AI Data Analysis Tools Compared:
7 Platforms Tested on Real Data

How We Tested

We used a single 50,000-row e-commerce dataset containing order IDs, timestamps, product categories, customer segments, revenue, refund flags, and geographic data. The same CSV was uploaded to all seven tools (or connected via database for Hex and Databricks). Every tool received the same five tasks:

• • Data import and profiling — how fast does the tool load the file and describe the schema?
• • Natural language query — “Which product category had the highest refund rate in Q3?”
• • Chart generation — “Create a monthly revenue trend chart broken down by region.”
• • Statistical analysis — correlation between order value and refund probability, with outlier detection.
• • Follow-up reasoning — “Why did revenue drop 23% in August? What factors correlate?”

We scored each tool on query accuracy (did it return the correct answer?), chart quality (readable, labeled, exportable?), speed, and how often we had to rephrase a question to get a usable result. We also cross-referenced user reviews from G2 (100+ reviews per tool where available) and Capterra to validate whether our experience matched broader sentiment.

Note: Prices listed are as of early 2026 and may change. Some tools offer annual discounts. We have no affiliate relationships with any of the tools reviewed in this article.

Side-by-Side Comparison

1. Julius AI — The Easiest On-Ramp for Non-Technical Users

Julius AI is what happens when you build a data analysis tool for people who have never opened a Jupyter notebook and never intend to. You drag a CSV into the chat, ask a question in plain English, and get a chart back. For roughly 70% of common business questions — revenue by category, month-over-month growth, which segment is underperforming — it just works.

Where it stumbled in our testing was on multi-step analysis. Ask “what is the refund rate by category?” and you get a clean bar chart in about eight seconds. Ask “which category has the highest refund rate, and does that correlate with order size or customer segment?” and you start getting partial answers or hallucinated correlations. The free tier gives you around 15 messages per month, which is barely enough to explore a single dataset. The $20/month Pro plan is where it becomes genuinely useful.

The chart output is surprisingly polished — better than what ChatGPT produces natively with matplotlib. Julius generates interactive Plotly-style charts that you can export as PNGs or embed. For a marketing manager who needs a quick chart for a slide deck, this is the fastest path from CSV to presentable visual we tested.

2. ChatGPT Advanced Data Analysis — The Swiss Army Knife

ChatGPT's Code Interpreter (now called Advanced Data Analysis) remains the most versatile option here, largely because it is not just a data tool — it is a general-purpose AI that happens to run Python. Upload a CSV, and it writes pandas code, runs it in a sandboxed environment, and returns results. The statistical reasoning is the strongest we tested: it correctly identified outliers using IQR, ran proper correlation analysis with p-values, and even suggested appropriate statistical tests without being prompted.

The tradeoff is in the user experience. Charts come out as basic matplotlib plots — functional but rarely presentation-ready without manual tweaking. The session state can be fragile: upload a large file, run fifteen queries, and occasionally the kernel resets and you lose context. There is also the file size wall — anything above roughly 100MB gets dicey, and it cannot connect to live databases at all.

For a solo analyst or developer who already pays $20/month for ChatGPT Plus, the data analysis capability is effectively a free bonus. That makes it hard to beat on value. But if you need consistent chart styling, collaboration features, or database connectivity, you will outgrow it quickly.

3. Hex — Where SQL Meets AI and It Actually Works

Hex is the tool on this list that data teams will actually adopt long-term, because it solves a real workflow problem rather than just adding AI to a demo. You connect your database (Snowflake, BigQuery, Postgres, Redshift — the usual suspects), and the AI assistant helps you write SQL queries, generate Python analysis, and build shareable dashboards. It is a notebook environment first, with AI layered on top.

The AI assistant understood our schema surprisingly well after we described the table structure. It generated correct JOIN queries, handled date filtering without us specifying the column format, and produced charts that matched our manual SQL output exactly. Where it really shines is the iterative workflow: write a SQL cell, ask the AI to “add a moving average to this,” and it modifies the query in-place. This is meaningfully different from starting over each time, which is what happens in ChatGPT.

The downside is the learning curve. If you do not write SQL or Python, Hex is not for you. The AI can generate code from natural language, but you need enough technical context to verify the output and debug when it goes wrong — and it goes wrong about 20–25% of the time on complex queries. At $28/user/month for teams, it is also the priciest option after Databricks.

4. Deepnote AI — Jupyter, But Collaborative and Less Painful

If your team currently shares Jupyter notebooks via email or Git and everyone hates it, Deepnote is the answer to a prayer you did not realize you were making. It is a cloud-based notebook environment with real-time collaboration (think Google Docs for data science) and an AI copilot that autocompletes code, suggests visualizations, and explains error messages.

The AI copilot is more assistant than analyst. It will not independently run a full analysis from a natural language prompt the way Julius or ChatGPT will. Instead, it helps you write code faster, catches errors before you run cells, and suggests chart types based on your data shape. For data scientists, this feels natural. For business users, it feels like you still need to code.

