Overview
Rasa is a modular conversational AI framework for building custom text and voice assistants, combining NLU, dialogue management, and integrations in one stack. It is open source at the core (Rasa Open Source) and extended by Rasa Pro and Rasa Studio for enterprises that need scalable, governed AI agents.
Pricing
Rasa Open Source
- Free and open source framework (Python) for intent classification, entity extraction, and dialogue management.
- Suitable for teams that want full control over infrastructure and are comfortable running their own stack.
Rasa Platform / Pro – Growth Tier
- Commercial Rasa Platform / Rasa Pro plans start around 35,000 USD per year for growth-stage companies with under ~500,000 conversations annually.
- Includes full platform access, basic support, and a no-code UI for designing assistants and managing deployments.
Enterprise
- Enterprise deals are quote-based, often well above 100,000 USD per year for large-scale deployments and advanced support.
- Adds premium support, enterprise security, SLAs, and tooling for large, multi-assistant environments.
Key Features
- Rasa Open Source (NLU + Core) – NLU for intents/entities and Core for dialogue management, enabling complex multi-turn conversations.
- Rasa Pro & Studio – Enterprise platform with orchestration, analytics, debugging tools, and a visual builder for flows and policies.
- Generative AI-native platform – Support for retrieval-augmented generation, tool-calling, and LLM-powered policies while retaining deterministic control.
- Omnichannel connectors – Integrations for web chat, WhatsApp, Messenger, telephony, and custom channels.
- Analytics & monitoring – Tools to inspect conversations, track performance, and iteratively improve models and stories.
Best Use Cases
- Enterprise customer support assistants – Custom, brand-controlled virtual agents for banking, insurance, telecom, and utilities.
- Internal IT & HR assistants – Automate employee service portals and internal help desks with secure on-prem or VPC deployments.
- Domain-specific bots – Assistants that must follow strict workflows or compliance requirements (healthcare, finance, public sector).
- Hybrid NLU + LLM agents – Combine deterministic flows with LLM-driven understanding for robust yet flexible behavior.
- Language- and region-specific assistants – Custom bots in many languages with local hosting where needed.
Pros
- ✅ Open-source and extensible – Core framework is open, allowing deep customization, model choice, and full data ownership.
- ✅ Built for complex conversations – Strong dialogue management for multi-turn, contextual flows beyond simple FAQ bots.
- ✅ Enterprise-focused platform – Rasa Pro and Studio bring governance, security, and tooling for serious deployments.
- ✅ Vibrant community & ecosystem – Large developer community, tutorials, and third-party integrations.
Cons
- ❌ Steeper learning curve – Requires ML and engineering chops; not as plug-and-play as low-code SaaS chatbots.
- ❌ Enterprise platform is costly – Commercial tiers starting around 35k USD/year are aimed at growth and enterprise companies.
- ❌ More DevOps responsibility – Self-hosting and customizing Rasa demand infrastructure and MLOps investment.
Official Website
Rasa – Official website and docs: https://rasa.com and https://rasa.com/docs
Release Date: 2016
Last Updated: December 2025