Clusy vs Perplexity
Perplexity is the fastest way to find what is already known and see where it came from. Clusy is for the part after that, when the answer does not exist yet and something has to be computed to produce it.
At a glance
| Clusy | Perplexity | |
|---|---|---|
| What it does | Researches the question, then computes a new answer from your data by running code | Retrieves and synthesizes answers that already exist on the web |
| Runs code | Yes, on managed compute | No |
| Works on | Your datasets and warehouses, plus scholarly sources and the web for the research step | The public web, across every subject, with citations |
| Output | A re-runnable notebook with executed cells and artifacts | A cited answer or report in a thread |
| Compute | free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 GPUs (141 GB VRAM) | None you control |
| Reproducible | Yes: re-run the notebook and get the path again | The web moves; the same question can return a different answer |
The core difference
Perplexity is an answer engine. It runs searches, reads what it finds, and returns a synthesis with citations, which makes it excellent for orientation: what is the state of the art, what does this library do, who has published on this. Deep Research extends the same loop across dozens of searches into a structured report.
None of that touches your data. A retrieval system can tell you which method other people used on a similar problem; it cannot tell you how that method performs on your dataset, because nobody has run it. That requires execution.
Clusy carries the search through to that point. Its agent does its own literature and web research, with scholarly sources and citations, but it treats the reading as the first step: it then writes the cells, runs them on a managed sandbox up to H100 and H200 GPUs, reads the real output, and iterates. The result is a notebook, reproducible and specific to your data rather than to the average of the internet. As a general-purpose search tool it is not trying to compete, and for a quick cited answer Perplexity remains the faster route.
Choose Clusy if…
- The answer has to be computed from your data.
- You need a model trained, evaluated, or benchmarked.
- The work must be reproducible and auditable later.
- The output is an artifact, not a summary.
Choose Perplexity if…
- Someone has probably already published the answer.
- You are orienting yourself in an unfamiliar area.
- You want citations you can click, quickly.
- No data of yours is involved.
Frequently asked questions
- Can Clusy search the web like Perplexity?
- It searches the web and scholarly sources, cites what it uses, and can go deep when the task needs it. The difference is intent: our search exists to inform work the agent is about to run, not to answer general questions quickly. For a fast cited answer on any subject, Perplexity is the better tool.
- Can Perplexity analyze my data?
- It can reason about numbers you paste and about published analyses. It does not execute code on your datasets, so it cannot fit a model, run an evaluation, or produce a result you could reproduce.
- Which one do researchers actually need?
- Usually both, in sequence. Perplexity or a deep-research tool to scope what has been done, then a computational notebook to run the experiment that has not been. They answer different halves of the same project.
Sources
Claims about Perplexity come from its own documentation, last checked : Deep Research announcement, How Perplexity works. Quotas, hardware tiers, and pricing move; check the vendor before relying on a number, and tell us if we have something wrong.
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