Greetings from Japan. My devotion to Google runs deep,
and colleagues jokingly ask if I'm planning to write a company anthem.
Well, it's a joke.
‘Opal is just a watered-down version of Dify!’ they're shouting on Reddit.
But is loving Google's ecosystem really a crime?
Having built my professional career alongside Google Workspace,
I may be biased—but that's precisely why I can explain that Opal isn't ‘inferior,’
it's just strategically different.
Pouring myself another cup of Google-branded tea, let's get down to business.
Introduction
In July 2025, Google Labs launched the experimental no-code AI tool “Opal” in beta in the United States. Subsequently, in October, it was rolled out to 15 countries including Japan, causing significant ripples in the AI workflow automation market. So, I've been trying it out, observing the -2025-10-08), sending significant ripples through the AI workflow automation market.
So, I've been playing around with it, keeping an eye on the wider scene and doing some research, and compiling links and such took quite a while, eating into my Japanese-style three-day weekend.
But hey, it's fun, so it's all good!
To be perfectly honest, within the technical community, it's known as “a substandard version of Dify”. or “a downgraded version of n8n” (https://www.reddit.com/r/singularity/comments/1nni7l7/googles_opal_is_disappointing_me_and_here_is_why/).
Well, that's understandable.
Having tried it out, I did wonder whether there's really any need to switch if you've already implemented Dify or are already using it.
Is Opal really a “downgraded version”? What I realised after actually using it was that Opal isn't inferior; it has a distinctly different purpose and strategy.
It really made me feel that sense of ‘Google's service’ at its core.
I absolutely love it, and if you called it Google devotion, I'd say yes! It's been with me throughout my entire working life.
That said, well, it probably isn't really aimed at people who are already ‘properly doing AI’ or ‘using AI to run their business’.
What exactly is Opal? A new approach called “Vibe Coding” (Google-style)
Opal's most significant feature is its adoption of an approach Google calls “Vibe Coding”.
This enables users to simply dictate ideas in natural language, whereupon the AI automatically constructs multi-step workflows and delivers them as ready-to-use mini-apps.
I'd also mentioned it on X (formerly Twitter) with ‘It's out!’, so I'll post it here for reference.
https://x.com/GoogleLabs/status/1975627276575498338
Opal's Core Features
- Natural Language Driven: Simply describe desired functions or logic in plain Japanese
- Simple Three-Element Structure: ‘Input (User Input)’ → ‘AI Processing (Generate)’ → ‘Output (Output)’
- Instant App Creation: Created workflows are automatically published as apps and can be shared via URL
- Integration with the Google AI Ecosystem: Access Gemini, DeepResearch, Veo, Nano Banana, and more
Put simply, it is an enhanced version of Gems (custom AI assistants you can create within the Gemini app). It enables you to achieve multi-step processes that the Gemini app alone cannot handle, all without any coding.
For those wishing to explore what Gems can do, this article provides a clear overview and is well worth consulting.
As it's an article from a Japanese technical site, you may need to translate it. My apologies.
Considering why it's called a ‘downgraded version’
Criticisms of Opal certainly have some validity. However, they can be understood as the result of strategic trade-offs.
Or rather, it gives the impression that it's targeting a more casual audience rather than being ‘engineer-focused’.
Criticism 1: Locked into the Gemini model
This is the most significant criticism. For engineers and prompt engineers, the inability to switch between Claude, GPT-4o, and Gemini depending on the task's nature is certainly a drawback.
Honestly, the appropriate model really does differ depending on the use case, doesn't it? I believe I've mentioned this myself in the past.
Compared to multi-model platforms like Dify or n8n, this limitation feels rather restrictive.
Or rather, if you're used to it, wouldn't ◎◎ be perfectly adequate?
However, there is a reason for this.
To realise Opal's core “Vibe Coding” (automatic generation of logic via natural language), the underlying large language model must fully comprehend the workflow logic builder's operation and output format.
By anchoring to Gemini, Opal maximises the reliability and speed of workflow auto-transformation while ensuring a stable user experience for non-technical users.
This isn't a flaw in functionality; rather, I felt it was the result of prioritising a user experience where ‘non-technical users can use it without hesitation’ above all else.
