【Definitive Guide】n8n vs Dify vs Opal (Google) — Workflow Automation / LLM App Development / Conversational “Mini AI Apps” Compared (2025 Edition)

Key points (Inverted Pyramid):

• n8n = general-purpose workflow automation. Balances drag & drop with code, flexible on-prem/cloud deployment, and execution-based pricing. Scales well as an automation backbone. With AI nodes, can build multi-step AI agents.
• Dify = open-source LLM app platform. Supports RAG ingestion, agents, workflows, model management, observability in one UI. Works across OpenAI / Anthropic / Azure OpenAI / Hugging Face / Replicate. Paid commercial tiers are available.
• Opal (Google) = “conversational mini AI app maker”. Describe tasks in natural language → auto-converted into workflows. Aimed at rapid prototyping, idea validation, and team sharing using Gemini models. Still experimental.
• How to choose:
  • Ongoing operations × SaaS integrations × robust reproducibility → n8n
  • Knowledge-powered LLM apps (RAG, agents) → Dify
  • Idea experiments, internal demos, lightweight bots → Opal
• Pricing & ops: n8n = free self-host + execution-based cloud pricing; Dify = free / Pro / Business message-credit plans; Opal = Labs experimental service positioned for prototyping.

1｜The “DNA” of Each Tool

n8n: Open workflow automation platform, positioned between Zapier and Make for developers. Mixes no-code blocks with custom code, runs on-prem or cloud. Recently emphasizes AI workflows and multi-step agents for automation-heavy teams.

Dify: Open-source LLMOps platform. One UI covers RAG dataset mgmt, prompt iteration, agents, workflows, model switching, logs/observability. Supports multiple model providers. Philosophy: bridge prototype → production.

Opal (Google): Announced July 2025. Experimental no-code “mini AI app” builder. Write requirements in natural language → converted to small AI workflows. Great for PoCs, lightweight demos, sharing ideas quickly. Built around Gemini.

Note: “Opal” here refers to Google’s Opal. Not to be confused with OPAL (Open Policy Administration Layer) in IAM.

2｜Strengths by Use Case

• n8n: Automation backbone
  Routine ops in HR, finance, sales; SaaS-to-SaaS integration; webhooks → transforms → API writes. With AI nodes: e.g., ticket classification → RAG response → CRM logging.

• Dify: Knowledge-powered LLM apps
  Internal knowledge QA (RAG), FAQ bots, summarization, extraction, doc QA, light agents. Optimized for information-heavy AI apps with observability + governance.

• Opal: Conversational mini apps
  Fast PoC needs: internal demos, small data wrangling, shared workflows. Conversational input → visual workflow with low learning curve. Perfect for “first touch with AI” inside teams.

3｜Feature Comparison

3-1. Builder UX

• n8n: block canvas + code. Supports branches, loops, error handling. Great for complex flows.
• Dify: LLM-specific visualization. Prompting, RAG, evaluation blocks built-in. Optimized for AI production workflows.
• Opal: natural-language → workflow conversion. Templates + conversational editing = near-zero learning curve.

3-2. Integrations

• n8n: extensive SaaS connectors, IT/Ops/Dev tools, on-prem. Great for corporate “plumbing.”
• Dify: strong in model provider integration. Manage RAG data connections in-UI.
• Opal: Gemini-focused. Easy Google-account sharing. Lightweight app sharing is the core.

3-3. RAG & Agents

• n8n: build-your-own using external vector DB + LLM. Freedom = high, but design burden on you.
• Dify: RAG + agents included out-of-the-box. With evaluation + observability.
• Opal: simple chains/mini apps. Full-scale RAG best with Dify.

3-4. Logs & Observability

• n8n: logs execution success/fail + retries. Great for ops reproducibility. No native LLM eval.
• Dify: platform-native logging of prompts, RAG calls, model runs. Enables evaluation + reproducibility.
• Opal: prioritizes speed of iteration + sharing, not audit-level ops.

4｜Pricing & Delivery

• n8n: community self-host or cloud. Cloud = execution-count pricing (budget-friendly vs per-step). Startup discounts + enterprise plans.
• Dify: message-credit plans (Free / Pro / Business). Limits on workspaces, app count, knowledge storage, log retention are explicit. Education/student free plans too.
• Opal: Google Labs experimental service. Messaging: for prototyping & idea validation. Availability may evolve by region.

