The head-to-head
SQOR vs. Palantir
Palantir deploys smarter people into your building. SQOR makes the people already there smarter.
Palantir's model embeds its engineers in your company to build a bespoke system, at negotiated platform fees its own releases count from $1 million up. SQOR deploys no one. An autonomous engine connects read-only and delivers the answer itself: reconciled to your books, with the cause, the forecast, and the move.
The verdict: Palantir is the old delivery model at its most powerful: brilliant engineers, embedded in your building, constructing a bespoke operational system on their platform. That is not our caricature; it is their own description. Palantir's blog says its forward-deployed engineers "embed directly with our customers," with a focus of "one customer, many capabilities." The price of that model is structural: there is no published price, the fee is a negotiated annual platform engagement, and its own Q1 2026 earnings release counts 206 deals of at least $1 million, including 72 of at least $5 million and 47 of at least $10 million. Our founder's read of the model is blunt, and we will own it as opinion: the platform is the Trojan horse, the services are the business, and the lock-in is the moat. SQOR.ai is the opposite proposition: nobody is deployed into your building. An autonomous engine connects read-only, computes every answer from your data, reconciles it to your books, and makes the team you already have smarter.
One honest carve-out: bespoke, government-grade operational systems with embedded teams are genuinely Palantir's category, and nobody sells that better. If you are buying a custom system and a staff to build it, that is what they sell. If you are buying answers from your own data, that is a different purchase entirely.
Palantir's own Q1 2026 earnings release counts 206 deals of at least $1 million, including 72 of at least $5 million and 47 of at least $10 million.
Enterprise pricing is a negotiated annual platform fee. A 2026 negotiation guide reports comparable deployments varying by two to three times on posture alone.
MIT's GenAI Divide research puts custom enterprise AI at nine months or longer from pilot to scale. Mid-market teams using ready tools moved in 90 days.
The comparison
Palantir builds you a system. SQOR hands you the answer.
The real question is who gets smarter. In the embedded model, capability arrives with their engineers and grows engagement by engagement; it scales with headcount and billable hours. 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. Nobody is deployed into your building, and your team stops waiting on builds and starts commanding the engine.
| SQOR.aiAn autonomous engine | PalantirAn embedded engagement | |
|---|---|---|
| Who gets smarter | The people already in your building: the engine hands them computed, reconciled answers | The building gets their people: embedded engineers arrive with the engagement |
| Who does the analysis | The engine itself: every KPI generated, computed, and monitored autonomously | Custom pipelines and ontologies, built by the embedded engineers with your team |
| What you get back | The number, the why, the forecast, and a scored course of action, in plain English | A bespoke operational system; the answers are whatever the build produces |
| 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 pipelines the engagement built; reconciliation is a build task, not a platform guarantee |
| The price | One readable per-seat price, warehouse included, seats unlimited in usage terms | No published price; negotiated annual platform fees; their own releases count deals from $1 million |
| Time to live | Two weeks to answers on your own data; the entire day-one ask is read-only access | Engagement-scoped builds; MIT puts custom enterprise AI at nine months or longer to scale |
| What is retained | Read-only, and Zero Data Retention standard: no LLM retains your data, up to fully sovereign | Your data is integrated into the platform the engagement builds, per your contract |
The fully-loaded math
The license was never the price.
Palantir publishes no prices. Its own earnings release counts 206 deals from $1 million up, including 72 from $5 million and 47 from $10 million. Here is a 25-person deployment walked at the very floor of their own smallest disclosed deal band, line by line, every input labeled. Attack it, or bring your own quote and we will walk yours.
The Palantir engagement, fully loaded
25 users, charged at the floor of their own smallest disclosed deal band
| The engagement: charged at the very bottom of the smallest deal band Palantir's own Q1 2026 release counts, $1 million REPORTED | $1,000,000/yr |
| The negotiation risk: a 2026 enterprise negotiation guide reports comparable mid-size deployments varying by two to three times on negotiation posture alone 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 | ≈ $1,225,000+/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, Palantir 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 Palantir still forces the data team SQOR removes, so its stack runs about $1,225,000, more than twenty-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. The embedded model builds capability by capability, and MIT's GenAI Divide research puts custom enterprise AI at nine months or longer from pilot to scale, while mid-market teams using ready tools moved in 90 days.
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 engagement above, one quarter of building burns roughly $306,000 before the first trusted answer arrives. The answer that shows up after the decision was worth nothing.
And the people? Nobody smarter needs to be deployed into your building. The team you already have gets the output of a twenty-person data function, and the capability stays when any engagement would have ended.
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, Palantir's own blog, and Palantir's own earnings release. These are end-user opinions and public disclosures, not Gartner research.
Palantir Foundry is an expensive solution.
I know we are paying substantial charges, and if those could be reduced, it would be more accessible.
There is a learning curve to Palantir Foundry, and better documentation would really help
embeds directly with our customers to configure Palantir's existing software platforms to solve their toughest problems
Palantir's own Q1 2026 release counts 206 deals of at least $1 million, including 72 of at least $5 million and 47 of at least $10 million.
When the price starts at seven figures and arrives by negotiation, the platform is not the product. The engagement is.
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.
Evaluating Palantir, or already inside an engagement? This is not a rip-out and it is not a competing build. SQOR.ai connects read-only to the systems you already run, 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. Compare the two models on your own data before you commit another year.
01
Connect
Plug into the stack you already run. Read-only by design, no infrastructure, no migration, and nobody embedded.
02
Ask
Type your first question in plain English. No pipelines to build, no ontology to model, no workstream to commission.
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 Palantir, asked plainly.
Is SQOR.ai a Palantir alternative?
What does Palantir actually cost?
Does SQOR.ai send engineers into my company?
Do I need a data team to run SQOR.ai?
Does SQOR.ai require a cloud data warehouse?
Has SQOR.ai been recognized by Gartner?
Make your own people smarter.
Bring the question you would commission a workstream for. We'll answer it live, reconciled to your books, with nobody deployed into your building.
Book a demo