Dify.ai Workflow Platform

Deploy Dify.ai on Clore.ai — build production-ready AI workflows, RAG pipelines, and agent applications with a visual interface at GPU cloud prices.

Overview

Dify.ai is an open-source LLM application development platform with 114K+ GitHub stars. It combines a visual workflow builder, retrieval-augmented generation (RAG) pipeline, agent orchestration, model management, and a one-click API deployment layer into a single self-hostable stack.

On Clore.ai you can run the full Dify stack — including its Postgres database, Redis cache, Weaviate vector store, Nginx reverse proxy, API workers, and web frontend — on a rented GPU server for as little as $0.20–$0.35/hr (RTX 3090/4090). The GPU is optional for Dify itself, but becomes essential when you integrate local model inference through Ollama or vLLM backends.

Key capabilities:

• 🔄 Visual workflow builder — drag-and-drop LLM pipelines with branching, loops, and conditional logic

• 📚 RAG pipeline — upload PDFs, URLs, Notion pages; chunking + embedding + retrieval all managed in UI

• 🤖 Agent mode — ReAct and function-calling agents with tool use (web search, code interpreter, custom APIs)

• 🚀 API-first — every app generates a REST endpoint and SDK snippets instantly

• 🔌 100+ model integrations — OpenAI, Anthropic, Mistral, Cohere, plus local models via Ollama/vLLM

• 🏢 Multi-tenant — teams, workspaces, RBAC, usage quotas

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Requirements

Dify runs as a multi-container Docker Compose stack. The minimum viable server for development is a CPU-only instance; for production with local model inference you'll want a GPU node.

Configuration	GPU	VRAM	System RAM	Disk	Clore.ai Price
Minimal (API keys only)	None / CPU	—	8 GB	30 GB	~$0.05/hr (CPU)
Standard	RTX 3080	10 GB	16 GB	50 GB	~$0.15/hr
Recommended	RTX 3090 / 4090	24 GB	32 GB	80 GB	$0.20–0.35/hr
Production + Local LLM	A100 80 GB	80 GB	64 GB	200 GB	~$1.10/hr
High-throughput	H100 SXM	80 GB	128 GB	500 GB	~$2.50/hr

Tip: If you only use cloud API providers (OpenAI, Anthropic, etc.), any 2-core CPU instance with 8 GB RAM works. A GPU matters only when running local models via Ollama or vLLM — see GPU Acceleration below.

Disk note

Weaviate and Postgres data grow quickly with document uploads. Provision at least 50 GB and mount persistent storage via Clore.ai's volume options.

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Quick Start

1. Rent a Clore.ai server

Browse to clore.ai, filter by your desired GPU, and deploy a server with:

• Docker pre-installed (all Clore images include it)

• Exposed ports 80 and 443 (add custom ports in the offer settings if needed)

• SSH access enabled

2. Connect and prepare the server

# SSH into your Clore server
ssh root@<clore-server-ip> -p <ssh-port>

# Update system packages
apt-get update && apt-get upgrade -y

# Verify Docker is available
docker --version
docker compose version   # Should be v2.x

3. Clone Dify and launch

# Clone the repository
git clone https://github.com/langgenius/dify.git
cd dify/docker

# Copy the example environment file
cp .env.example .env

# (Optional) Edit settings before launch
nano .env

# Pull all images and start all services in background
docker compose up -d

# Watch logs during startup (takes 2-3 minutes first run)
docker compose logs -f

4. Verify all services are healthy

# Check container status
docker compose ps

# Expected output:
# NAME                    STATUS
# docker-api-1            Up (healthy)
# docker-web-1            Up (healthy)
# docker-worker-1         Up (healthy)
# docker-nginx-1          Up
# docker-db-1             Up (healthy)
# docker-redis-1          Up (healthy)
# docker-weaviate-1       Up (healthy)
# docker-sandbox-1        Up (healthy)

5. Access the web UI

Open your browser and navigate to:

http://<clore-server-ip>:80

On first launch, Dify will redirect you to the setup wizard to create the admin account. Complete the wizard, then log in.

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Configuration

All configuration lives in dify/docker/.env. Here are the most important settings:

Essential environment variables

# ── Secret Keys (CHANGE THESE) ──────────────────────────────────────────────
SECRET_KEY=your-super-secret-key-change-this-immediately
# Generate a strong key:
# python3 -c "import secrets; print(secrets.token_hex(32))"

# ── Application URL ──────────────────────────────────────────────────────────
# Set to your server's public IP or domain
CONSOLE_WEB_URL=http://<clore-server-ip>
APP_WEB_URL=http://<clore-server-ip>

# ── Database (Postgres) ─────────────────────────────────────────────────────
DB_USERNAME=postgres
DB_PASSWORD=difyai123456          # Change in production!
DB_HOST=db
DB_PORT=5432
DB_DATABASE=dify

# ── Redis ───────────────────────────────────────────────────────────────────
REDIS_HOST=redis
REDIS_PORT=6379
REDIS_PASSWORD=difyai123456       # Change in production!

# ── Storage (local disk by default) ─────────────────────────────────────────
STORAGE_TYPE=local
# Or use S3-compatible storage:
# STORAGE_TYPE=s3
# S3_ENDPOINT=https://s3.amazonaws.com
# S3_BUCKET_NAME=your-dify-bucket
# S3_ACCESS_KEY=...
# S3_SECRET_KEY=...

# ── Default LLM provider ─────────────────────────────────────────────────────
# Set in the UI after login, or pre-configure here:
# OPENAI_API_KEY=sk-...