Or rather, I suspect it was intentional.
Criticism 2: Limited Integration Capabilities
While n8n boasts integrations with over 1,100 applications (https://hostadvice.com/blog/ai/automation/n8n-vs-dify/), Opal's native integrations are heavily skewed towards Google Workspace and GCP.
In contrast, Google officially supports connections with Zapier and Tray.io. This means Opal adopts a strategy of focusing on its core functionality—building AI logic—while delegating external SaaS connections to Zapier.
Frankly, it’s not necessary to integrate everything and anything. Besides, existing Google services are already rather overwhelming in their capabilities…
Google, thank you as always.
I find myself thinking that as long as you've got Google Workspace covered, you're pretty much set...
It's not omnipotent, mind you. It's not Zeus, even if it is Google.
There's antitrust law to consider, after all.
Criticism 3: Unstable due to being a beta version
Stability issues have indeed been reported, such as errors occurring during complex document processing (https://www.reddit.com/r/vibecoding/comments/1mfwjs3/googles_opal_sucks_venting_my_frustrations/).
However, Google is making rapid improvements, such as real-time error display, enhanced step-by-step debugging, and the introduction of parallel processing.
I suppose it can't be helped – it is a beta, after all.
In fact, releasing this in beta is typical Google. I might be cutting them some slack, but that's how I feel.
Three Strategic Strengths Discovered Through Actual Use
Opal's true value lies in the strategic strengths born from its simplicity.
Strength 1: Overwhelming Speed of Proof of Concept
According to Opal's official review, it can realise a working prototype from an idea within 60 seconds.
Or rather, if what you want to create or achieve is clear and not overly complex, it's genuinely fast.
Or rather, if you can organise it logically in Japanese, you could probably whip it up using just Japanese.
It might demand quite a bit from the individual's skill, but even so, it feels like the barrier has been lowered significantly for non-engineers.
It's terrifyingly good... that's what I think.
This rapid trial-and-error cycle is optimised not for building large-scale SaaS applications, but for constructing internal research workflows and specific task automation apps.
Whereas traditional no-code tools demanded skills in node connections and UI manipulation, Opal might well have reduced the learning curve to near zero—a thought that genuinely inspired awe.
Strength 2: Seamless Integration with the Google Ecosystem
Opal goes beyond mere functional integration to offer deep integration with Google Workspace.
- Direct output to Google Docs, Sheets, and Slides
- Seamless file loading from Google Drive
- Access management using Google Groups
The ability to combine cutting-edge multimodal AI capabilities using only Google account authentication, without multiple API keys or complex authentication setups, offers an overwhelming advantage for businesses operating within the Google ecosystem.
This is seriously powerful.
Most companies already have Google accounts, I reckon. They basically run their operations using Google services...
Though some might use separate chat services, perhaps.
Strength 3: A phased migration path to the enterprise
Opal's most crucial strategic position is to serve as a phased migration path to Google's enterprise products.
Gem (single prompt)
↓
Opal (multi-step workflows)
↓
Gemini Enterprise / Agent Builder (production environment)
Gemini Enterprise incorporates enterprise-essential features that Opal deliberately lacks, such as version control, audit logs, advanced evaluation, and guardrails.
One could interpret this as enabling a step up within Google services.
Opal is designed as a strategic funnel, offering users a successful experience in building AI workflows for free (or at low cost), then encouraging migration to paid enterprise products once their ideas have matured.
Isn't that brilliant? It's properly structured so users can step up from their current position.
And then there's the funding. (The struggle that money's needed everywhere is universal, isn't it...)
Comparison with other tools: It's not a matter of superiority or inferiority because their purposes differ
| Perspective | Opal | Dify | n8n | Agent Builder |
|---|---|---|---|---|
| Design Philosophy | Democratisation of AI adoption | AI-native application development | System integration automation | Enterprise AI |
| Model | Gemini fixed | Multi-model support | Via external API | Gemini/Google AI |
| Integration Scope | Google ecosystem + Zapier | LLM/RAG-centric | 1,100+ apps | Enterprise data/audit |
| Target Users | Non-technical users | AI developers | Technical staff/SRE | Large enterprises |
| Production Readiness | Low (for PoC) | Medium to high | High | Highest |
Opal vs Dify: Who Creates the AI Logic?
