Decagon vs Cresta

Detailed side-by-side comparison to help you choose the right tool

Decagon

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Customer Support AI

Enterprise conversational AI platform for building customer-facing agents across voice, chat, and email.

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Starting Price

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Cresta

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Customer Support AI

Cresta is an enterprise contact center AI platform with real-time agent assist, full-coverage QA, and autonomous voice agents on customer-specific models.

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Starting Price

Custom

Feature Comparison

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FeatureDecagonCresta
CategoryCustomer Support AICustomer Support AI
Pricing Plans6 tiers6 tiers
Starting Price
Key Features

      Decagon - Pros & Cons

      Pros

      • Customer roster (Notion, Bilt, Rippling, Duolingo) is unusually strong proof of production fit
      • Agent Operating Procedures give ops teams real control without engineering tickets
      • Executes actions (refunds, plan changes) not just answers — measurable containment uplift
      • Per-reply policy/tone evaluation makes brand and compliance teams more comfortable
      • Voice + chat + email in one platform avoids stitching multiple vendors together

      Cons

      • Enterprise-only — no self-serve tier or transparent pricing on the site
      • Six-figure annual contracts are out of reach for SMB and growth-stage CX teams
      • Requires meaningful integration work with existing CRM and ticketing systems
      • Heaviest value lands at high contact volumes; ROI is weaker for low-ticket-volume orgs
      • Some flow-authoring complexity still requires forward-deployed engineering at launch

      Cresta - Pros & Cons

      Pros

      • Models fine-tuned on the customer's own calls and outcomes — sounds like the brand, not a generic chatbot
      • Three connected layers (Assist, Intelligence, AI Agent) share one data pipeline — no integration sprawl
      • Native connectors for Genesys, Five9, NICE, Salesforce, Zendesk are battle-tested at Fortune 1000 scale
      • Full-coverage QA replaces sampling — every conversation is scored, not 2% of them

      Cons

      • Enterprise contracts only — minimum deal size and rollout time excludes small teams
      • Implementation requires a multi-week data and model-tuning phase before measurable ROI
      • Pricing is opaque — no public per-seat number makes early-stage comparison hard
      • No MCP integration; agentic extensions require custom CCaaS connector work

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