Dify vs Langflow

Out of the box, Langflow does not open an unauthenticated admin console. Its official release packages and Docker images disable automatic login by default and require a superuser password set through LANGFLOW_SUPERUSER_PASSWORD. Before you expose a Langflow instance to anyone, set that variable from your secrets store, confirm the password is not in a committed compose file or shell history, and test that a browser session without credentials is refused. Langflow's own identity model beyond the superuser account is something to confirm in the current documentation and your own test environment, not something to assume from the project README.

Dify handles the same question differently. Its SECRET_KEY signs session cookies and JWTs and also encrypts OAuth credentials, so the value is part of the trust boundary, not just a setting. The environment reference warns that changing it after deployment logs out all users. Generate it once, store it in your secrets manager, and treat rotation as a planned outage. Workspace encryption keys can be held locally or in Azure Key Vault, which is a decision to make before you load provider credentials.

Where SSO matters, the gap is in what is documented as available. Dify Enterprise lists OpenID Connect, SAML and OAuth2, with identity providers such as Microsoft Entra ID, Okta and GitHub, and separate SSO scopes for workspace access and web app access. For Langflow, the equivalent is not established by the material reviewed here, so ask the vendor and your architects which identity options a self-hosted deployment supports, whether they require custom work behind a reverse proxy such as Caddy or Nginx, and who owns session management. Confirm each option in a staging environment with your own identity provider before you commit to either platform.

Both platforms are free to download and run, so the running cost sits in the infrastructure, the people who build and maintain flows or apps, the model and vector store choices, and any paid edition you add. Start by separating one-time costs (planning, initial deployment, integration with your identity provider, hardening and backup design) from recurring costs (hosting, model usage, upgrades, testing and support). For Dify, the Docker Compose guide asks for at least 2 CPU cores and 4 GiB of RAM and starts PostgreSQL, Redis, Weaviate, Nginx and a code sandbox alongside the api, worker and web services, so the baseline is a multi-container stack to size and patch. Langflow installs as a Python package, a Docker image or a desktop app, and its production guidance moves you from SQLite to PostgreSQL, so its baseline is smaller but the database and Kubernetes or reverse proxy setup still need an owner.

Upgrade cadence is a recurring cost you should price in. Dify's releases have arrived roughly monthly, with 1.16.0 on July 17, 2026, 1.17.0 on August 25, 2026 and 1.17.1 on September 10, 2026, so plan for regular test and rollout cycles. Langflow publishes weekly patch releases, which keeps fixes flowing but means more frequent change to review, especially for flows that depend on custom Python components. Model provider and vector store choices move the bill more than either platform's license. Hosted model usage is billed by the provider, and the vector store you pick (Weaviate by default in Dify, or options such as Qdrant, Milvus or pgvector) determines whether you run another stateful service. Dify's published prices are the only ones to cite: the self-hosted Community edition is free and limited to a single workspace, the self-hosted Enterprise edition is custom priced, and Dify Cloud Professional is listed at 590 US dollars per year. Langflow has no published paid edition in the supplied facts, so confirm with its maintainers what support, if any, you can buy.

When you request a quote, ask for each item separately: the infrastructure sizing and who operates it, the skills and hours needed to build and maintain flows or apps, the database and vector store configuration, the upgrade and regression testing process, the number of workspaces or tenants in scope, and what support window and response commitments are included. For Dify, confirm early whether a single workspace is enough, because a second tenant changes the license and edition question. For Langflow, ask who will own the MCP and API endpoints once flows are published. DeployOpen plans, implements, hardens, backs up, upgrades and supports either platform inside your infrastructure under a written, fixed scope, and provides retainers for ongoing support, but it does not publish prices, so the figures come from your agreed scope.

Most delays on either platform come from ownership and scope questions that were never written down, not from the software itself. On Dify, the work that stretches a project is knowledge base ingestion, model provider configuration, the choice of vector store and the workspace and tenant model. Dify's default metadata database is PostgreSQL, and the vector store defaults to Weaviate with options such as Qdrant, Milvus and pgvector, so the vector store decision should be made before any documents are loaded, because re-indexing a large corpus into a different store is rework your team pays for. Decide early how many workspaces you need, since the Community edition is limited to a single workspace, and whether SSO for Enterprise will be in scope, because SSO is one of the features that only Enterprise includes.

On Langflow, the long tail usually sits in custom Python component development, the move off SQLite and the HTTPS and high availability layer. Langflow uses SQLite by default and its docs say PostgreSQL is better suited to production, so plan the database cutover and a data migration path for flows, users and stored state before go-live rather than after. Official Langflow packages disable automatic login and require LANGFLOW_SUPERUSER_PASSWORD, which means someone must own the credential and the reverse proxy configuration behind Caddy or Nginx. If you plan to run Kubernetes for high availability, confirm with your platform team who operates that cluster before the project starts, since the Langflow deployment docs describe it as the production path but do not size it for you.

Across both platforms, write the scope before work starts and define four things: who builds flows or apps and who operates the platform, the licensing position for multi-tenant or customer-facing use (Dify's license forbids operating a multi-tenant environment from its source without written authorization), the identity integration and its owner, and the data migration plan including any prior flows or knowledge bases. Ask each vendor or implementation partner for a quote that lists these items separately, so that a change to any one of them shows up as a scope change rather than an overrun.