Teams can spend months choosing a BI tool and then use it badly for years. The choice matters less than people think, and the things that decide it are usually not the features on the comparison charts. I've worked with Power BI, Tableau, Looker and AWS QuickSight, and here is how I'd narrow it down.
Start with what you already have
If your company lives in Microsoft 365, Power BI is already half-installed. People know Excel, licences may already be paid for, and sign-in and sharing work the way your staff expect. That convenience is worth a great deal. A tool that people can open without a training course gets used.
If your data is in Google BigQuery and your team is comfortable with SQL, Looker is a natural fit. If you have a mixed set of sources and analysts who love visual exploration, Tableau tends to be the one they ask for.
None of that is a rule. It's a starting point, and the starting point is your existing stack and your existing skills, not a feature table.
What each one is good at
Power BI is strong on price and on how well it works with Excel and the wider Microsoft world. It handles a lot of ordinary business reporting well. Its modelling language, DAX, takes time to learn properly, and people who skip that step produce reports that are slow or subtly wrong.
Tableau is excellent for exploring data visually and for polished, flexible charts. Analysts tend to enjoy working in it. It generally costs more, and without discipline it's easy to end up with a pile of individual workbooks that each calculate things slightly differently.
Looker works differently. You define metrics once in a modelling layer, and every chart pulls from that. That's a real advantage if your biggest pain is inconsistent numbers. The price is that someone has to build and look after the model, and that needs a person who is comfortable with code.
QuickSight is worth a look if you're heavily on AWS and want something simple and cheap to embed. It is more limited than the others for complicated reporting.
Questions that decide it
Who will maintain it? The best tool is the one your team can look after. A powerful system that needs a specialist you don't have will rot.
Where does the data live? Connecting to your warehouse well matters more than any feature. Test your actual data, not the demo.
Who are the viewers? Forty executives who want a single page on their phone is a different problem from five analysts digging into raw rows.
How strict do your definitions need to be? If numbers must match across every report, a tool with a central model has an edge. If people just need quick exploration, it matters less.
What's the total cost? Count licences for viewers and creators, the build time, and the time spent on upkeep. The sticker price is only part of it.
Questions that don't
Whether the charts look nicer in one tool. They look good enough in all of them, and a clear design in any tool beats a flashy one in another.
Whether one of them has some particular feature. Check that it has the five things you'll actually use. Ignore the other two hundred.
A quick way to decide
Pick the two most likely options. Take one real report your team already uses, with real data. Rebuild it in both. Give yourself a week. Have the people who will use it look at the result and the people who will maintain it try to change something. You'll learn more from that than from any comparison article, including this one.
And whatever you choose, fix the data model underneath first. I've seen the same disorderly tables produce disorderly dashboards in every tool on this list. The tool is rarely the problem.