Sigma is spreadsheet-shaped cloud BI: SaaS only, with a cloud data warehouse required underneath it and no on-prem option for the platform or its AI. Qrly is BI + AI in one self-hostable platform — natural-language Ask (NL→SQL), anomaly detection and schema descriptions all running on your choice of local LLM (Ollama, LM Studio) or cloud (Anthropic Claude, Google Gemini, OpenAI, Azure OpenAI), across 40 connection types, with flat pricing for unlimited users.
No marketing fluff. Sigma is a well-regarded product with a long track record — here is where each tool is genuinely stronger, and where teams consistently land during evaluation calls.
The features most teams actually evaluate when comparing Qrly and Sigma.
From real migration conversations with data teams and the people who own the platform bill.
Sigma embeds well, and embedding is a separate commercial motion with its own terms sitting alongside the internal licence rather than inside it. What starts as a request from your product team turns into a procurement exercise, and the shape of that contract quietly starts deciding how many customers you are willing to put a dashboard in front of.
Qrly's embedding is the same surface as everything else. Sign a JWT, drop in an iframe, pass locked parameters the viewer cannot override, and serve as many customer tenants as you like — no per-viewer seat and no second contract. The dashboard your customer sees is the one your analyst built, governed by the same permissions and the same always-applied model filters.
Sigma is quoted per user by account type, which on its own is predictable enough. The part that catches finance out sits underneath it: every workbook runs live against Snowflake, BigQuery or Redshift, so each refresh is warehouse compute billed by your cloud provider. Give a dashboard to two hundred people who reload it each morning and you have created a recurring second bill nobody signed off.
Qrly's flat licence never scales with headcount — the 51st user costs nothing, and neither does the 501st — and four opt-in caching tiers, result materialisation on a refresh interval, a per-user daily query budget and a query governor exist precisely so a popular dashboard does not turn into a variable cost. Budget conversations become a one-line item.
Sigma's AI runs where Sigma runs: in the vendor's cloud, against models the vendor chose. The quality is good, but for regulated teams, legal, finance or public-sector operators the data-flow shape is often the whole decision, because the prompts and the schema they carry still leave the perimeter — and on a SaaS-only platform there is no configuration that changes it.
Qrly ships AI with a local option: point it at an Ollama or LM Studio endpoint on your own hardware and every prompt, every schema description and every generated query stays inside your network. Row access is off by default — the model sees your schema, not your rows, until you explicitly enable it — and there is no separate AI subscription to renegotiate when a sensitive project starts.
Sigma is a good product with one structural constraint: it is a cloud service over a cloud warehouse, and both halves are somebody else's infrastructure. If the data cannot leave the building, if procurement needs the platform inside your own network, or if your source of truth is a Postgres or SQL Server instance rather than a warehouse, the evaluation ends there no matter how good the interface is.
Qrly self-hosts as a single deployment on any Linux box or Kubernetes cluster, speaks 12 SQL dialects across 40 connection types, and holds nothing back for a cloud tier. Your data stays where it already is, your LLM stays where you put it, and there is no phone-home in the middle.
Our own list price in full, and an honest account of how Sigma 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 export to negotiate. Qrly connects to the same warehouse Sigma queries — Snowflake, BigQuery and Redshift are all supported connection types — so the data never moves and the work is a rebuild of definitions, not a transfer of rows. Mid-size teams typically finish the cutover in under a week; larger organisations run a parallel window of thirty days before dropping the old contract.
For most teams — yes. Both let people who do not write SQL build their own questions, and both run live against the database rather than a stale extract. The differences are structural: Sigma is SaaS-only and needs a cloud data warehouse underneath it, while Qrly self-hosts on your own Linux box across 40 connection types and 12 SQL dialects, with a signed-JWT embed portal as the same surface your analysts use, on-prem AI via Ollama or LM Studio, multi-tenancy in the base product, and OIDC as a standard feature rather than an enterprise upsell. If your data already lives in Snowflake and your team thinks in spreadsheets, Sigma's grid is hard to beat — for everyone else the fit is close, and the contract shape is simpler.
Nothing has to be extracted. Qrly connects to the same warehouse Sigma queries — Snowflake, BigQuery and Redshift are all supported connection types — so the data never moves and the migration is a rebuild of definitions rather than a transfer of rows. Workbook logic becomes saved questions in the visual builder, spreadsheet formulas become server-validated calculated fields, and anything already written as SQL can be pasted in, where a flat SELECT is reverse-engineered into a visual definition with CTEs, UNION and subqueries reported as lossy.
Sigma's grid is the easiest surface in BI for someone who thinks in formulas, and Qrly does not imitate it. Qrly gives them a visual query builder plus a result table that behaves like one: click-to-sort per column, conditional formatting rules, per-column totals and subtotal rows grouped by a breakout, drill-through from any chart point or row into the underlying records, and CSV or JSON export. Reports also come in a spreadsheet type backed by Qrly's own spreadsheet engine, so the people who want a grid still get one.
No, and it is worth being blunt about it. Sigma input tables let a user type values back into the warehouse; Qrly is deliberately read-only. Native SQL must begin with SELECT or WITH, roughly 30 keywords including INSERT, UPDATE and DELETE are blocked outright, and the connection pool hands out read-only connections. If planning writeback is central to your workflow, Sigma does something Qrly does not. If read-only is the point — regulated data, or a production database nobody should be writing to from a dashboard — that constraint is the feature.
They solve the same problem from opposite ends. Sigma has no separate query language: you build in a spreadsheet grid and it compiles what you built into warehouse SQL, one way. QQL is a JSON query document assembled in a visual builder — source table or another saved question, a nested AND/OR filter tree over around 35 operators, aggregations, breakouts, having, joins and order-by with explicit NULL handling — compiled to 12 SQL dialects, so the same question runs unchanged on Postgres and BigQuery. It also round-trips: every visual question compiles to SQL you can read, and a flat SELECT converts back into a visual definition, with lossy constructs reported rather than hidden.
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 organization, one installation. Sigma does not publish list prices; it is quoted per user by account type. The line that catches finance out is the second one: because every view runs live against the warehouse, Snowflake, BigQuery or Redshift compute is billed on top by your cloud provider, and it scales with how often people look.
Self-hostable. Flat pricing. Built-in embedded analytics. On-prem AI. Made in Belgium.