Julius vs Hex — does the analysis have to be reproducible?

The short version

• One-off look at a CSV on your machine → Julius (conversation is enough)
• The same aggregation every month, touched by other people → Hex (the code survives)
• It needs to connect to the warehouse → Hex

1. Pricing

2. They are used in fundamentally different ways

With Julius you upload a file and write something like 'show revenue by month' — the aggregation and the chart come back, and the intermediate steps are the model's business. It can also assemble the result into a report.

Hex is a notebook of SQL and Python cells with an agent on top. You can have the agent write cells, but the artefact is code, and the result can be published internally as a dashboard or a data app.

3. Reproducibility is the fork in the road

For a one-off analysis, losing the steps costs nothing. The problem arrives with 'produce the same number next month'. A conversational analysis does not necessarily follow the same path when asked the same question, so when the figure differs from last month you cannot separate a change in the data from a change in the method.

When the code survives, you rerun it, and any difference is attributable to the data. Numbers that go to an executive meeting or outside the company need that property.

During exploration the opposite is true: conversation is faster. Testing ten hypotheses to see which hold up is not work you want to write code for. Finding the signal in Julius and rebuilding the analysis that matters in Hex is a sensible division.

4. How much data you hand over

Both are cloud services, so the data under analysis leaves your systems. Uploading a table containing customer personal data may breach internal policy.

• Drop identifying columns (name, email, phone, address) before uploading
• Pseudonymise IDs where they are still needed — real IDs are rarely required for analysis
• Check whether inputs can be excluded from model training
• Hex holds SOC 2 and has enterprise contracting, which helps in procurement

5. A limitation both share

The AI computes within the data it is given, but it does not know what the data means. It cannot tell whether a zero is a measured zero or a missing record, or whether duplicate rows are real duplicates or an artefact of how the extract was built. Trusting a number produced without checking that is how a plausible, wrong conclusion gets made.

Start by asking for row counts, nulls per column and duplicate rows. One question establishes what you are actually holding. Skipping it makes everything downstream worthless.

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