Changing the exposed port

By default Nginx listens on port 80. To change it:

# In docker-compose.yaml, edit the nginx service:
# ports:
#   - "8080:80"   ← change 8080 to your preferred host port

# Then restart:
docker compose down && docker compose up -d

Persistent data volumes

Dify's Compose file mounts these volumes by default:

volumes:
  oradata:          # Postgres data
  redis_data:       # Redis AOF
  weaviate_data:    # Vector store
  app_storage:      # Uploaded documents and generated files

To back up:

# Stop services first, then tar the Docker volumes
docker compose stop
docker run --rm \
  -v docker_oradata:/data \
  -v $(pwd)/backups:/backup \
  alpine tar czf /backup/postgres-$(date +%Y%m%d).tar.gz /data
docker compose start

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GPU Acceleration

Dify's core platform is CPU-based, but you unlock local model inference by integrating Ollama or vLLM as model providers — both benefit enormously from a GPU.

Option A: Ollama sidecar (easiest)

Run Ollama alongside Dify on the same Clore server:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model (e.g. Llama 3 8B)
ollama pull llama3:8b

# Verify GPU is used
ollama run llama3:8b "Hello!"
nvidia-smi   # Should show ollama process using VRAM

Then in Dify UI → Settings → Model Providers → Ollama:

• Base URL: http://localhost:11434

• Select your model and save

For a full Ollama guide, see language-models/ollama.md.

Option B: vLLM sidecar (high-throughput)

# Run vLLM as a separate container with GPU passthrough
docker run -d \
  --name vllm \
  --gpus all \
  --runtime nvidia \
  -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e HUGGING_FACE_HUB_TOKEN=<your-hf-token> \
  vllm/vllm-openai:latest \
  --model mistralai/Mistral-7B-Instruct-v0.2 \
  --dtype auto \
  --max-model-len 4096

# Verify endpoint
curl http://localhost:8000/v1/models

Then in Dify UI → Settings → Model Providers → OpenAI-compatible:

• Base URL: http://localhost:8000/v1

• API Key: dummy

• Model name: mistralai/Mistral-7B-Instruct-v0.2

For full vLLM setup, see language-models/vllm.md.

GPU memory recommendations for local models

Model	VRAM Required	Recommended Clore GPU
Llama 3 8B (Q4)	6 GB	RTX 3060
Llama 3 8B (FP16)	16 GB	RTX 3090 / 4090
Mistral 7B (Q4)	5 GB	RTX 3060
Llama 3 70B (Q4)	40 GB	A100 40GB
Llama 3 70B (FP16)	140 GB	2× H100

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Tips & Best Practices

Cost optimization on Clore.ai

# Use spot pricing — Dify's databases maintain state in volumes
# so you can stop/restart the instance cheaply

# Before stopping your Clore instance, dump the DB:
docker exec docker-db-1 pg_dump -U postgres dify > dify_backup_$(date +%Y%m%d).sql

# Compress the storage volume for transfer
tar czf dify_storage_$(date +%Y%m%d).tar.gz \
  $(docker volume inspect docker_app_storage --format '{{.Mountpoint}}')

Enable HTTPS with Caddy (recommended for production)

# Add Caddy to docker-compose.yaml or run standalone
docker run -d \
  --name caddy \
  --network docker_default \
  -p 443:443 -p 80:80 \
  -v $PWD/Caddyfile:/etc/caddy/Caddyfile \
  -v caddy_data:/data \
  caddy:latest

# Caddyfile contents:
# your-domain.com {
#     reverse_proxy nginx:80
# }

Scale workers for heavy workloads

# Run 4 celery workers instead of 1
docker compose up -d --scale worker=4

Monitor resource usage

# Live container stats
docker stats

# GPU utilization (if using local models)
watch -n1 nvidia-smi

# Dify application logs
docker compose logs -f api worker

RAG performance tuning

• Set chunk size to 512–1024 tokens for most document types

• Enable parent-child retrieval for long documents in Dataset settings

• Use hybrid search (keyword + vector) for better recall on technical content

• Index documents during off-peak hours to avoid API rate limits

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Troubleshooting

Services keep restarting

# Check logs for the failing service
docker compose logs --tail=50 api
docker compose logs --tail=50 worker

# Common cause: wrong SECRET_KEY or DB connection
# Ensure .env SECRET_KEY is set and unique
grep SECRET_KEY .env

"Migration failed" on startup

# Run DB migrations manually
docker compose exec api flask db upgrade

# If that fails, check Postgres is healthy first
docker compose exec db pg_isready -U postgres

Can't connect to Ollama from Dify

# Ollama by default binds to 127.0.0.1
# Change it to listen on all interfaces:
OLLAMA_HOST=0.0.0.0 ollama serve

# Or edit the systemd service:
systemctl edit ollama
# Add under [Service]:
# Environment="OLLAMA_HOST=0.0.0.0"
systemctl restart ollama

# Test from within a Dify container:
docker compose exec api curl http://host.docker.internal:11434/api/tags

Out of disk space

# Check volume sizes
df -h
docker system df

# Remove unused images
docker image prune -a

# Weaviate logs can grow large — rotate them
docker compose exec weaviate truncate -s 0 /tmp/weaviate.log

Weaviate vector store errors

# Reset Weaviate (WARNING: deletes all embeddings, re-index needed)
docker compose stop weaviate
docker volume rm docker_weaviate_data
docker compose up -d weaviate

# Then re-index your datasets in Dify UI → Datasets → [dataset] → Re-index

Port 80 already in use

# Find what's using port 80
ss -tlnp | grep :80
# or
lsof -i :80

# Stop conflicting service or change Dify's nginx port in docker-compose.yaml

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Further Reading

• Dify Official Documentation

• Dify GitHub Repository

• Dify Self-hosting Guide

• Running Ollama on Clore.ai

• Running vLLM on Clore.ai

• Clore.ai GPU Comparison

• Dify Community Discord