5｜Sample Scenarios

n8n: Support ticket → AI response → CRM log

Trigger: inbound ticket. Process: detect lang → classify → RAG answer → translate. Output: reply + CRM record + ticket update. Strength = branches, retries, error handling.

Dify: Internal knowledge QA bot (RAG)

Ingest docs → vectorize → RAG config. Bot = workflow with pre/post steps. Ops: switch models, review logs, annotate + retrain. Strength = integrated RAG + observability.

Opal: Invoice PDF summarizer mini app

Instruction: “upload PDF → extract keypoints → export CSV.” Auto-generated workflow. Share link internally. Strength = few-minutes PoC with Gemini + templates.

6｜Security & Governance

• Data location & control

  • n8n: on-prem option. Easy incident tracing via audit logs + re-execution.
  • Dify: control over model endpoints, knowledge mgmt, log retention, throttling.
  • Opal: experiment/demo focus. Use anonymized data; avoid external sharing unless policy allows.

• Model switching & auditability

  • Dify excels with multi-model observability.
  • n8n = ops-level reproducibility. LLM quality eval needs add-ons.

7｜Tool Relationships

• n8n vs Dify:

  • Overlap = simple text/AI tasks.
  • Distinction = n8n = SaaS automation backbone; Dify = knowledge-powered LLM production.
  • Best practice = hybrid (n8n for plumbing, Dify for RAG/agents).

• Opal vs n8n/Dify:

  • Opal = ultra-low-friction entry. PoC in Opal → production in n8n/Dify.

8｜Selection Checklist (12 Qs)

1. Need self-host? (Yes → n8n or Dify)
2. Heavy SaaS plumbing? (Yes → n8n)
3. Safe RAG knowledge apps? (Yes → Dify)
4. Multi-model switching? (Yes → Dify)
5. Need instant prototype? (Yes → Opal)
6. Built-in LLM observability? (Yes → Dify)
7. Tolerate branches/retries complexity? (Yes → n8n)
8. Conversational editing + sharing? (Yes → Opal)
9. Budget by execution count? (Yes → n8n Cloud)
10. Budget by message credits? (Yes → Dify)
11. Mixed eng/non-eng users? (n8n + Opal)
12. Need production-ready design? (n8n = ops, Dify = LLM ops, Opal = PoC)

9｜Who Benefits?

• IT/Ops: routine account mgmt, API retries → n8n.
• Support/CS: RAG + observability → Dify.
• New biz/PM: fast demos with Gemini templates → Opal.
• Dev/Data: model A/B with Dify, ETL with n8n.
• Execs: predictable spend (n8n exec count, Dify credits). Opal for AI literacy.

10｜Ready-to-Use Templates

1. RAG Bot (Dify): ingest FAQ docs, metrics = accuracy/quotation/hallucination, weekly review + retrain in-UI.
2. SaaS Pipeline (n8n): new lead trigger → dedup → scoring (AI) → assign. Handle 429 with backoff + retries.
3. Internal PoC (Opal): meeting note → keypoints → CRM memo. Write in NL → auto-generated workflow → share link.

11｜FAQ

Q. Is Dify really open source?
A. Yes, with RAG, agents, workflows, model mgmt, observability in one UI. Supports OpenAI, Anthropic, Azure, HF, Replicate.

Q. Is n8n strong outside AI?
A. Absolutely. IT/Ops automation + SaaS integrations are core. AI nodes are add-ons.

Q. Is Opal production-ready?
A. No, aimed at prototyping, demos, internal sharing. For ops-grade, use n8n/Dify.

12｜Editorial Summary: How to Choose

• Base automation = n8n. Handles branches/retries. Insert AI where needed.
• Production LLM apps = Dify. Built-in RAG + observability. Straight line from prototype to production.
• Fastest PoC = Opal. Conversational → workflow. Iterate many PoCs, then graduate to n8n/Dify.

Three-tier strategy (Opal → Dify/n8n) = the 2025 reality.

References (primary / trusted)

• n8n: product site (AI workflows/agents), pricing docs.
• Dify: OSS LLM platform site, pricing plans (credit tiers).
• Opal (Google): dev blog, Labs landing, 3rd-party explainer (Gemini, no-code, PoC focus).