Production layouts for self-hosted Dify: Docker Compose hardening, external PostgreSQL, Redis and object storage, TLS, offline installs, cloud images and Enterprise on Kubernetes.
Taking the default Compose stack from test to production: replacing default secrets, sizing the host beyond the 2 CPU and 4 GiB minimum, which of the core services and dependencies to move out, and the env file layout.
Moving Dify metadata off the bundled db_postgres container to a managed or clustered PostgreSQL: connection pool settings, TLS for the plugin daemon connection, version choice and backup responsibility.
When a team must standardize on a MySQL-compatible database: the supported DB_TYPE values, what changes in ports and tuning, and the trade-offs against Dify's default PostgreSQL.
Removing the single Redis container as a failure point: standalone versus Sentinel versus Cluster modes, TLS and ACL settings, separating the Celery broker database, and key prefixes for shared instances.
Choosing STORAGE_TYPE for uploads, dataset documents and workspace keys: OpenDAL schemes, native S3, Azure Blob and Google Cloud Storage, with MinIO or Ceph inside your own network.
Serving Dify over TLS from the bundled Nginx: certificate placement, the Certbot challenge option, TLS protocol settings, long proxy timeouts for streaming responses, and updating every public URL variable to https.
Putting Dify behind an existing ingress, WAF or load balancer: trusting forwarded headers safely, the WebSocket path for collaboration, upload body limits and the URL variables that must match the external hostname.
Running Dify without internet access: disabling the Marketplace, installing signed .difypkg plugins locally, a private PyPI mirror for plugin dependencies, private image registries, and self-hosted models for every model job.
Installing Dify Enterprise from the official Helm chart: Kubernetes and Helm versions, the six domains, testing versus production resources, external PostgreSQL, Redis, Qdrant and object storage, and OpenShift root requirements.
Cloud infrastructure for Dify Enterprise: the managed services Dify documents for AWS and an Azure proof of concept, how they map to the resources checklist, and network design for private model endpoints.
Estimating hardware for Dify: the Compose minimum, Enterprise testing and production profiles, how knowledge indexing and workflow concurrency drive worker and database load, and when Qdrant needs its own cluster.
Running the Dify API, worker and web services from source with the middleware stack in Docker: when platform changes justify a fork, how upgrades get harder, and the ports the middleware publishes.
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