Description

Rasa is a developer-focused open-core conversational AI platform designed to help enterprises build robust, context-aware voice and text-based assistants. These retain full control over data and business logic. It bridges machine‑learning NLU (Natural Language Understanding) and dialogue management with integrations into existing systems. Using both machine learning and a generative AI engine, Rasa makes it easy to design deeply nuanced and secure customer conversations, whether deployed on-premises or in the cloud.

Rasa’s LLM-agnostic and no-code tools allow organizations to iterate quickly, maintain compliance, and scale automation affordably. This distinguishes it from rigid rule-based chatbot solutions. With Rasa Pro and Rasa Studio, teams access enterprise‑grade observability, low‑code tuning, and role‑based collaboration. Rasa is ideal for robust customer service, internal helpdesk, lead‑gen, and multilingual assistants.

Key Features of Rasa

1. CALM Framework: Conversational AI engine with LLM integration ensures accurate, context-driven conversations.
2. Rasa Open Source NLU and Core: ML‑based intent/entity understanding and dialogue policy training 
3. Open-Source and Pro-Code: Flexible open-source core (Rasa Open Source) and Rasa Pro for high customizability and transparency.
4. No-Code UI (Rasa Studio): Enables business users and designers to build and optimize assistants without programming.
5. Custom Actions Server: Integrate external APIs and perform real-time operations securely.
6. Advanced Security: On-prem deployment, PII data management, GDPR, and PCI compliance.
   Enterprise Search: Enables assistants to retrieve accurate information quickly from multiple data sources.
7. Multi‑channel connectors: supports REST, WebSocket, plus integration with Slack, WhatsApp, Alexa & more 
8. Extensive Integrations: REST APIs, WebSocket, database, and custom connectors for seamless business workflows.
9. Intuitive Conversation Repair: Automatically handles digressions, interruptions, and context shifts.
10. Conversation‑driven development: review live interactions and iterate 
11. Full code control: allows teams to customize pipelines and logic fully.
12. Multi‑language support: build assistants in almost 100 languages.
13. Observability and Analytics: Built-in support for monitoring, end-to-end testing, and real-time insights to continuously improve performance.
14. Open source, large community: over 50 M downloads, 15k forum members, 750+ contributors 

Rasa Key Customers

Rasa’s website mentions Autodesk, Swisscom, Deutsche Telekom, N26 (Europe’s leading mobile bank), and multiple Fortune 500 companies (across banking, insurance, telecom, and healthcare). Here are some use cases their customers are using Rasa for:

• N26 (mobile banking) – 20 % of customer service handled by Rasa (goal to reach 30 %).
• T‑Mobile – 10 % of messaging customers use a self‑service assistant built on Rasa.
• Orange (Telecom France) – Djingo assistant automates technical support across channels.
• Dialogue (Canada healthcare) – multilingual patient‑intake evaluation assistant.
• PicPay (Brazil fintech) – scaled emergency aid distribution via Rasa chatbot.

Learn more: Rasa Customer Case Studies

Who is the CEO or Founder of Rasa?

Rasa was co‑founded by Alan Nichol (CTO) and others; the current CEO is Alex Weidauer (co‑founder).

Where is Rasa Headquartered?

Rasa is headquartered in Berlin, Germany — Schönhauser Allee 175, 10119 Berlin, Germany.

Rasa Funding News

Rasa has secured $83.4 million in funding across nine rounds, including three seed rounds, five early-stage investments, and one late-stage raise.

Its most significant round to date was a $30 million Series C led by PayPal Ventures and StepStone Group, with participation from Andreessen Horowitz, Accel, and Basis Set Ventures. Funds earmarked for scaling generative conversational AI and expanding Rasa Pro and Rasa Studio globally.

Who Should Use Rasa?

Ideal for technical teams, product developers, and enterprises needing full control over data, integrations, and customization.

• Enterprises that need highly customizable, compliant conversational AI.
• Financial institutions, insurers, telecom, and healthcare providers that seek strict data privacy.
• Product teams that want to scale from experimentation to production quickly.
• Developers and business users collaborating on digital assistant projects.

Best use cases for Rasa include enterprise customer service automation, internal IT helpdesk bots, multilingual assistants, and domain‑specific conversational workflows like claims processing, digital banking, and personal finance AI.

Rasa Pros

• Highly customizable and open-source.
• Enterprise-grade security and privacy controls.
• Active developer community and rich documentation.
• Handles complex, multi-turn conversations with context awareness.
• Flexibility to deploy both on-premise and cloud.
• Strong multi‑channel support and language flexibility.

Rasa Cons

• Steep learning curve for beginners.
• Advanced deployment requires Python/programming expertise.
• Documentation can be overwhelming for non-technical users.
• No out-of-the-box analytics for beginners.
• Community support is only available in the free tier; advanced support requires paid plans.
  

Rasa Integrations

Out‑of‑box connectors for Slack, WhatsApp, Facebook Messenger, Telegram, Twilio, Alexa Skills, Google Home Actions, REST APIs, plus the ability to build custom channel integrations. 

• REST and WebSocket APIs
• Database and external system connectors
• Kubernetes deployment via Helm
• Multi-channel messaging (including WhatsApp, Messenger, custom channels)
• IVR and telephony systems (via additional connectors)

Rasa Free Plan

Rasa’s Free Developer Edition includes:

• All of Rasa Pro for local or single-production use (one bot per company)
• Up to 1,000 external or 100 internal conversations/month
• Community support via forums

Choose paid plans as the free plan lacks premium support and is limited for large teams or production-scale workloads; it provides basic features for experimentation. For example, the free plan is suitable for prototyping and hosting small bots. Paid plan needed for more volume, advanced support, enterprise security, multiple bots, or conversations.

Rasa Paid Plan Pricing

Rasa Paid Pricing Plan	Price	Rasa Features	Best For
Growth	Contact Sales	Full platform, no-code UI, basic support, up to 500,000 conversations/year	Growing teams, SMBs
Enterprise	Contact Sales	All features, premium support, advanced security, unlimited scale	Large enterprises, compliance-heavy orgs

Rasa Discounts

No published discounts listed as of the latest review; users are advised to contact sales for volume or enterprise deals.

Rasa Alternatives

AI Tool Name	Strengths	Limitations
Microsoft Bot Framework	Deep Azure integration, robust tools, global support, and strong entity extraction	Less open, heavier vendor lock-in
Google Dialogflow	NLP, easy setup, Google ecosystem	Lacks local/on-prem deployment, less open, limited data control
Botpress	Developer-friendly, open source, modular UI	Less enterprise focus
IBM Watson Assistant	Advanced AI, voice options, security	Proprietary, higher cost, less flexible workflows
Kore.ai	Workflow automation, great for customer service	Proprietary, less customizable
Amazon Lex	Seamless AWS integration	Vendor lock‑in, less control over logic

What distinguishes Rasa from its competitors?

Rasa’s unique blend of open-source flexibility, on-premise deployment, LLM-agnostic AI, and deep enterprise controls makes it stand out for organizations demanding customization, privacy, and adaptability not seen in other platforms. It also provides strong support for enterprise‑scale customization, data privacy, self‑hosted deployments, multi‑language support, and conversational logic flexibility.