The head-to-head
SQOR vs. Dataiku
Dataiku is a platform for your data-science team. SQOR is answers for the whole company.
Dataiku equips specialists to build projects, models, and notebooks, at a price its own users call very high and its own channel aims at bigger companies. SQOR is the output without the specialist team: every answer computed from your sources, reconciled to your books, with the cause, the forecast, and the move, for anyone who can type a question.
The verdict: Dataiku is a genuinely strong platform for data-science teams: projects, models, notebooks, governance, the full workshop for specialists. Read who it is priced and shaped for, in its own users' and channel's words: "the pricing for Dataiku is very high, which is its biggest downside"; "it is very expensive"; "the licenses are a bit high for companies that are still hesitating to get started"; and from a reseller, "I primarily recommend it to bigger companies." There is no published price; Vendr's live transaction data puts the median contract at $159,648 a year. And the platform's output still travels through specialists: someone builds the project, someone interprets the model, someone answers the executive's question on a delay. SQOR.ai inverts the shape: the machine learning is operated by the engine itself, every number is computed from source and reconciled to your books, the AI is blocked from inventing figures, and the answer, with its cause, forecast, and a scored course of action, goes directly to whoever asked, at one readable per-seat price with the warehouse included.
One honest carve-out: if your specialists build custom models for products, research, or operations, that is real data science, and Dataiku is a serious platform for that work. This page is about the other 95 percent of the company: the people who need trusted answers, not a modeling workbench.
The median Dataiku contract in Vendr's live transaction data, before the specialist team the platform assumes and the infrastructure underneath it.
Dataiku publishes no prices: a free trial, a free edition, and contact-sales for everything organizations actually run.
Its own channel says it plainly: recommended primarily to bigger companies, with the data teams to match. The answers still route through them.
The comparison
Dataiku equips the specialists. SQOR answers everyone.
The real question is where the answers come from. On Dataiku, the platform is a workshop: your data scientists build the projects and the rest of the company waits at their door. With SQOR.ai, an autonomous engine runs machine learning across ALL of your data, every key performance indicator ("KPI"), every day, defining, computing, and explaining the metrics itself, extracting the causes, the predictions, and a military-grade course-of-action engine's recommendations, with the warehouse included. The workshop becomes an answer desk with no line in front of it.
| SQOR.aiAnswers for everyone | DataikuA platform for specialists | |
|---|---|---|
| Who it serves | The whole company: anyone who can type a question gets a reconciled answer | The data-science team; the platform is shaped for specialists, by design |
| Who does the analysis | The engine itself: every KPI generated, computed, and monitored autonomously | Your data scientists, in projects and models they build on the platform |
| What you get back | The number, the why, the forecast, and a scored course of action, in plain English | Models, notebooks, and dashboards your specialists build and interpret |
| Who vouches for the number | The math: every figure computed from source, reconciled to your books, as-of dated. The AI is blocked from inventing figures | The project's author; no answer-level reconciliation to your books |
| The price | One readable per-seat price, warehouse included | No published price; Vendr's live median is $159,648, and their users call the pricing very high |
| Time to live | Two weeks to answers on your own data; the entire day-one ask is read-only access | Platform standup, then project builds, then the specialists' backlog |
| What is retained | Read-only, and Zero Data Retention standard: no LLM retains your data, up to fully sovereign | Data flows into the projects your team builds, per your deployment |
The fully-loaded math
The license was never the price.
Dataiku publishes no prices, so we walk the verifiable middle: Vendr's live transaction data puts the median contract at $159,648 a year, and the platform is built to be operated by specialists you also pay. Here is a 25-person deployment walked line by line, every input labeled. Attack it, or bring your own quote and we will walk yours.
The Dataiku stack, fully loaded
25 users served the specialist way: a workshop, plus the specialists
| The platform: the median Dataiku contract in Vendr's live transaction data, July 2026 REPORTED | $159,648/yr |
| The infrastructure the projects run on, billed separately per your deployment STRUCTURAL | unpriced |
| The data team SQOR removes entirely: one data engineer ($130,000, low end of 2026 surveys) and one analyst ($95,000) who build and maintain what the tool needs ESTIMATE | $225,000/yr |
| Fully-loaded total | ≈ $384,648+/yr |
SQOR.ai, fully loaded
25 seats, everything included
| 25 seats at $163.20 per seat per month, all-in, at typical usage of 15 questions a day (about 450 a month) | $48,960/yr |
| The warehouse: Google Cloud and BigQuery included | $0 |
| Required specialists: none. The engine computes, explains, forecasts, and recommends | $0 |
| Fully-loaded total | $48,960/yr |
on the recurring cost. And the cost gap is the smaller story, because even fully loaded, Dataiku surfaces a fraction of the signal in your data while SQOR reads all of it. You pay less and see more. A junior data engineer or analyst now commands $96,250 to $138,500 out of a strong program, which lands a first hire near $120,000 fully loaded with benefits and payroll taxes. SQOR runs 25 people, everything included, for $48,960, about $160 each a month: less than half the cost of that single hire. And Dataiku still forces the data team SQOR removes, so its stack runs about $384,648, more than seven times the entire SQOR bill. Your numbers will differ; bring your quote and we will walk yours.
