Decagon vs kapa.ai
A side-by-side comparison of Decagon and kapa.ai, two Support tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
kapa.ai
SupportTurns your technical docs into a RAG assistant that answers users' questions.
View kapa.aiAt a glance
| Attribute | Decagon | kapa.ai |
|---|---|---|
| Category | Support | Support |
| Pricing | PAID | PAID |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms | Web, API | Web, API |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Decagon | kapa.ai |
| Capabilities (differs) |
|
|
The honest brief
Decagon
Prices per conversation or resolution, not per seat, and lets ops teach agents via natural-language procedures.
- Outcome-based pricing
- Ops author logic, engineers keep guardrails
- Chat, email, and voice
- Takes actions, not just answers
- Enterprise sales-only
- Setup and tuning required
- Model choice abstracted away
kapa.ai
Purpose-built for external end users — tuned for answer accuracy with citations to avoid misleading customers, not just internal ticket deflection.
- RAG grounded in your own docs plus 30+ sources
- Deploys to docs, Slack, Discord, Zendesk
- Analytics surface unanswered questions
- SOC 2 Type II compliant
- Proven at OpenAI and Docker scale
- Opaque, sales-led pricing
- No self-serve free tier
- Enterprise-oriented setup
- Answer quality depends on your docs
When to pick which
Decagon leans on Ticket deflection as a headline capability; kapa.ai treats it as secondary.
- Ticket deflection (primary capability)
Their capability lists differ in recorded depth — compare the full lists above before deciding.