The head-to-head
SQOR vs. Tellius
Tellius answers on the model your team builds. SQOR builds the model itself.
This is the closest comparison in the category, so here is the honest split. Tellius' own docs start the conversation at "choose the relevant Business View," the model your data team built and published. SQOR's engine builds the model itself from your sources, reconciles every figure to your books, and includes the warehouse in one readable price.
The verdict: Tellius is the strongest technical neighbor we compare against, and this page treats it that way. Its automated root-cause analysis genuinely leads the market, its Kaiya assistant reaches into Slack, Teams, dashboards, and browsers, and proactive monitoring is core product, not an add-on. The split is in front of the first question and underneath every answer. In front: Tellius' own documentation starts at "Choose the relevant Business View," and a Business View is something your team, with granted privileges, creates, populates with datasets, and publishes before Kaiya can answer on it. Underneath: the answers are generated on what was modeled, with no answer-level reconciliation to your books. SQOR.ai closes both gaps by construction: the engine builds the model itself from your source systems, machine learning computes every number deterministically, each figure reconciles to your ledger, the AI is blocked from inventing numbers, and every answer carries the cause, the forecast, and a scored course of action. And the price shape differs the same way: Tellius publishes no prices, while its own blog puts the AWS consumption rate at a starting point of $6 an hour. SQOR.ai is one readable per-seat price, warehouse included.
One honest carve-out, wider than usual: if your data team is ready to model and maintain Business Views, and your priority is automated root-cause exploration inside the tools you already chat in, Tellius is a serious, credible choice. If the answers must reconcile to the books, arrive without a modeling team, and land at a price you can read, that is the purchase SQOR.ai was built for.
What both plans on Tellius' pricing page say instead of a price. Premium caps at ten users; Enterprise is a sales conversation.
Tellius' own published starting rate on AWS Marketplace. Always on, the starting rate alone runs $52,560 a year, before the team that models the views.
Their own docs: choose the relevant Business View ("BV") to start. Someone builds and publishes that model before the first answer.
The comparison
Two AI analysts. One split: who builds the model.
If you are evaluating both, you are asking the right question, because these are the two closest answers to it. Here is the split. On Tellius, your data team models and publishes the Business Views and Kaiya answers on them. With SQOR.ai, an autonomous engine runs machine learning across ALL of your data, every key performance indicator ("KPI"), every day, building the model itself, computing and explaining the metrics, extracting the causes, the predictions, and a military-grade course-of-action engine's recommendations, with the warehouse included. One asks your team to build first. The other builds itself and shows its reconciliation.
| SQOR.aiThe model builds itself | TelliusAnswers on the views your team models | |
|---|---|---|
| Who builds the model | The engine: it extracts and defines every KPI from your source systems itself | Your data team: Business Views are created, populated, and published before Kaiya answers |
| What you get back | The number, the why, the forecast, and a scored course of action, in plain English | Automated insights and root-cause analysis, genuinely strong, on the modeled views |
| 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 Business View's author; no answer-level reconciliation to your books |
| How the answer is made | Pre-computed and truth-gated before serving; the same question gets the same audited answer | Generated at ask time on the modeled view |
| Where answers reach you | The SQOR.ai portal today | Kaiya Everywhere: Slack, Teams, dashboards, and browsers. Credit where due |
| The price | One readable per-seat price, warehouse included | No published price: both plans say Customized; their own AWS rate starts at $6 an hour of consumption |
| Time to live | Two weeks to answers on your own data; the entire day-one ask is read-only access | Data connected, prepared, and modeled into Business Views first |
The fully-loaded math
The license was never the price.
Tellius publishes no prices: both plans say Customized. So we walk the one rate Tellius itself has published, the $6-per-hour AWS Marketplace starting rate from its own blog, and label every step. Here is a 25-person deployment walked line by line. Attack it, or bring your own quote and we will walk yours.
The Tellius stack, fully loaded
25 users served on their own published starting rate, plus the team the Business Views assume
| The platform: their own published AWS starting rate of $6 an hour, always on, is 6 x 24 x 365 = $52,560; "starting at" means real consumption scales up from here DERIVED | $52,560/yr |
| The enterprise quote: both published plans say Customized, so the real number arrives by sales call and is not guessed here 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 | ≈ $277,560+/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 builds the model, 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, Tellius 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 Tellius still forces the data team SQOR removes, so its stack runs about $277,560, 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 Tellius rollout connects the data, prepares it, and models the Business Views before Kaiya answers on them. 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 modeling burns roughly $69,000 before the first trusted answer arrives. The answer that shows up after the decision was worth nothing.
And the people? They stop modeling views and start commanding the engine. One person gets the output of a twenty-person data team; nobody publishes a Business View before asking a question.
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 own pages said it first
Don't take our word for it. Take theirs.
This vendor's public review corpus is thin, so this section leans on the most reliable sources there are: Tellius' own pricing page, docs, and blog, fetched this month, plus one user review. These are public statements and end-user opinion, not Gartner research.
Choose the relevant Business View (BV) from the list of all available BVs.
Both published plans say Customized. Premium caps at ten users; Enterprise is Talk To Sales.
starting at just $6/hour. This true consumption based pricing model…
There is a bit of a learning curve in the beginning, especially when setting things up or customizing dashboards
$6 an hour, every hour, is $52,560 a year at their own starting rate, before the data team that models the Business Views.
When both tabs are open, ask each one the same two questions: who built the model, and who vouches for the number?
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 →
The bake-off
Both tabs open? Good. Run us side by side.
This is the one page in our comparison set where we say: evaluate both, live, on your own data. 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. Then ask both platforms the same question and check whose number reconciles to your books.
01
Connect
Plug into the stack you already run. Read-only by design, no infrastructure, no migration, and no Business View to model first.
02
Ask
Type the same question you would ask Kaiya. No view to choose, no model to publish, no data team in front of the answer.
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 Tellius, asked plainly.
Is SQOR.ai a Tellius alternative?
What does Tellius actually cost?
Is Tellius' automated root-cause analysis good?
Does SQOR.ai need a semantic model or Business View built first?
Does SQOR.ai require a cloud data warehouse?
Has SQOR.ai been recognized by Gartner?
Run the bake-off. We want the same question asked twice.
Bring the hardest question you plan to ask in your evaluation. We'll answer it live, reconciled to your books, with no Business View modeled first.
Book a demo