI-native customer support agent for high-volume enterprise teams
Enterprise
Read full review →Side-by-side comparison
| Cresta | Decagon AI | |
|---|---|---|
| Tagline | Real-time AI coaching and post-call analytics for contact center agents | AI-native customer support agent for high-volume enterprise teams |
| Pricing | Enterprise | Enterprise |
| Categories | enterprise, customer-support, voice-agents | customer-support, enterprise |
| Made by | Cresta | Decagon |
| Launched | 2019-01 | 2023 |
| Platforms | Web | Web, API |
| Status | active | active |
Cresta highlights
- + Real-time agent assist showing next-best-action suggestions during live calls
- + Automatic call scoring against custom quality frameworks
- + Coach-on-call feature that flags coaching moments to supervisors in real time
- + Conversation intelligence with topic tracking and sentiment analysis
- + Automated after-call work including call summaries and CRM note generation
Decagon AI highlights
- + AI agents that resolve customer support tickets end to end
- + Multi-step reasoning across complex, multi-turn support conversations
- + Deep integration with Salesforce, Zendesk, Intercom, and custom back-end systems
- + Real-time action execution in connected systems (refunds, account changes)
- + Escalation to human agents with full conversation context
Frequently Asked Questions
Which is better, Cresta or Decagon AI?
Neither is universally better. Cresta (Enterprise) leans into enterprise, while Decagon AI (Enterprise) is closer to customer-support. Pick based on which workflow you actually do every day.
What is the price difference between Cresta and Decagon AI?
Cresta is enterprise. Decagon AI is enterprise. See the pricing row in the comparison table.
Can I use Cresta and Decagon AI together?
In most cases, yes. They serve overlapping but distinct needs, so running them side by side is common until you decide which fits your workflow.