Qrly is the self-hostable BI + AI platform Looker Studio cannot be — Looker Studio (formerly Google Data Studio) has no AI Ask, no self-hosting, and needs a Google account for everyone who opens a report. Qrly ships natural-language Ask (NL→SQL), AI anomaly detection and BYO LLM (local Ollama / LM Studio or cloud Claude, Gemini, OpenAI, Azure) on your own infra, with embedded analytics via signed-JWT and flat pricing for unlimited users.
Looker Studio is free, genuinely good at what it does, and almost certainly has more reports built in it than every paid BI tool combined. The Qrly pitch is depth, governance and where your data lives — not that Looker Studio is bad. The decision usually comes down to those three axes rather than to who draws the nicer bar chart.
The features most teams actually evaluate when switching from Google Data Studio.
From real migration conversations with data and IT leaders deciding whether Looker Studio can still carry reporting that has outgrown it.
Looker Studio draws charts on top of a connector, and that is the whole architecture. There is nowhere to put a metric definition, nothing to mark as reviewed, and no query anyone else can reuse — so the same calculation gets re-typed as a calculated field in the fourth report, subtly differently, and two dashboards start disagreeing about revenue. The tool is not at fault; it was never designed to be the place a definition lives.
Qrly is built the other way round. A saved question is a reusable object other questions reference as a view, with cycle detection and a bounded nesting depth. Above it sit OLAP models with a measure catalogue, named hierarchies and always-applied model filters. Around it sit a verified-question badge with its own audit log and a lineage graph that shows which dashboards a table actually feeds.
Not at any price, not in any tier. Reports, cached extracts and the credentials that reach your database all sit in Google's estate, and every person who opens a report needs a Google account. For defence contractors, hospitals, municipalities, smaller banks and research institutes, that is not a preference to be negotiated — it is the sentence in the regulator's letter.
Qrly self-hosts on any Linux box or Kubernetes cluster with no user floor and no minimum spend. Air-gapped installs are supported. A 40-person internal team runs the same binary a 4,000-person enterprise runs, on the same licence terms, with the data never leaving the perimeter.
Whatever assistance Looker Studio offers runs in Google's cloud, on Google's terms, with your schema and your prompts crossing the boundary to get there. For teams holding customer PII, health records or regulated financial data, that is not a procurement debate — it closes the door on the feature set entirely, and the answer is to switch it off rather than to configure it.
Qrly points at Ollama or LM Studio on your own hardware, or at any OpenAI-compatible endpoint you control. Natural-language Ask that compiles to SQL, generated schema and column descriptions, result narration, anomaly explanations — all inside your network. The agent layer is off by default, every write is staged as a proposal for approval, and the published MCP surface is read-only.
A Looker Studio report is only as quick as the connector behind it, and you have almost no levers. A report over a large spreadsheet or a third-party partner connector is slow in a way no amount of chart tuning fixes, and the usual advice — extract the data and refresh it on a schedule — trades freshness for speed rather than solving anything.
Qrly gives you the levers. Four opt-in caching tiers, per-question result materialisation on a refresh interval, per-connection query timeouts, row caps, concurrency and queue limits, a per-user daily query budget and a query governor. Large results stream as NDJSON with a cancel that genuinely aborts the request, and every question carries an EXPLAIN plan and a performance analyser that ranks its findings before you ask an LLM anything.
Our own list price in full, and an honest account of how Google prices Looker Studio. 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 Looker Studio estates are rebuilt on Qrly inside a working week.
There is no Looker Studio export a tool can ingest, and nobody sells one. That matters less than it sounds: the report is a view, and the data was always somewhere else. Qrly connects to the same BigQuery, Cloud SQL, Postgres or MySQL the report was already reading, and the report itself is rebuilt in the visual builder.
For warehouse and database reporting — yes, and with a good deal more underneath it. Qrly ships 13 visualisation types, dashboards with cascade filters and live push, scheduled subscriptions and report bursting, OLAP star and snowflake models, and saved questions that other questions reference as views. Where Looker Studio still wins is Google-owned sources: if the report is mostly Analytics, Ads and Search Console, its first-party connectors are hard to argue with, and there is no shame in keeping it for that one job.
That is the honest reason to keep Looker Studio. Google's own connectors to Analytics, Ads, Search Console and Sheets are first-party, free, and maintained by the company that owns the data. Qrly connects to databases and warehouses — 40 connection types, from Postgres and MySQL through to BigQuery, Snowflake and Redshift — and does not ship a Google Analytics connector. Teams that already land marketing data in the warehouse lose nothing in the move; teams that do not should keep Looker Studio pointed at those sources and put everything else in Qrly.
A join, defined once in the query rather than inside a single report. Qrly supports LEFT, INNER, RIGHT and FULL joins against a table or against another saved question compiled as a subquery, with cycle detection and a maximum nesting depth of five. The difference that matters is reuse: a blend belongs to the report that owns it, so the next report redoes the work and eventually disagrees with the first one. A Qrly question is a source other questions can point at.
No — and neither can anything else, because a Looker Studio report has no export format another tool can read. What actually transfers is the data source underneath it. Qrly connects straight to the BigQuery, Cloud SQL, Postgres or MySQL the report was already querying, through one of 40 connection types, and the report is rebuilt in the visual builder. Where a source used a custom query, paste the SQL and Qrly reverse-engineers a flat SELECT into a visual definition, reporting CTEs, UNIONs and subqueries as explicit lossy-feature warnings.
Yes, with a signed JWT and locked parameters, so each viewer sees their own slice and cannot widen it by editing a URL. Looker Studio embeds a report as an iframe governed by that report's sharing settings — public to anyone with the link, or restricted to named Google accounts. That is perfectly good for an internal audience, and awkward the moment you need to show a customer their own data and nobody else's.
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. Looker Studio itself is free, and Looker Studio Pro is a paid per-user add-on that Google prices on its own pricing page — there is no six-figure licence to compare against, and we are not going to pretend there is. The cost that matters is not the licence at all: it is BigQuery, which charges for the bytes scanned every time somebody opens a report. That meter tracks your users’ curiosity. Ours tracks your turnover, once a year.
A full BI platform in one flat-priced licence. Self-hostable. On-prem AI. Made in Belgium.