We appreciated the built-in integrations — connect to BigQuery, Snowflake, or S3 without leaving the notebook. The commenting and sharing features are miles ahead of Jupyter. At $22/user/month for teams, it is priced competitively with Hex, though Hex has the edge on dashboard publishing and Deepnote has the edge on notebook collaboration.

5. Databricks Assistant — A Cannon for a Problem That Might Need a Slingshot

Including Databricks Assistant in a comparison with $20/month tools feels a bit unfair, like entering a Formula 1 car in a go-kart race. But it keeps showing up in “AI data analysis tools” searches, so it deserves an honest mention. If you already run Databricks for your data lakehouse, the built-in AI assistant is a natural extension. If you do not, there is no reason to start.

The assistant generates Spark SQL and PySpark code, explains existing queries, and helps debug notebook errors. It understands your Unity Catalog schema, which means it can reference table and column names without you spelling them out. For enterprise data teams querying terabyte-scale datasets, this is valuable. For a marketing analyst with a 50K-row CSV, it is like renting a warehouse to store a suitcase.

6. Rows AI — The Spreadsheet That Tries to Think

Rows takes a different approach: instead of building a new interface, it adds AI directly into a spreadsheet. If you have ever wished you could type “summarize the trends in column B” into a cell and get an answer, Rows does exactly that. The AI Analyst feature lets you ask natural language questions about your sheet data, and it generates charts and summaries inside the spreadsheet itself.

At $9/month for the Pro plan, it is the cheapest paid option here. But the row limit is the dealbreaker: the free plan caps at 100 rows, and even Pro maxes out at 10,000 rows. Our 50K-row test dataset simply could not be loaded in full. We tested with a 10K-row sample, and the AI handled basic aggregation and chart generation capably, though it struggled with anything beyond straightforward “sum by category” queries.

Where Rows genuinely shines is integrations. It can pull live data from APIs, enrich spreadsheets with OpenAI calls per row, and connect to external data sources without leaving the sheet. For a founder who lives in spreadsheets and needs quick AI-powered lookups rather than deep statistical analysis, it fills a real niche.

7. Akkio — The Prediction Machine

Akkio is the odd one out in this lineup because it is not really a general data analysis tool — it is a no-code machine learning platform that happens to include data exploration features. Its core value proposition is taking a dataset and building predictive models (churn prediction, lead scoring, revenue forecasting) without writing a single line of code. It does this well enough that it has the highest G2 score of any tool here at 4.8/5 across about 130 reviews.

We fed it our e-commerce dataset and asked it to predict which orders would result in refunds. In roughly three minutes, it trained a gradient boosted model, showed feature importance, and gave us a confusion matrix with roughly 78% accuracy. That is legitimately impressive for zero code and three minutes of work. A data scientist could likely push accuracy to 85%+ with proper feature engineering, but for a first pass, Akkio is remarkably fast.

The downside is the price and scope. At $49/month for the Starter plan, it is the most expensive option on this list. And if you do not need predictive modeling, most of its value proposition evaporates. The data exploration and charting features are decent but not as refined as Julius or Hex. You are paying for the ML capability, and if that is not what you need, you are overpaying.

Genuine Downsides Across the Board

After three weeks of testing, a few uncomfortable truths emerged that apply to the entire category, not just individual tools:

• • Confident incorrectness is the biggest risk. Every tool occasionally produced answers that looked authoritative but were subtly wrong — a misidentified column, a filter that silently excluded null values, a correlation presented as causation. If you do not understand the data well enough to spot these errors, AI analysis can be more dangerous than no analysis.
• • Data privacy is poorly addressed. Julius, ChatGPT, and Rows all process your data on external servers. For companies with sensitive customer or financial data, this is a non-starter. Only Hex and Databricks offer self-hosted or VPC-deployed options. Deepnote sits somewhere in between with SOC 2 compliance but cloud-only hosting.
• • Reproducibility is fragile. Ask the same question twice in ChatGPT or Julius and you might get slightly different answers, different chart styles, or different statistical approaches. This is fine for exploration but problematic for reporting. Hex and Deepnote mitigate this by saving the generated code, which can be re-run deterministically.
• • None of these tools understand your business. They can crunch numbers, but they cannot tell you that the August revenue drop was because your biggest client renegotiated their contract. Domain context still requires a human analyst who knows the business.

Which Tool Fits Your Workflow

Frequently Asked Questions

Bottom Line

The gap between these tools is less about raw AI capability and more about who they are built for. Julius AI and Rows target people who have never written a line of code. ChatGPT serves the curious generalist. Hex and Deepnote are for data teams that already write SQL and Python. Databricks is for enterprises. Akkio is for prediction-specific use cases.

If we had to pick one for general-purpose data analysis, ChatGPT Advanced Data Analysis wins on accuracy and value — especially if you already subscribe to Plus. For teams, Hex is the most complete package despite the price. And if you just need a quick chart from a spreadsheet without learning anything new, Julius AI is the least painful way to get there.

The honest truth is that none of these tools are ready to replace a competent analyst. They are ready to make a competent analyst about 40% faster, and to let non-analysts answer questions they would have previously needed to email the data team about. That is a meaningful improvement, even if it is less revolutionary than the marketing suggests.