Kore.ai
Enterprise Conversational AI

Kore.ai

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

Kore.ai

VS
Top enterprises trust Rasa
At a glance

Two platforms, two opposite philosophies

Kore.ai

Kore.ai is an enterprise Experience Optimization Platform with multi-engine NLP, pre-built industry agents, and flexible deployment (cloud or on-premise).

Founded
2013
HQ
Orlando, FL
Funding
~$300M
Capterra
4.6 / 5 (Capterra)

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
Kore.ai
Rasa
Deployment Model
Cloud SaaS or enterprise on-premise
Cloud, on-premise, or hybrid; open-source option
NLU Engine
Proprietary multi-engine NLP with domain training
Transformer-based, fully customizable
Target Audience
Mid-market to enterprise; banking, healthcare, retail, HR
Any scale; developers and non-technical teams
Primary Use Case
Multi-channel customer service and HR/IT bots
Multi-channel conversational assistants
Data Privacy
Cloud tenant with on-premise option; GDPR-ready
On-premise option for full data control; GDPR-ready
Integrations
100+ pre-built connectors (Genesys, NICE, Salesforce, SAP)
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.

Enterprise SaaS vs. Open-Source Framework

Kore.ai

Kore.ai is positioned as an enterprise platform that accelerates time-to-value via pre-built industry agents and a Gartner Magic Quadrant Leader position. Its multi-engine NLP handles complex enterprise language, and on-premise deployment appeals to regulated industries. Kore.ai bundles integrations, industry templates, and support into a complete managed service. The company targets organizations that want conversational AI quickly without building from scratch. Reviewers praise the pre-built agents and on-premise capability, but report that integration configurations can be messy, and advanced feature learning curves are steep. Enterprise pricing is opaque and requires custom quotes. Kore.ai's governance is less transparent than Rasa Orchestrator's policy-based approach—decisions emerge from the NLP engine, not auditable rules.

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.
Kore.ai

Kore.ai offers both cloud SaaS and on-premise deployment. Cloud customers benefit from managed infrastructure and automatic updates. On-premise customers deploy Kore.ai on their own Kubernetes or VM infrastructure, giving data residency control. On-premise setup requires. For regulated industries, on-premise appeals, but Kore.ai reviewers report complex setup. Hidden costs: on-premise requires your ops team to maintain security patches, scaling, and high-availability infrastructure. Kore.ai's cloud SaaS removes this burden but locks you into their infrastructure.

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.
Kore.ai

For on-premise deployments, compliance is shared: Kore.ai handles application security, your team handles infrastructure. For cloud SaaS, Kore.ai handles all compliance, but auditing is harder because you cannot inspect infrastructure.

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.
Kore.ai

Kore.ai aims for both non-technical and technical audiences. Pre-built agents and UI-based dialogue design appeal to business users. API-first architecture allows. Reviewers report that basic deployments are fast, but complex integrations become messy due to configuration complexity. For teams with dedicated implementation resources, Kore.ai's breadth works. For lean teams needing rapid iteration, learning curve is steep.

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.
Kore.ai

Kore.ai pricing is fully custom and opaque. ROI calculation is hard without transparent pricing. Reviewers report enterprise deals ranging from $50k to $500k+/year, but without public pricing. This opacity makes early-stage evaluation difficult and favors large enterprises with dedicated procurement teams.

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.