# Rasa vs 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

### Kore.ai

**VS**

The Alternative

Self-hosted, customer-owned conversational AI.  
Top enterprises trust Rasa.

## Two platforms, two opposite philosophies

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

**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.

### Comparison matrix

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

| Differentiator | Rasa | Kore.ai | Verdict |
| --- | --- | --- | --- |
|  |  |  | No items found. |

### How we built this comparison

This comparison draws on user reviews from G2, Capterra, TrustRadius, and GetApp, combined with vendor documentation, published pricing, and enterprise buyer interviews.

### Deep dive

## The dimensions, side by side

### 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

|  |  |
| --- | --- |
| **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. |

### NLU Capabilities & Accuracy

|  |  |
| --- | --- |
| **Kore.ai** | - Kore.ai's multi-engine NLP combines rule-based and ML approaches.  
- Domain-specific training via.  
- Pre-built agents come with industry-tuned NLU (banking, healthcare, retail).  
- Intent recognition is good for common queries, but.  
- Customization: Kore.ai allows.  
- For proprietary business logic (custom entities, domain-specific language), Kore.ai's approach is.  
- Unlike Rasa's code-first NLU, Kore.ai emphasizes UI-based training, appealing to non-technical users but potentially limiting advanced customization. | **Rasa**  
- Rasa NLU is transformer-based and fully customizable.  
- Start with Rasa's pre-trained models (BERT, GPT-2 finetuning) or bring your own.  
- Modify intent classification, entity extraction, and dialogue routing via code.  
- Inspect every NLU decision through the Rasa NLU API.  
- For domain-specific language (medical terms, finance jargon, regional dialects), Rasa's training pipeline is transparent—you control preprocessing, feature extraction, and model hyperparameters.  
- Unlike black-box competitors, there is no "trust the AI" requirement.  
- Rasa Orchestrator governs dialogue routing, not NLU.  
- If NLU confidence is low, explicit policies route to human escalation.  
- This auditability is critical for compliance. |

### Security & Compliance

|  |  |
| --- | --- |
| **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

|  |  |
| --- | --- |
| **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

|  |  |
| --- | --- |
| **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. |

### Customer Success and Support

|  |  |
| --- | --- |
| **Kore.ai** | - Gartner Magic Quadrant Leader status suggests solid support and customer satisfaction, but reviews are mixed: some users praise support quality, others report slow response times and unhelpful documentation.  
- For enterprise deals, dedicated CSM support is likely.  
- For smaller customers, support quality may be inconsistent. | **Rasa**  
- Rasa Developer users: community support via GitHub Discussions, Rasa Forum, and #rasa Slack (100k+ members).  
- Response time: volunteer-driven.  
- Rasa Enterprise: dedicated CSM, Slack support channel, 4-hour response SLA for P1 issues, monthly business reviews, and custom onboarding.  
- Premium support tiers available for mission-critical deployments.  
- Rasa Academy provides courses on NLU tuning, dialogue design, and voice integration.  
- Implementation partners (consulting firms) available for complex deployments.  
- Documentation is comprehensive: 200+ pages, 50+ tutorials, 100+ code examples. |

## The verdict

### Which platform wins for your use case

**Kore.ai** fits large enterprises that want a broad omnichannel virtual-assistant suite with extensive prebuilt tooling, and that prefer a vendor-managed platform over an open, developer-owned framework.

**Rasa** fits enterprises that need ownership, self-hosted deployment, native voice, deterministic governance, and transparent pricing.

Choose Kore.ai for its managed strengths; choose Rasa for ownership, self-hosting, and governance.

### Common questions

**What's the main difference between Kore.ai and Rasa?**  
Kore.ai is an enterprise SaaS platform with pre-built industry agents, emphasizing quick time-to-value. Rasa is an open-source framework emphasizing customization, on-premise deployment, and transparent pricing.

**Can I deploy Kore.ai on-premise?**  
Yes, Kore.ai offers on-premise deployment, but requires your team to maintain infrastructure. Rasa also offers on-premise with free open-source edition.

**Does Kore.ai support voice?**  
Yes, via CCaaS (contact center as a service) integrations. Rasa supports native voice via Twilio, AudioCodes, Genesys, Jambonz.

**What's Kore.ai's pricing model?**  
Custom enterprise licensing. Rasa: open-source free; commercial from $50k/year.

**How long does Kore.ai deployment take?**  
Pre-built agents: weeks. Custom deployments: months. Rasa: open-source can start immediately; enterprise support available.

**Is Kore.ai better for regulated industries?**  
On-premise deployment available, but Rasa offers on-premise + SOC 2 + GDPR-ready architecture.