Rasa is an open source machine learning framework. It helps you build contextual assistants that can have layered conversations with lots of back and forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that have been previously discussed — Rasa enables you to build assistants that can do this in a scalable way. With Rasa, you can build contextual assistants for the most popular messengers (Facebook, Slack and others), for your own conversational channels, and voice assistants like Alexa Skills and Google Home Actions.
Deploy Rasa on Kubernetes via Helm.
Build assistants with intent recognition and dialogue management.
Connect assistants to messaging and service channels.
Configure replicas and dependencies for reliable assistant operations.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy Rasa on Kubernetes.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by Rasa. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
