BI + AI in one self-hosted platform — natural-language Ask (NL→SQL) and BYO LLM (local Ollama / LM Studio or Anthropic, Gemini, OpenAI, Azure) included by default. Apache Superset has no AI Ask, no native LLM integration and a heavy Python + Celery ops burden — Qrly ships as a single binary with the AI story already built in.
No marketing fluff. Here is where each tool is genuinely stronger.
The features most teams actually evaluate when weighing Qrly against Apache Superset.
Patterns that come up repeatedly in migration conversations with data and platform leaders.
Apache Superset is a Python and Flask application, and the single-container demo is not the shape you run in production. That shape is a web tier, Celery workers, Celery beat for schedules, a message broker, a cache, a separate metadata database, and a headless browser so that alerts and reports can screenshot a dashboard. Every one of those needs monitoring, patching and a place in your upgrade plan, and the Python dependency graph has to resolve again on every one of them.
Qrly ships as a single binary or container. Scheduling, materialisation, streaming and the AI layer live in the same process. One thing to start, one thing to back up, one thing to upgrade — and the difference shows up on the first upgrade weekend rather than in the evaluation.
Superset can embed a dashboard, but it is a build: turn on the embedded feature flag, register the embedding, stand up a service that mints guest tokens with the right row filters, and wire the JavaScript SDK into your own front end. It works, and every team that does it writes and then maintains that token service themselves.
Qrly embeds a dashboard with a signed JWT and locked parameters, so an embedded viewer cannot widen the query beyond what the token allows. Cascade filters, live push over an interval or a Postgres LISTEN/NOTIFY channel, and scheduled subscriptions all work inside the embed. Same install, same licence, same access controls.
Superset is free under Apache 2.0, which is genuinely valuable and we respect the project for it. The cost lands somewhere else. Authentication, security roles, caching, async query execution and most of the interesting behaviour are set in a Python configuration file and behind feature flags, which means a code review and a redeploy for changes that a reviewer will expect to see in an admin screen. Upgrades are yours to plan, and so is every driver you install.
Qrly puts SSO, OIDC, audit logs, query governance and on-prem AI in the base product, configured in the interface rather than in a file on the server. We are equally clear about what is missing: there is no MFA and no row-level security today, and embed locked parameters are the closest equivalent.
Superset gives you two good tools that face away from each other. Explore builds a chart from ad-hoc filters and metrics and will show you the SQL it generated. SQL Lab runs whatever you write, with Jinja templating on top. What it will not do is take the SQL an analyst wrote and load it back into the chart builder, so the moment a question outgrows Explore it leaves the visual world permanently.
Qrly treats that as a round trip. Any visual question compiles to SQL you can read, and a flat SELECT pasted into the editor is reverse-engineered into a visual definition — joins, filters, group by, having, order by with NULL ordering, limit and offset — with CTEs, UNION and subqueries reported as lossy rather than silently dropped. Drill-down keeps working either way: ROWS mode strips the aggregation, FILTER mode narrows to the group you clicked.
Our own list price in full, and an honest account of what Apache Superset actually costs to run, given that nobody sells it. 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 rebuild their core dashboards in a working week, even from a large Apache Superset instance.
Both tools read your database directly, so there is nothing to extract and nothing to load. What transfers is the chart and query definitions, and Superset exports those cleanly as YAML or through its REST API.
{{template_variables}} bound as prepared-statement parameters.For most teams — yes. Qrly covers the core of what teams use Apache Superset for: database connections, SQL exploration, saved charts, dashboards and scheduled delivery. Teams that depend on a database driver Qrly does not ship — Oracle, Teradata, SAP HANA, MongoDB, Cassandra, Dremio and Firebird are the notable gaps — should confirm the match first. What Superset asks you to assemble from Celery workers, a broker, a cache, a metadata database and a headless browser, Qrly ships as a single process, with signed-JWT embedding included.
Nothing has to be extracted from the database, because both tools read it directly. Point Qrly at the same connection and the warehouse never notices the change.
Superset exports its dashboards, charts and datasets as YAML, and the REST API returns the same objects — that export is your inventory. The SQL is then rebuilt in Qrly: paste a saved SQL Lab query into the Monaco editor and Qrly reverse-engineers a flat SELECT into a visual definition, reporting CTEs, UNION and subqueries as lossy. Jinja template parameters become {{template_variables}} bound as prepared-statement parameters.
Apache Superset is an Apache Software Foundation project under the Apache 2.0 licence: a Python and Flask application that in production also wants Celery workers, a message broker, a cache, a metadata database and a headless browser for report screenshots. Qrly is a single binary or container built in Belgium on a flat licence priced by revenue. Both self-host, and both keep your data in your own region.
The practical differences are the operational surface, signed-JWT embedding and a native Alert engine in the base product, and a visual query builder that converts to SQL and back. Superset in return costs nothing to licence, has a much larger chart library, and reaches databases Qrly has no driver for.
Three things, and they are real. It is free under Apache 2.0, so there is no licence conversation at all. Its visualisation library is far larger than Qrly's thirteen chart types plus the dashboard trend card. And because it sits on SQLAlchemy it reaches databases Qrly has no driver for — Oracle, Teradata, SAP HANA, MongoDB, Cassandra, Dremio and Firebird among them. If your data lives in one of those, that decides the question before any of the rest of this page matters.
Superset splits the work in two. SQL Lab runs raw SQL with Jinja templating; Explore builds a chart from ad-hoc filters and metrics and will show you the SQL it generated. The two do not round-trip — hand-written SQL cannot be loaded back into the chart builder — so a question that outgrows Explore leaves the visual world for good.
QQL is one artefact for both jobs: a JSON query document with around 35 filter operators, four join types, calculated fields and question-as-view composition, compiled to SQL for 12 dialects. Paste a flat SELECT in and Qrly converts it back into that visual definition, flagging CTEs, UNION and subqueries as lossy. For time-based analysis it offers PAST_N_DAYS, START_OF_MONTH and AT_TIMEZONE, an OLAP time hierarchy from year down to hour, and prior-period and prior-year comparison columns.
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. Apache Superset has no commercial edition and therefore no list price — it is an Apache Software Foundation project under Apache 2.0, and anyone quoting a per-user figure for it is selling their own hosting rather than Superset. Its cost is operational: a Python application, Celery workers, a broker, a cache and a metadata database to keep running and upgrade, with nobody to escalate to. That figure depends on what your engineers cost, so we will not guess at it.
Self-hostable. Flat pricing. Embedded Analytics, Alert and QQL included. Made in Belgium.