Decagon
Enterprise Conversational AI

Decagon

Teams evaluating an AI customer service platform ask the same three questions: can we self-host, can our team own it, and what does it actually cost? Here's how Rasa compares on the dimensions that decide   enterprise deals

The short version
Rasa is the self-hosted, customer-owned alternative for teams that need to own the agent, run it in their own environment, and govern regulated workflows with explicit policies.
Self-hosted
Customer-owned
Native voice
Guided governance
Published pricing
Competitor

Decagon

VS
Top enterprises trust Rasa
At a glance

Two platforms, two opposite philosophies

Decagon

Decagon focuses on autonomous AI agents trained on company knowledge. Handles customer conversations without human routing. Integrations with Zendesk, Salesforce, Intercom.

Founded
2023
HQ
San Francisco, CA
Funding
$481M+
Capterra
Not yet rated

Rasa is an enterprise conversational AI platform built on a self-hosted, developer-owned architecture. Patented dialogue management (CALM) delivers guided governance: business logic controls high-risk actions through explicit policies, regardless of LLM output. Native voice (Twilio, AudioCodes, Genesys), 100% on-prem or private cloud, transparent conversation-volume pricing. Customers include N26, Deutsche Telekom, Helvetia, Autodesk.

Founded
2016
HQ
San Francisco / Berlin
Funding
~$70M raised
Capterra
4.7 / 5
Own the agent

One platform for voice and chat, running in your environment

Rasa runs the same guided-governance engine across phone and chat, fully self-hosted. Your team configures flows, policies, and integrations directly, with no managed-service dependency.

  • Native voice over Twilio, AudioCodes, and Genesys, sharing context with chat.
  • Explicit policies control high-risk actions on regulated workflows, regardless of LLM output.
  • Deploy on-prem, in private cloud, or air-gapped, with no customer data leaving your perimeter.
Comparison matrix

Side-by-side on the six dimensions that decide enterprise deals

The dimensions enterprise teams use when picking between a managed cloud service and a customer-owned platform.
Dimension
Decagon
Rasa
Deployment Model
Cloud-only SaaS
Cloud, on-premise, or hybrid; open-source option
NLU Engine
Proprietary, autonomous-first
Transformer-based, fully customizable
Target Audience
Tech-forward companies seeking autonomous containment
Any scale; developers and non-technical teams
Primary Use Case
Fully autonomous customer resolution
Multi-channel conversational assistants
Data Privacy
Cloud-only; no self-hosted option
On-premise option for full data control; GDPR-ready
Integrations
Zendesk, Salesforce, Intercom
100+ via Zapier, REST APIs, custom plugins
Deep dive

Five dimensions, side by side

The decisions that actually move enterprise deals, explored one by one.

Autonomous-First Philosophy vs. Governed Dialogue Ownership

Decagon

Decagon's autonomous-first philosophy prioritizes resolution containment: let the AI handle as many conversations as possible without escalation. Its integration with Zendesk, Salesforce, and Intercom enables quick training on existing customer conversations. Clean integrations and fast setup appeal to teams optimizing for deflection. However, Decagon is cloud-only with no self-hosted option, disqualifying it for regulated industries. Chat-focused, no native voice. Limited public reviews constrain visibility into production reliability. Governance controls for high-risk industries (healthcare, finance) are minimal—Decagon's autonomy philosophy is incompatible with audit requirements. For tech-forward companies in non-regulated industries that can accept higher autonomous escalation risk, Decagon's philosophy works. For compliance-heavy organizations or regulated sectors, the cloud-only autonomous approach is a dealbreaker.

Rasa

Rasa's customer-owned architecture: self-hosted from day one, patented dialogue management for guided governance, full code-level extensibility, native voice via Twilio/AudioCodes/Genesys, and transparent conversation-volume pricing. N26, Deutsche Telekom, and Helvetia run Rasa for regulated workflows.

Deployment Model & Data Sovereignty

Where and how your data lives determines compliance posture, latency, and switching costs.
Decagon

Decagon is cloud-only SaaS. No self-hosted option in current public materials. For tech-forward companies comfortable with SaaS and lacking compliance requirements, cloud-only is acceptable. For regulated industries or organizations with data residency mandates, cloud-only is disqualifying. Switching cost is moderate: your training data and integrations are portable, but retraining on another platform requires setup.

Rasa

Rasa supports cloud, on-premise, and hybrid deployment out of the box. Your data never leaves your infrastructure unless you choose to send it elsewhere. On-premise deployment is included in the open-source edition at no cost; enterprise support adds dedicated implementation specialists and SLAs. Unlike managed services, Rasa gives your team full control: choose your hosting provider, VPC, Kubernetes distribution, or even air-gapped environments. Compliance teams approve faster because your security team reviews the architecture. No vendor dependency on SaaS uptime. For regulated industries (healthcare, finance, legal), on-premise is non-negotiable. Rasa delivers it without premium surcharge or complex licensing.

