Deploying langflow at scale

[rijojoseph07]

Hi all,

I’m trying to deploy Langflow in production on Kubernetes using the official Helm chart. I’m running into several issues, especially when deploying the backend and frontend separately:

• Job queue not found: The backend frequently fails with messages about the job queue being missing.
• Ephemeral MCP server configs: The MCP configurations are currently stored in local files. This causes problems because when a request lands on a new pod (due to scaling or restarts), newly added MCP servers vanish or are unavailable; it looks like there’s no persistence or centralized storage.
• Redis not used for config: From the deployment, it looks like Redis isn’t used for MCP server configs—just for some session or cache data.

Questions:

• Is there an up-to-date, comprehensive guide for deploying Langflow at scale in production on Kubernetes?
• How does the community recommend managing MCP server configs across multiple pods?
• Are there best practices or examples for leveraging Redis or other persistent backend stores for these configurations?
• Is there a way to deploy the backend/frontend independently without these issues?
• Would appreciate any guidance or pointers, especially from teams who have Langflow running reliably at scale.

Image used is v1.6.8 and also tried to build v1.7.0 from main branch.

Thanks!