Dify is an “AI engineering platform” focused on building RAG pipelines and advanced agent strategies.
The fundamental difference lies in the fact that Dify allows users (engineers) to design the AI's logic, whereas Opal generates the logic itself.
Therefore, I feel Opal might hold a slight advantage in making it less necessary to consciously focus on engineering aspects.
However, when considering thorough in-house utilisation and the need for meticulous attention to detail alongside model flexibility, I still lean towards Dify...
But honestly, if it's a much later stage (like several years down the line) where they're thinking ‘We want to use AI’ or ‘We absolutely refuse to spend money on it!’, then I might be tempted to say Google Opal would be fine...
It really comes down to when exactly they plan to use AI and how seriously they intend to implement it. Hmm, personally, given the current situation, I think Dify would be better. But then again, there's that trust in the Google brand name, so if I were proposing implementation to senior management, I'd be torn.
Opal vs n8n: Differences in the Scope of Automation
n8n is a versatile tool for automating data movement and processes between IT systems.
Whereas n8n provides “workflows for system integration”, Opal specialises in “visualising AI thought processes (multi-step prompts) and rapidly turning them into applications”.
I've dabbled with n8n personally, but it just doesn't quite suit my taste – purely subjective, mind. So if anyone with deeper insights here could share their thoughts in the comments, I'd be chuffed.
I do feel their target audiences and the domains they automate are fundamentally different.
When should one choose Opal?
Opal is the optimal solution in the following cases where speed, simplicity, cost, and affinity with the Google ecosystem take precedence over model flexibility.
When to choose Opal
- Proof of Concept within the Google Ecosystem: Information gathering, summarisation, and report generation within Google Workspace, including Gmail, Drive, Docs, and Sheets
- AI Adoption Projects Led by Non-Technical Users: Marketing and HR departments, and smaller organisations (SMEs with relatively few employees) seeking to start using AI without programming or complex configuration
- Ultra-rapid idea validation: Verify within minutes whether a task can be achieved using Gemini, enabling a development style where failure carries minimal risk
- Trial of Google's proprietary AI models: For those wishing to easily test DeepResearch, Veo, Nano Banana, and similar offerings
When to choose Dify/n8n
- Requires model selection based on use case: Claude, GPT-4o, and Gemini should be deployed appropriately for each task
- Stable production deployment is essential: This remains the critical factor for enterprise adoption
- Complex external integrations required: Connectivity with diverse business tools such as Salesforce and Slack
- Implementation of advanced RAG and agent strategies
Summary: Opal is not a “downgraded version” but an 'entry point'
Criticism that Google Opal is a “degraded Dify” stems from a misunderstanding of Opal's strategic positioning.
Opal's constraints are deliberately designed to lower the barrier to entry for building AI workflows to the absolute minimum, making it a strategic democratisation tool enabling non-technical users to experiment with AI within their own workflows.
If OpenAI introduced humanity to the experience of conversing with AI through ChatGPT, then Google Opal may well be introducing humanity to the experience of AI workflows.
In terms of model flexibility, it certainly falls short of Dify and n8n. However, I think that's perfectly acceptable because their purposes are different.
- Opal: The first step in AI workflows, proof of concept, and onboarding to Google products
- Dify/n8n: Full-scale production operations and handling complex requirements
There's no need to master every tool. Simply choose the right one for the task and your skill level.
It's not so different from engineers specialising in front-end or back-end development, I suppose.
As the competitive focus in AI workflow construction shifts from “feature richness” to “UX simplicity”, Opal is playing a vital role in accelerating the democratisation of AI development by introducing a new paradigm: “Vibe Coding”.
If you've never touched anything like this before, start with Opal. If you then think, ‘Actually, I do want to use different models,’ you can move on to Dify or n8n.
That gradual approach might be the true value Opal offers.
But having tried it, I did think: for non-engineers, this level of accessibility is probably just right.
Taking that first step – it needs to be this accessible, otherwise it's a bit much...? I wonder.
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