The head-to-head
SQOR vs. Domo
Credits you can't predict. Renewals you can't see coming. SQOR is one price you can read.
Domo bills in consumption credits its own users call confusing, publishes no prices, and its documented renewal increases run from 150 percent to a reported 1,120 percent. SQOR is one readable price for the answer itself: reconciled to your books, with the cause, the forecast, and the move.
The verdict: Domo is a dashboard estate: a thousand connectors feeding boards your team assembles, maintains, and interprets, billed in a credit currency that its own users call confusing and that famously double-charges data preparation. The pricing page publishes no prices, the median buyer pays a reported $60,500 a year anyway, and documented renewal increases run from 150 percent after seven loyal years to a reported 1,120 percent with two months' notice. SQOR.ai is the opposite shape: one readable per-seat price, warehouse included, and instead of an estate to assemble, an engine that computes every answer from your data, reconciles it to your books, and tells you the cause, the forecast, and the move.
One honest carve-out: Domo's connector breadth is enormous, and if your goal is assembling a governed dashboard estate across hundreds of sources with a team to run it, that breadth is real. If your goal is answers, the estate was never the point.
The renewal increase a verified G2 reviewer reports receiving with two months' notice, for the same users and lower usage. SQOR.ai renewals do not work like that.
What the median Domo buyer actually pays, per Vendr contract data, on a pricing page that publishes no prices. SQOR.ai's price is one number you can read.
Domo's Magic ETL charges credits twice for one transformation: once to bring data in, again to save the result. Preparing your data costs double what you'd expect.
The comparison
Domo connects your data. SQOR answers it.
The real question is who does the work. On Domo, your team assembles the estate: connectors, datasets, cards, and dashboards, and its automated insights can only flag anomalies on metrics your team already defined. 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. Your team stops assembling and starts commanding.
| SQOR.aiAn autonomous engine | DomoA dashboard estate your team assembles | |
|---|---|---|
| Who does the analysis | The engine itself: every KPI generated, computed, and monitored autonomously | Your team assembles connectors, datasets, cards, and dashboards, then keeps them alive |
| What gets analyzed | All of it: machine learning runs across every metric, daily | The metrics your team already defined; its automated insights only flag anomalies on those |
| What you get back | The number, the why, the forecast, and a scored course of action, in plain English | Dashboards and alerts; the interpretation is still your job |
| 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 | Governed cards and datasets; no answer-level reconciliation to your books |
| The warehouse bill | Included: Google Cloud and BigQuery are in the price | Consumption credits with no published prices, data preparation that bills twice, and a documented renewal-repricing pattern |
| Time to live | Two weeks to answers on your own data; the entire day-one ask is read-only access | The estate assembly first: connectors, datasets, cards, dashboards |
| What is retained | Read-only, and Zero Data Retention standard: no LLM retains your data, up to fully sovereign | Stored in Domo's cloud, per Domo's policies |
The fully-loaded math
The license was never the price.
There is no list price to start from, because Domo does not publish one. What exists is contract data and a documented pattern: a median buyer near $60,500 a year, credits that bill data preparation twice, and renewal increases its own reviewers describe in percentages that belong in a casino. Here is a 25-person deployment walked at the MEDIAN, line by line, every input labeled. Attack it, or bring your own contract and we will walk yours.
The Domo stack, fully loaded
25 users at the median contract, and the team the estate needs
| The platform: the median buyer pays about $60,500 a year per Vendr contract data (larger contracts are reported well into six figures; the 84-deal average is a reported $134,000) REPORTED | $60,500/yr |
| The renewal risk their own reviewers document: increases from 150 percent to a reported 1,120 percent at repricing DOCUMENTED | 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 | ≈ $285,500+/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, Domo 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 Domo still forces the data team SQOR removes, so its stack runs about $285,500, more than five 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 Domo rollout runs through the estate assembly: connectors, datasets, cards, and dashboards, before the first board is trusted. 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 $71,000 before the first insight arrives. The answer that shows up after the decision was worth nothing.
And the people? They stop feeding dashboards and start commanding the engine. One person gets the output of a twenty-person data team; nobody spends their week building charts.
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 platforms and pricing analyses. These are end-user opinions, not Gartner research.
We were notified two months before our renewal that next year's price was going to be 1,120% more than our last renewal price.
A customer of seven years reports a 150 percent price increase at renewal.
the licensing for users and storage can be confusing
Magic ETL transformations consume credits twice: once to ingest the data, again to save the transformed output.
Vendr contract data puts the median Domo buyer near $60,500 a year. Domo's pricing page publishes no prices.
A price you cannot read is a price you cannot budget. Their own customers keep learning that at renewal.
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.
Nothing your team built is wasted: SQOR.ai connects read-only to the same sources Domo's connectors reach, 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, the same sources Domo connects to. Read-only by design, no infrastructure, no migration, no data team.
02
Ask
Type your first question in plain English. No connectors to babysit, no cards to build, no credit pool to watch.
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 Domo, asked plainly.
Is SQOR.ai a Domo alternative?
What does Domo actually cost?
Why do Domo credits surprise teams?
Does SQOR.ai bill in credits?
Can SQOR.ai read the sources Domo connects to?
Has SQOR.ai been recognized by Gartner?
Stop assembling. Start asking.
Bring the question your team would normally build a dashboard for. We'll answer it live, reconciled to your books, at a price you can read today.
Book a demo