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Sep 29, 2026 12:39:39 PM
Sep 29, 2026 12:39:39 PM
By Igor Alcantara, Director of Data Science & Education at IPC Global, Host of Data Voyagers, Qlik MVP and Partner Ambassador
Power BI is free to start. That part is true, and it is a real advantage. Anyone with a Microsoft 365 account can open Power BI Desktop this morning and have something useful on screen before lunch. Nobody has to approve anything.
It reminds me of the kid who comes home and announces that someone down the street is giving away puppies. And they are. The puppy costs nothing. The food, the vet, the fence, the crate, the time, and everything else are separate conversations, and those conversations start on day two.
Software works the same way. The license is the puppy. The question worth asking before you sign anything is what the next two years look like once the thing is living in your house.
Two pieces of research got me thinking about this. The first is mine. In early 2025, I created a fictional company and wrote a series of realistic business requirements for a project, based on very similar use cases from prospects over the previous 18 months. I had fifteen consulting companies, a of mix of sizes, quote the Power BI build. For the Qlik side, I used delivery data from our own real projects.
The second piece of research is the first BARC Benchmark (September 2025), which ran both platforms through identical automated workloads. I had nothing to do with that one. I should also say that I work for a Qlik partner, which is why I got quotes from independent companies, so the numbers would be real and not biased. BARC's half needs no such caveat.
The cost of the puppy
The fictional project was deliberately ordinary. Twenty source tables on-premise, five dashboard applications, one machine learning model, twenty-five full users and a hundred consumers. Nothing exotic. Possibly close to something your team ran last year.
On the Microsoft side, meeting those requirements meant Microsoft Fabric F64, Power BI Pro licenses, the Enterprise Gateway and Azure Machine Learning. Annual software cost came to $64,236, and almost all of that is the Fabric capacity at just over $5,000 a month. The same requirements on Qlik Cloud came to $34,200 a year, with analytics, data preparation and Qlik Predict (called Qlik AutoML when the study was written) included rather than priced separately. I guess the puppy is not free after all.
Then there is the part that never appears on a pricing page or on a sales call: the hours. For the Power BI side I had fifteen consulting companies quote the implementation, dropped the outliers, and averaged the middle ten. The Qlik estimate came from our own project history, since that is what we do all day and we have it all logged. Qlik landed at 164 hours. Microsoft landed at 274. The gap is not spread evenly across the project, which is the most interesting part. Setup and provisioning was 4 hours against 20. Deploying a single machine learning model was 8 hours against 46, because Azure ML is a capable platform that also happens to be a separate platform you have to stand up, connect and then keep alive.
I used blended rate of $195 an hour for both. I don't have a confident current number for what a Power BI-specialist rate should be, so rather than guess at a discount, I kept it level and let the hours do the talking. Even a noticeably cheaper rate on the Microsoft side wouldn't close a 110-hour gap, At $195 an hour, Year 1 lands at $66,180 for Qlik and $117,666 for Microsoft. Year 2, calculated as the subscription plus 20%of the implementation cost (which is an industry standard) for ongoing work, comes to $40,459 against $74,922. Year 2 is the more useful number, since it shows what the thing costs to live with once the one-time setup is out of the way.
Neither figure includes the Azure networking billing Microsoft has announced, or the extra Fabric consumption that machine learning work generates, which is billed by execution, RAM and CPU. The puppy is still free. Everything downstream of it is not.
How it runs once you own it
Cost is only half of this. A platform that is cheaper and slower is not really cheaper. You pay the difference in people's time instead of in invoices.
BARC ran both platforms through automated user sessions using the same data, the same filter logic and the same call sequences, scaling up to fifty concurrent users against a ten million record model. Neither vendor knew the test was happening, and neither vendor configured its own setup. Certified partners did that, under confidentiality agreements.
The headline scores were 100 for Qlik and 40 for Power BI, with productivity at 100 against 31 and scalability at 100 against 48. The spread of response times tells you more than the scores do. Qlik ranged from 0.45 to 4.09 seconds, averaging 1.37. Power BI ranged from 0.49 to 302.33 seconds, averaging 3.84, with one session that never came back at all. The fast half of Power BI is perfectly respectable. The slow half is what costs you, and there is something odd about it: a cluster of requests landing at almost exactly 60, 120 and 300 seconds, lined up suspiciously well with minute boundaries. BARC's reading is shared infrastructure or throttling. Microsoft has not said which one it was.
BARC also counted clicks. Power BI needed about 30 percent more user interactions to finish the same task, mostly when working through hierarchical filters. And it measured how much work one user actually gets through. Over roughly 107 minutes of identical activity, the same automated user finished 18 sessions in Power BI and 28 in Qlik. Same work, 64 percent of the time.
BARC tested the standard entry level cloud tiers of both products, so no Power BI Premium and no Qlik Premium, and BARC expects both would score higher. This was also the first round, capped at 50 users and 10 million records by license availability. It is a well-designed experiment and you can read the paper and reproduce it in your own environment. Most importantly, read the methodology, not just the scoreboard.
One result runs the other way. Power BI opened reports faster, with a median of 6.2 seconds against Qlik's 8.7. The explanation is simple: Qlik opens all the data in memory first, while Power BI defaults to Direct Query most of the time. Qlik was pulling roughly two and a half times more data on open, which BARC read as prefetching and caching that pays off during navigation afterward. Two different bets about where to spend the user's patience. Two extra seconds upfront saves several more later in the session.
What Power BI gets right
Power BI wins a lot of deals, and the reasons are not mysterious.
If your organization already lives in Microsoft 365, the integration is real. Reports show up in Teams, identity and governance fit are familiar to IT, hiring is easy, and typical departmental use performs fine.
That is a big part of how Power BI spreads. It comes in through a door that is already open. Somebody in finance builds a report, other people ask for one, and months later there are 40 of them.
The problem shows up later, when a departmental tool has quietly become an enterprise platform and nobody has gone back to recalculate what that promotion costs.
The household usually has both
The either/or framing is the wrong place to start.
Plenty of organizations run Qlik for the complex, governance-heavy, high-concurrency work and Power BI for lighter departmental reporting. Knowing what you are feeding, and what feeding it will cost you in year two, matters more than picking a favorite animal.
If your Power BI footprint grew out of a free tier and nobody has run the numbers since, run them. Use your own data volumes, your own user counts and your own consulting rates. The answer you get will be yours, and it may not look like mine. What I can tell you is that "it was free when we started" is not a budget.
What has been your experience? Add your feedback in the comments, I'd love to compare notes.
The full IPC Global use case study, including the complete cost breakdown and the phase by phase development estimates, is available here.
