The head-to-head
SQOR vs. Sigma
Sigma is a spreadsheet on your warehouse bill. SQOR is the answer, warehouse included.
Sigma cannot run without a cloud data warehouse you pay for separately, and its live-query design runs your team's work on that meter. The workbooks are still built by hand. SQOR is one readable price for the answer itself: computed for you, reconciled to your books, with the cause, the forecast, and the move.
The verdict: Sigma is the old category at its most modern: a genuinely slick spreadsheet interface on live warehouse data. Read the architecture plainly: Sigma has no warehouse of its own. It cannot run without a cloud data warehouse you pay for separately, its own documentation says its queries run on your connected platform's compute, and its own users report costs that "can feel unpredictable at times." And while the meter runs, the analysis is still handmade: your analysts build the workbooks, write the formulas, and interpret the results. SQOR.ai is the opposite shape: the warehouse is included in one readable per-seat price, and an engine 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: the spreadsheet idiom is real. For analyst teams that live in spreadsheets on governed, modeled warehouse data, Sigma is the most modern grid in the old category, and its caching tiers genuinely blunt some of the compute cost. The question is whether your next dollar buys a faster grid or the answer itself.
Sigma has none of its own; it runs on the cloud warehouse you pay for separately, metered per query.
The median Sigma contract per Vendr's live transaction data, before the warehouse bill underneath it. Sigma itself publishes no prices.
The share of total Sigma cost that independent analyses attribute to the warehouse compute behind the spreadsheet. Heavy use lands on your bill.
The comparison
Sigma hands your team a faster grid. SQOR hands them the answer.
The real question is who does the work. On Sigma, your analysts build the workbooks, the formulas run on a warehouse you pay for separately, and the interpretation is still a human job. 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 building workbooks and starts commanding the engine.
| SQOR.aiAn autonomous engine | SigmaWorkbooks your analysts build | |
|---|---|---|
| Who does the analysis | The engine itself: every KPI generated, computed, and monitored autonomously | Your analysts: they build the workbooks, write the formulas, and maintain them |
| What gets analyzed | All of it: machine learning runs across every metric, daily | The workbooks your team built, on data someone already modeled into the warehouse |
| What you get back | The number, the why, the forecast, and a scored course of action, in plain English | Workbooks and dashboards; 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 | The formulas your team wrote; no answer-level reconciliation to your books |
| The warehouse bill | Included: Google Cloud and BigQuery are in the price | Required to function and billed separately by your cloud provider; queries run on your compute as your team works |
| Time to live | Two weeks to answers on your own data; the entire day-one ask is read-only access | A warehouse first, modeled data second, then the workbooks get built |
| What is retained | Read-only, and Zero Data Retention standard: no LLM retains your data, up to fully sovereign | Your data stays in your warehouse; Sigma queries it live, per Sigma's policies |
The fully-loaded math
The license was never the price.
Sigma publishes no prices, but the floor is verifiable: Vendr's live transaction data puts the median contract at $65,080 a year, and Sigma cannot run without a cloud data warehouse billed separately by your cloud provider. 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 Sigma stack, fully loaded
25 users, the warehouse it requires, and the team the workbooks assume
| The platform: the median Sigma contract in Vendr's live transaction data, July 2026; Sigma itself publishes no prices REPORTED | $65,080/yr |
| The warehouse Sigma requires: 25 people asking 15 questions a day is 135,000 queries a year; charged at a $0.05-per-query low anchor, which lands inside the 20 to 50 percent share independent analyses attribute to warehouse compute ESTIMATE | $6,750/yr |
| The unpredictability their own users report: compute scales automatically and budgeting gets tricky; that risk has no line item 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 | ≈ $296,830+/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, Sigma 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 Sigma still forces the data team SQOR removes, so its stack runs about $296,830, more than six 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 Sigma rollout assumes the warehouse exists, the data is modeled into it, and the workbooks get built by your analysts. 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 $74,000 before the first trusted answer arrives. The answer that shows up after the decision was worth nothing.
And the people? They stop rebuilding workbooks and start commanding the engine. One person gets the output of a twenty-person data team; nobody spends their week maintaining a grid.
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, Sigma's own documentation, and live transaction data. These are end-user opinions and third-party data, not Gartner research.
costs can feel unpredictable at times
it has a very limited number of functions
refreshing the grid takes forever
Sigma generates and runs optimized SQL queries to retrieve data from your connected data platform.
Vendr's live data puts the median Sigma contract at $65,080 a year. Independent analyses put the warehouse bill at 20 to 50 percent or more on top.
When every click can bill your warehouse, the spreadsheet is never the whole price.
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.
Already running Sigma? Keep it running while you compare; this is not a rip-out. SQOR.ai connects read-only to the same warehouse Sigma sits on, 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, including the warehouse Sigma queries. Read-only by design, no infrastructure, no migration, no data team.
02
Ask
Type your first question in plain English. No workbook to build, no formulas to maintain, no warehouse meter running behind your clicks.
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 Sigma, asked plainly.
Is SQOR.ai a Sigma alternative?
What does Sigma actually cost?
Does SQOR.ai require a cloud data warehouse?
Will SQOR.ai spike my warehouse bill?
Do I need SQL or formulas with SQOR.ai?
Has SQOR.ai been recognized by Gartner?
Stop building the workbook. Start getting the answer.
Bring the workbook your team rebuilds every month. We'll answer the question behind it live, reconciled to your books, with the warehouse included in the price.
Book a demo