Just saw the announcement drop in my feed. Perplexity Pages... honestly, my first reaction was "oh, another AI wants to be a content creator." But after poking around the examples, I'm kinda intrigued. It feels like they're aiming for something between a polished research report and a shareable knowledge base article, all generated from a thread of questions.
As someone who's constantly drafting post-mortems, cost analysis reports, and architecture docs for my team, I can see the potential utility. Imagine feeding it a query like:
```text
Compare the cost profiles of AWS Lambda Provisioned Concurrency vs. AWS App Runner for a low-traffic API with sporadic bursts. Include specific pricing in us-east-1 and mention monitoring metrics to watch.
```
And getting back a well-structured, cited document you could share internally. That's more valuable than a simple Q&A thread. The "focus on facts and citations" angle is a smart differentiator from generic blog generators.
My big question for the community is: **Where does this actually fit in a tech workflow?** Is it for:
- Personal learning/note-taking (like a supercharged digital garden)?
- Public "show your work" for technical topics?
- Internal team documentation driven by AI?
I'm also low-key worried about the pricing model down the line. If this becomes a core feature, does it stay in the Pro tier, or will it become a new add-on? My FinOps senses are tingling.
Has anyone gotten early access to try it with a technical deep-dive? I'm really curious about how it handles complex, multi-faceted cloud architecture questions compared to just asking a series of normal Perplexity queries.
cost first, then scale
You've hit on a promising use case with internal documentation drafts like post-mortems and cost analyses. The immediate utility I see is in accelerating the first draft phase, where gathering accurate, cited data is the biggest time sink.
However, that specific AWS pricing example is also a perfect illustration of its limitation. These services have nuanced pricing models that change quarterly. A generated page would give you a snapshot based on its training data, but for a real internal report, you'd still need to manually verify every figure against the latest AWS Pricing Calculator API or the vendor's current documentation. The value isn't in the final numbers, but in the structure and the list of metrics to watch it provides.
So to answer your workflow question, I'd place it firmly in personal learning and draft scaffolding. It's for creating a shareable *starting point* that a human expert must then validate and own. Using it for public "show your work" carries significant reputational risk if any cited fact is stale.
Your specific AWS cost analysis example is actually the perfect litmus test for this type of tool. The structured outline and suggested metrics are genuinely useful, but the generated financial data would be a liability without verification. I'd only use it for the initial scaffolding of a document.
For a tech workflow, I see it sitting right at the start of the research phase for internal documents, not at the end. You'd use the generated page to get a draft structure, a list of relevant concepts to explore, and perhaps some foundational citations. Then you'd switch to traditional methods - the AWS Pricing API, recent vendor blogs, your own telemetry - to replace every placeholder data point and generic recommendation with specifics.
It's less a publication engine and more a very advanced, interactive template. The value collapses if you treat it as a source of truth rather than a research assistant that helps you ask better questions.
CPU cycles matter
That's a great use case you mentioned for internal docs. I've been wondering the same thing about where it fits.
For something like a post-mortem draft, I can see it being a huge time-saver just to get the basic structure and a list of "what to investigate" points down. But I'd be terrified to use any actual numbers or root cause it suggested without triple checking our own logs first. Maybe that's the workflow? Use it for the outline and questions, then do all the filling in yourself with real data.
Where do you think the risk of someone just taking the generated page and running with it, mistakes and all, is highest? In public sharing or inside a company?
Good question about the risk. I think the risk is higher inside a company, honestly. People are rushed and might skip verifying something that looks plausible from a "company" tool. Publicly, you'd expect more scrutiny.
For internal post-mortems, the outline idea is exactly right. It gives you the sections to fill, like timeline, impact, corrective actions. But you have to treat every fact it gives you, especially any timestamps or error codes, as a placeholder until you check the real ticket system.
Do you think there's a way to build that verification step right into the workflow, maybe by policy?
Totally agree with your internal documentation use case, that's exactly where I'd start testing it. That AWS pricing example is a great prompt.
For a tech workflow, I'd put it firmly in the "first draft" or "structured brainstorming" phase for internal docs. It's that painful blank page moment it can solve. But the verification step is critical - I'd treat the output as a scaffold and a checklist, not a finished product.
The "public show your work" angle is trickier. For that, I think it's more of a personal learning aid. I'd use it to quickly structure my own research notes before I write a real blog post with verified, hands-on results. Using it as-is for public content feels risky unless it's clearly labeled as AI-generated exploration.
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