Security & Compliance

Certifications, data handling, and audit posture.
Decagon

Cloud-only + autonomous philosophy means compliance auditing is limited. You cannot inspect dialogue decisions or control escalation logic for compliance. For healthcare, financial services, or legal, this is a critical limitation. For non-regulated tech companies, cloud-only SaaS compliance may suffice.

Rasa

Rasa runs on your infrastructure or your chosen cloud provider. You control encryption at rest (your key management), encryption in transit (TLS 1.2+), and access controls (IAM). Rasa does not hold customer data in a shared multi-tenant system. Enterprise Rasa includes SOC 2 Type II compliance, GDPR readiness, and audit-ready logging via OpenTelemetry. For HIPAA, you implement HIPAA-compliant infrastructure; Rasa is platform-agnostic. For PCI-DSS, on-premise deployment gives your compliance team full visibility. Rasa's architecture is inspectable: every webhook, every API call, every data flow is traceable. This transparency enables fast audit cycles and regulatory approval.

Developer Experience & Integration

Who builds, debugs, and extends the agent.
Decagon

Decagon is designed for non-technical teams. For teams that want fast implementation and high autonomy, Decagon's setup is appealing. For teams needing custom logic or governance controls, Decagon is limiting. Developer flexibility is sacrificed for ease of use and autonomous containment.

Rasa

Rasa is built for engineers. Start in VS Code or your IDE of choice; deploy via GitHub Actions or Jenkins. Rasa provides SDKs (Python, JavaScript), OpenAPI specs, and REST APIs. Extend via custom Action Servers (Python, JavaScript, Go). Integrate any CRM, database, or third-party service via REST or MCP. No low-code UI required; if your team prefers code, Rasa is fully scriptable. Rasa Playground and Rasa Studio provide visual debugging for non-engineers. For large teams, Rasa's dialogue state is event-based and queryable—understand exactly why the AI made a decision. CI/CD integration is native. Deployment is reproducible: commit your models, test in CI, deploy to production via container orchestration.

Pricing & ROI

Cost structure and predictability.
Decagon

Decagon pricing is custom. Limited public pricing information makes ROI calculation difficult. For small to mid-market companies, custom pricing is hard to estimate without a quote. At enterprise scale, costs can be significant. Outcome-based models can make total cost harder to predict.

Rasa

Rasa Developer Edition is free, forever. One bot per company; up to 1,000 external conversations per month. Community support via GitHub and Rasa Forum. Rasa Enterprise is transparent annual licensing based on conversation volume (e.g., $50k/year for 1M conversations). Volume discounts apply. No per-agent fees. No per-resolution charges. No add-on surcharges. What you see is what you pay. Multi-year agreements available. Dedicated CSM, premium support (4-hour response SLA), and custom onboarding included at Enterprise tier. No vendor lock-in: if you outgrow Rasa, export your models and dialogue definitions; they are plain YAML and JSON.

The Forrester Wave
The Forrester Wave Strong Performer Q2 2026
Analyst recognition

Independently recognized for enterprise conversational AI

Rasa's standing in the Forrester Wave reflects the strategy enterprises are buying: a platform they own and govern, not a managed service they rent. Evaluate the architecture, then evaluate the analyst view.
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The wider field

Ten alternatives enterprises evaluate

The conversational AI field at a glance, with Rasa shown for reference and a note on where each platform fits. Compare each head-to-head on its own page.
Platform
Founded
HQ
Funding
Rating
Rasa
2016
San Francisco / Berlin
~$70M
4.7 / 5 (Capterra)
Decagon
2023
San Francisco, CA
$481M+
Not yet rated
DRUID AI
2017
Bucharest, Romania
$124M
4.8 / 5 (Gartner)
Gorgias
2015
San Francisco, CA
$104M
4.6 / 5 (Capterra)
Zendesk AI
2007
San Francisco, CA
PE-owned (Hellman & Friedman / Permira)
4.4 / 5 (Capterra)
Intercom Fin
2011
San Francisco, CA
$511M
4.5 / 5 (Capterra)
Parloa
2018
Berlin, Germany
$560M+
4.0 / 5 (G2)
Ada
2016
Toronto, Canada
$200M
4.7 / 5 (Capterra)
Salesforce Agentforce
1999
San Francisco, CA
Public (NYSE: CRM)
Not yet rated
Kore.ai
2013
Orlando, FL
~$300M
4.6 / 5 (Capterra)
Sierra AI
2024
San Francisco, CA
$1.4B+ raised
4.8 / 5 (Capterra)
Data current as of May 2026; sources: company announcements, Crunchbase, and Capterra/G2/Gartner Peer Insights. Ratings are Capterra unless tagged (G2 or Gartner). "Not yet rated" indicates too few public reviews for a reliable score. Funding reflects total disclosed capital raised; "PE-owned" and "Public" denote companies not on a venture funding track. Review and verify before each publish cycle.
Comparison matrix

Side-by-side on the six dimensions that decide enterprise deals

The dimensions enterprise teams use when picking between a managed cloud service and a customer-owned platform.