Choose a Langflow deployment based on whether you need to author flows or serve them. Use Docker for a quick local start, Docker Compose for a configurable single-host setup with persistent PostgreSQL, and the Langflow Kubernetes runtime when you need a headless service for production flow execution. The visual IDE is for creating and managing flows; the production runtime serves them through its API.
Which Langflow deployment route should you choose?
| Route | Best fit | What you operate | Main trade-off |
|---|---|---|---|
| Docker quickstart | Local evaluation or a simple container run | Langflow container, access controls, and any persistence you configure | Fast to start, but you must plan persistence, upgrades, secrets, and network restrictions yourself. |
| Docker Compose | Development or a configurable single-host stack | Langflow plus configurable services such as PostgreSQL and persistent storage | More control than a single container, but it does not by itself provide production availability or operational safeguards. |
| Kubernetes IDE chart | A development environment where users need the visual editor | The interactive IDE and API in a Kubernetes cluster | Supports authoring in the cluster, with the resource and exposure needs of an interactive application. |
| Kubernetes runtime chart | Serving packaged flows in production | A headless runtime, configured flows, secrets, services, and replicas | Supports API-based serving and scaling, but requires Kubernetes operations and careful configuration. |
These deployment roles and documented examples are described in Langflow’s deployment architecture, Docker deployment, and Kubernetes runtime documentation. The documentation surfaced for this guidance is for Langflow 1.12.x; verify commands, image tags, Helm values, and defaults against the release you install.
Start locally with Docker
Langflow’s Docker quickstart runs the official image and maps host port 7860 to container port 7860. The interface is then available through the host port. Follow the current Docker guide for the exact command and image reference; do not assume a mutable latest tag is suitable for a production deployment.
Authentication matters even during evaluation. The official images set LANGFLOW_AUTO_LOGIN=false by default, so provide a strong superuser password unless you are deliberately configuring another supported authentication mode. Langflow’s authentication guidance warns: “Never expose Langflow ports directly to the internet without proper security measures.” See API keys and authentication.
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A container start is not a persistence plan. Decide where the database and flow data will live, how they will be backed up, and how you will restore them before relying on a deployment for work you need to keep. Avoid exposing the mapped port beyond trusted access unless authentication, network restrictions, and appropriate transport security are in place.
Use Docker Compose for a configurable single-host stack
Compose is the natural next step when you want to configure Langflow alongside services such as PostgreSQL. Langflow’s documented example includes a PostgreSQL service and persistent volume storage; it also covers custom dependencies, packaging flow JSON into a custom image, and upgrades that retain database and flow data. Use the release-matched Docker deployment guide rather than copying an old Compose file unchanged.
Rank #2
For a deployment that should survive container replacement, configure persistent storage and a database strategy deliberately, and test backups and restores. Langflow’s Kubernetes architecture guidance strongly recommends an external PostgreSQL database for its described Kubernetes deployment; more generally, do not treat a local default database as automatically production-ready. See deployment architecture.
Check the configuration Compose will actually use
Langflow documents this precedence for its settings: CLI options override .env values, which override system environment values. Compose adds its own variable-interpolation behavior, so a shell export may not override a literal value in the Compose file. Inspect the rendered Compose configuration before starting or changing the stack; confirm that database connection values, passwords, secret keys, and other sensitive settings resolve as intended. Langflow’s environment variable documentation and Docker guide describe these configuration considerations.
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Rank #3
Separate the visual IDE from production flow serving
The IDE includes the editor and API used to create and manage flows. The production runtime is a different deployment role: it is headless and focuses on serving flows through the API. A production runtime is therefore not a substitute for the interactive authoring interface; package or otherwise configure the flows it should serve, then call its API from clients or services. Langflow explains these roles in its deployment architecture and Kubernetes best practices.
Deploy the production runtime on Kubernetes
The Kubernetes runtime path requires a Kubernetes server, kubectl, and Helm. The official guide walks through adding the Langflow Helm repository, installing the runtime chart, checking pods and services, and forwarding port 7860 for access. It then shows how to query the flows API; clients execute flows through the runtime API. Follow the current Kubernetes deployment guide for release-specific commands and chart configuration.
Rank #4
Configure flows, credentials, and access
Provide the runtime with the flows and settings it needs, and keep credentials out of plain configuration wherever possible. Langflow’s Kubernetes guide demonstrates referencing credentials with Kubernetes secretKeyRef. Langflow’s global-variable documentation also describes storing credentials in Kubernetes Secrets rather than the Langflow database. Keep the IDE, runtime, database, and API reachable only by the users and services that need them, and use authentication and TLS where appropriate. See Kubernetes deployment, global variables, and authentication.
Review chart security and capacity settings
The documented runtime chart sets readOnlyRootFilesystem: true by default as a security measure; disabling it degrades the security posture. Inspect the values for the exact chart version you install rather than assuming defaults remain unchanged. The chart also provides controls for replica count and resource requests. Langflow’s best-practices page gives resource minimums for its documented IDE and runtime deployment model, but those are version- and workload-sensitive operating guidance, not a capacity guarantee. Use the distinctions and current values in Kubernetes best practices to size the two roles separately.
Best Value
Run production preflight and secure the deployment
Langflow documents LANGFLOW_DEPLOYMENT_PROFILE=prod as a way to run production preflight checks before workers start. A failed required check aborts startup. The documented checks include PostgreSQL reachability and security configuration for MCP, SSRF protection, connector SSRF validation, and allowlists. Treat these setting names and checks as release-specific: confirm them in the documentation for the version you deploy. See production best practices and environment variables.
- Enable an appropriate authentication mode and restrict network access to the IDE, runtime, and database.
- Store credentials in secrets rather than exposing them in images or broadly readable configuration.
- Set a strong
LANGFLOW_SECRET_KEY. The key protects sensitive values and JWT signing in relevant configurations; instances in a multi-instance deployment need a consistent key. - Use TLS for connections where appropriate, keep Langflow and its dependencies current, and monitor the deployment for security issues.
- Plan database and flow-data backups, then verify that recovery works.
Langflow’s relevant guidance is in authentication, Kubernetes best practices, Kubernetes deployment, and global variables. Because images, chart defaults, and security checks can change, pin and test a release before upgrading a deployment that serves users.
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