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Choose a 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 stack with persistent PostgreSQL, and Langflow’s Kubernetes runtime when you need a headless service for production flow execution. The visual IDE is for building and managing flows; the production runtime serves them through its API.
Choose the deployment that fits your job
| Option | Best fit | What it runs | Main trade-off |
|---|---|---|---|
| Docker quickstart | Local evaluation or a simple, controlled single-container setup | The Langflow image, with port 7860 mapped to the host | Fast to start, but persistence, upgrades, secrets, and network restrictions need deliberate configuration. |
| Docker Compose | Development or a configurable single-host stack | Langflow plus supporting services such as PostgreSQL and persistent storage | More control over dependencies and data than a basic container, but not equivalent to production availability or operational controls. |
| Kubernetes IDE chart | A development environment where people need the visual editor | The IDE and API in a cluster | Supports interactive authoring, but requires cluster resources and careful access controls. |
| Kubernetes runtime chart | Production serving of packaged flows | A headless runtime that serves flows through the API | Designed for serving and scaling, but adds Kubernetes operations and configuration. |
Langflow’s official deployment guides surfaced for version 1.12.x. Image tags, commands, chart values, and defaults can change, so confirm them against the exact release you plan to run. See the Docker deployment guide and the Kubernetes runtime guide.
Deploy Langflow on Docker for a quick start
The Docker quickstart uses the official Langflow image and maps host port 7860 to container port 7860. After starting the container, open the host’s port 7860 in a browser. The image’s documented default is LANGFLOW_AUTO_LOGIN=false; provide a strong superuser password unless you have deliberately configured another authentication mode.
A quickstart container is useful for evaluation, but do not assume its database or filesystem state is an adequate persistence plan. Decide where flow and database data will live, how it will be backed up, and how an upgrade will preserve it. Avoid relying on a mutable latest image tag for a production deployment: pin a release and test upgrades against that release’s image and data behavior.
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Use Docker Compose for a configurable single-host stack
Compose is the more suitable Docker route when Langflow needs supporting services or explicit persistence. Langflow’s example stack includes PostgreSQL and persistent volume storage. It can also be adapted to include custom dependencies or packaged flow JSON, as described in the Docker guide.
Check configuration precedence
Langflow’s documented precedence is CLI options over .env values over system environment values. Compose adds its own variable-substitution behavior, so a shell export may not override a value written literally into the Compose configuration. Inspect the rendered Compose configuration before deployment to verify the effective image, credentials, ports, and database settings. The details are documented in Langflow’s environment-variable guide and Docker guide.
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Plan persistence and recovery
Use persistent storage for the database and any other data the deployment must retain, and define a backup and restore process. A persistent volume helps data survive container replacement; it is not, by itself, a backup. For Kubernetes, Langflow likewise strongly recommends an external PostgreSQL database in the architecture described by its deployment architecture guide.
Choose the Kubernetes IDE or runtime deliberately
The IDE and runtime serve different roles. The IDE includes the visual editor and API used to create and manage flows. The production runtime is headless: it focuses on serving flows through the API rather than providing an interactive editing interface. Choose the IDE for authoring and the runtime for production flow serving.
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Deploy the production runtime
The documented runtime route requires a Kubernetes server, kubectl, and Helm. The guide’s workflow is to add the Langflow Helm repository, install the runtime chart, inspect pods and services, and forward port 7860 when you need temporary access to the service. Consult the release-specific guide for the exact repository and chart commands rather than copying commands across versions.
- Prepare the cluster: Confirm that the Kubernetes context points to the intended cluster and that
kubectland Helm are installed. - Configure the runtime: Set the image, packaged flows, credentials, replica count, and resource requests in the chart configuration appropriate to your release.
- Install and inspect: Install the runtime chart, then check the pods and services to confirm that the deployment is ready.
- Test access: Use the documented port-forwarding approach on port 7860 when appropriate, and query the flows API to check that the runtime can serve the configured flows.
- Make an API request: Execute flows through the runtime API; use the IDE separately when flows need to be authored or managed interactively.
The runtime guide shows Kubernetes secretKeyRef references for supplying credentials. It also documents replica count and resource requests as configuration controls. Its chart sets readOnlyRootFilesystem: true by default; disabling that setting weakens the security posture. Check the values for the chart release you install rather than assuming defaults remain unchanged.
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Secure credentials, access, and production startup
Restrict access and protect secrets
Langflow’s authentication guide warns: “Never expose Langflow ports directly to the internet without proper security measures.” Enable an appropriate authentication mode, restrict network access, and use TLS for connections. Store sensitive values in a secret store rather than embedding them in an image or committing them to source control. Kubernetes deployments can provide runtime credentials through Kubernetes Secrets; Langflow’s global-variable documentation also describes storing credentials in Kubernetes Secrets instead of the Langflow database.
Set a consistent LANGFLOW_SECRET_KEY across instances in a multi-instance deployment. Langflow documents that this key protects sensitive values and, in relevant configurations, is used for JWT signing. See the authentication guide, global variables guide, and Kubernetes guide.
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Run production preflight checks
For the production checks documented by Langflow, set LANGFLOW_DEPLOYMENT_PROFILE=prod. Required check failures abort startup before workers start. The documented checks include PostgreSQL reachability and security configuration for MCP, SSRF protection, connector SSRF validation, and allowlists. Verify the variable and checks for your precise release in the production best-practices guide and environment-variable guide.
Size and operate the deployment for its workload
Langflow’s Kubernetes best-practices guidance distinguishes IDE resources from runtime resources and publishes minimums for its described deployment model. Those are version- and workload-sensitive operational guidance, not a universal capacity guarantee. Consult the current best-practices page, then monitor actual resource use and adjust requests and replicas for the flows and concurrency you serve.
Quick Recap
- Keep the IDE and production runtime separate when interactive authoring is not required in the serving environment.
- Use a reachable PostgreSQL database and confirm persistence and backups before relying on the deployment for important flows.
- Review authentication, network exposure, TLS, secret handling, and preflight settings during upgrades as well as initial setup.
- Pin software versions and review release-specific image and chart settings before upgrading.
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