ThoughtSpot is a search-driven BI suite: somebody models a worksheet layer first, then business users ask questions of it — and the AI runs in ThoughtSpot's cloud, so the prompts leave your perimeter. Qrly is BI + AI in one focused self-hostable platform: natural-language Ask (NL→SQL) on your choice of local LLM (Ollama, LM Studio) or cloud (Claude, Gemini, OpenAI, Azure), with flat pricing for unlimited users.
No marketing fluff. ThoughtSpot has legitimate fans — here is where each tool is genuinely stronger.
The features most teams actually evaluate when switching from ThoughtSpot.
From real migration conversations with data and analytics leaders.
ThoughtSpot's search bar is the demo, and it is a good one. What the demo does not show is the work in front of it: worksheets built and joined, columns renamed the way business users actually speak, synonyms curated, and all of it maintained as the warehouse moves underneath.
Organisations with a data team to own that curation get real value from it. Everyone else discovers that the search bar answers confidently and wrongly the moment the model behind it has drifted, and that nobody is quite sure who owns the drift.
Qrly picks one model — connections, questions, dashboards — and lets you start on the first afternoon. A star or snowflake OLAP model is there when you want governed dimensions and measures, with model filters always applied as a security boundary. It is not a prerequisite for your first answer.
ThoughtSpot's AI — Sage, the SpotIQ narratives, the answers written back in plain English — runs in ThoughtSpot's cloud against a model ThoughtSpot chose. For a bank, a hospital group or a public body the question is not whether the feature is good; it is whether schema names, column values and analyst prompts may leave the building at all.
The usual answer is to switch the AI off, which removes the reason the platform was bought, or to open a procurement exercise that outlasts the evaluation that started it.
Qrly's four AI personas point at whichever model you configure — a local Ollama or LM Studio instance on your own GPU, or Claude, Gemini, OpenAI or Azure. Schema-only by default; letting a model see actual rows is an explicit per-connection opt-in. Ask writes SQL you can read before you run it.
A search-driven answer is generated for you, which is excellent right up to the moment somebody asks how the number was produced. The phrase you typed is not the query that ran, the query that ran is not something you edit, and an engineer who wants the same logic in a pipeline has to rebuild it from the description.
That matters most where it matters most: the board figure, the regulatory return, the invoice reconciliation. Anything that has to be defended needs a definition somebody can read, review and version — not a phrase that happened to work last Tuesday.
Qrly's questions round-trip. A visual definition compiles to SQL you can read; a flat SELECT reverse-engineers into a visual definition, with CTEs, UNION and subqueries reported as explicit lossy-feature warnings rather than quietly dropped. The analyst edits in the builder, the engineer edits the SQL, and it is the same question either way.
ThoughtSpot is cloud-first. A self-managed deployment exists, but it is a heavyweight installation with its own hardware footprint and its own operational burden, and it is not where the product's development energy goes. For teams with strict residency obligations under GDPR or sector law the practical choice is a US-operated cloud service or an on-premise estate somebody has to run properly.
Qrly self-hosts as a single deployment on any Linux box or Kubernetes cluster, with no user minimum and no per-seat floor. Your data stays where you put it, and EU residency is the default because the product was built in the EU.
Our own list price in full, and an honest account of how ThoughtSpot charges. The Qrly column is the published schedule evaluated at €15M of revenue — the number of people using it does not move it.
Methodology, 28 August 2026. Qrly publishes a complete rate card, so the figures in our column are quoted exactly. The other column describes a pricing model rather than quoting a number: vendor list prices change without notice and several of these vendors do not publish one at all. For current figures, go to the vendor’s own pricing page.
Most teams are up and running on Qrly within a working week.
There is no record-level migration between BI tools, and any vendor promising one is selling you a CSV round-trip. What actually moves is the layer underneath: the warehouse connection, the SQL, and the definitions you rebuild on top of them.
For teams who adopted ThoughtSpot to give business users self-service access to warehouse data — yes. Qrly covers connections, questions built visually or in SQL, dashboards, reports, alerts and embedding, under one mental model with nothing to switch on first. Teams who bought ThoughtSpot specifically for natural-language search over a curated worksheet layer, or for SpotIQ automated insights, should weigh that honestly — Qrly reaches the same answers through a visual query builder, raw SQL and an AI Ask that writes the SQL for you, which is a different route to the same place.
Not as records — no BI vendor moves a competitor's content honestly, whatever the migration page says. What moves is the layer underneath. Point Qrly at the same Snowflake, BigQuery, Redshift or Postgres connection and the tables are already there. Saved SQL comes across by pasting it into a native question, and Qrly's SQL-to-visual conversion turns a flat SELECT into a visual definition, warning you about CTEs, UNION and subqueries it cannot represent. Worksheets and Liveboards are rebuilt rather than imported — usually as an OLAP model plus a dashboard, and usually faster than teams expect.
SpotIQ's automated insight runs and the ThoughtSpot search bar are genuinely good, and Qrly does not clone them. What Qrly ships instead is an AI Ask that turns a plain-English question into SQL you can read and edit before running it, a change-detection badge that flags a KPI when it moves more than 5 per cent, flips empty or changes its result hash, and a per-question performance analyser that only spends AI tokens when you ask it to. Different mechanism, overlapping job — and all of it runs against whichever model you configure, including a local Ollama or LM Studio one.
Search-first BI has to be fed before it works: worksheets modelled, join paths agreed, columns renamed the way business users speak, synonyms curated, and the whole thing maintained as the warehouse moves underneath. Organisations with a data team to own that get real value from it; everyone else finds the search bar is only ever as good as the model behind it. Qrly has one mental model — connections and questions — that an analyst learns in an afternoon, an OLAP model layer for when you want governed dimensions and measures, and no obligation to build either before your first answer.
Yes, both, and there is no tier above this one. Alerts run on scheduled question runs, dashboards embed into your own application with a signed JWT and locked parameters so each viewer sees only their own slice, and scheduled subscriptions go out over four mail providers (IMAP, Microsoft 365, Google and generic SMTP). ThoughtSpot embeds well too, and it has monitors and KPI alerts of its own — the difference here is commercial rather than technical. Embedding in Qrly is not a separate product line to license, and the deployment doing the embedding can sit inside your own perimeter.
Qrly is priced on revenue, not seats: a €15M-revenue company pays €6,300 a year — €18,900 over three years — for unlimited everything, one organisation, one installation. ThoughtSpot publishes an entry plan and quotes everything above it, so take the current figure from their own pricing page rather than from us. The meter is people either way, which is the awkward part of a search-first product: the more of the company you let search the warehouse, the more it costs. Ours does not move when the team grows.
Self-hostable. Flat pricing. One tool that does business intelligence and support — well. Made in Belgium.