Then there is time.
SQOR.ai is live on your own data in two weeks. A specialist platform stands up, then the projects get built, then the backlog forms. Industry research on enterprise analytics and AI projects puts typical pilot-to-production timelines at a quarter to three quarters.
Delay has a price tag.
Money has a time value, and so do answers. Every month between signing and answering is a month of fully-loaded spend with zero decisions improved: on the stack above, one quarter of building burns roughly $96,000 before the first trusted answer arrives. The answer that shows up after the decision was worth nothing.
And the people? Your specialists stop fielding routine questions and get their real work back. One person gets the output of a twenty-person data team; the workshop stops being a bottleneck.
Bring us any comparable quote and we will beat it.
Applies to a written quote for a comparable offering: autonomous decision intelligence with the data warehouse included and no required specialist staffing. One quote per organization. SQOR.ai determines comparability in good faith and will explain its determination.
Gartner® Hype Cycle 2026, twice
Named in two 2026 Gartner Hype Cycles.
SQOR.ai was named in the Hype Cycle for Data Science and Machine Learning, under the category Gartner calls Vibe Analytics and rates Transformational, its highest benefit rating,1 and again in the Hype Cycle for Analytics and Business Intelligence.2 Vibe Analytics, in one line: the dashboard era ends and asking becomes the interface.
Their users said it first
Don't take our word for it. Take theirs.
Real user reports from public review platforms and live transaction data. These are end-user opinions and third-party data, not Gartner research.
The pricing for Dataiku is very high, which is its biggest downside
It is very expensive
The licenses are a bit high for companies that are still hesitating to get started
I primarily recommend it to bigger companies
Vendr's live data: the median Dataiku contract is $159,648 a year, ranging from about $6,900 to $334,860. Dataiku publishes no prices.
A six-figure median for the workshop, and the company's questions still wait at the specialists' door.
Meet Esa
Every team deserves a JARVIS.
“Tony Stark is a witty guy with a funny mustache, right up until his AI, JARVIS, turns him into Iron Man. That is what we watch happen at the companies where Esa is deployed, and what we intend for every one.”
Esa is the intelligence layer inside SQOR.ai. She answers in plain English, watches every metric nightly, and tells you what moved, what is coming, and what to do about it, giving the team you already have the reach of a twenty-person data function.
Ask. Understand. Act.
Why the math works
Built so the meter reads zero.
The warehouse is included
Google Cloud and BigQuery are part of the subscription. The single largest infrastructure line item in the old category is zero here.
Your Google commit works here
SQOR.ai is sold through Google Cloud Marketplace. If you carry a Google Cloud committed-spend agreement, up to 25% of your commit credits can be applied to SQOR.ai subscriptions.
No required specialists
No dashboards to build, no semantic model to hand-code, no analyst queue. The engine does the analysis; your team does the deciding.
The math is the authority
Machine learning computes every number from your data and reconciles it to your books. The AI explains the number and is blocked from inventing it.
Beside your books, never instead of them
SQOR is operational intelligence that sits beside your audited source of truth. It reads and reconciles; your system of record stays the system of record, and nothing here replaces your audited financials.
Zero Data Retention, standard
SQOR Shield ships with every deployment: no large language model ("LLM") retains your data, and nothing about your business ever trains one. Your prompts, payloads, and answers are processed in the moment and stored nowhere outside of SQOR. Ask any vendor on this page what their LLM keeps.
Google-grade security underneath
SQOR.ai is built entirely on Google Cloud, so your data is protected by the same infrastructure Google publishes its security protocols and certifications for: encryption at rest and in transit by default, and the compliance regime of one of the world's most audited clouds. Google's encryption-at-rest documentation →
Switching
Leaving the old category takes a question, not a quarter.
Running Dataiku today? Keep it; this is not a rip-out, and if your specialists build custom models on it, that work continues untouched. SQOR.ai connects read-only to the same source systems, your data stays exactly where it is, and your metric definitions are captured during the proof of value ("POV") so the answers speak your company's language from day one.
01
Connect
Plug into the stack you already run. Read-only by design, no infrastructure, no migration, no project to stand up first.
02
Ask
Type your first question in plain English. No notebook, no model build, no specialist queue between you and the number.
03
Act
Get the answer, reconciled to your books, with its source and as-of date. Take it to the board. Two weeks, counted from the day we get read-only access, which is the entire day-one ask, with a POV priced to be a no-brainer.
Questions buyers ask
SQOR.ai vs Dataiku, asked plainly.
Is SQOR.ai a Dataiku alternative?
What does Dataiku actually cost?
Do I need data scientists to run SQOR.ai?
Does SQOR.ai replace our data-science team?
Does SQOR.ai require a cloud data warehouse?
Has SQOR.ai been recognized by Gartner?
Give everyone the answers. Give the specialists their time back.
Bring the question that is sitting in your data team's backlog right now. We'll answer it live, reconciled to your books, with no project built first.
Book a demo