Sisense sells AI as a separate add-on, negotiates embedding rather than including it, and builds its performance story around a copy of your data held in an Elasticube. Qrly ships natural-language Ask (NL→SQL), AI change detection and BYO LLM (local Ollama / LM Studio or cloud Claude, Gemini, OpenAI, Azure) alongside signed-JWT embedding — one product, transparent flat pricing, queries running in place against your own databases.
No marketing fluff. Here is where each tool is genuinely stronger.
The features most teams actually evaluate when switching from Sisense.
From real migration conversations with data and engineering leaders.
Embedded and OEM analytics is what Sisense is genuinely best at. The SDKs are mature, the white-labelling is thorough, and if you are putting dashboards inside a product you sell, Sisense has solved problems most BI vendors have not looked at. We are not going to pretend otherwise.
The friction is commercial rather than technical. Embedding for external audiences is negotiated rather than switched on — fine when you know at the start of a project, painful when you find out halfway through one.
Qrly embeds dashboards with a signed JWT and locked parameters as part of the base licence. No embedded SKU, no OEM agreement, no separate conversation — the fee is your revenue tier, and shipping the feature to more customers does not change it.
Sisense does not publish a price list, so the sticker is whatever comes back from a sales cycle — and we are not going to invent one on their behalf. What is knowable in advance is the shape of it: the meter is people, and AI, additional environments, premium support and marketplace add-ons are quoted on top of the platform. A number that is negotiated rather than published is a number that moves again at every renewal.
The worst part of per-seat pricing is not the sticker price — it is the behavioural consequence. Teams delay onboarding, share logins, and avoid giving read-only access to the product managers, engineers and executives who should be watching the numbers. The tool becomes a cost centre to ration rather than a system of record to open up.
Qrly is priced as a flat licence tied to your company's revenue tier. The 51st user costs nothing. Neither does the 501st, nor the read-only product manager who just wants to see this week's trend.
Sisense's performance comes from the Elasticube: a columnar store that ingests your data, models it, and serves queries from its own copy. It is fast, and it is a real engineering achievement. It is also a second place your data lives, with a build schedule, a freshness lag, a storage footprint and — for anyone maintaining a data-protection register — another system to describe, secure and account for.
Live connections exist, but the product is designed around the cube. Teams that adopt it end up managing builds: which cubes rebuild when, which failed overnight, and which dashboard is quietly serving yesterday's numbers to somebody who thinks it is today's.
Qrly copies nothing. Questions compile to SQL and run against your database on a read-only connection with a row cap, a statement timeout and bound parameters. Where you want speed it is opt-in and explicit — four caching tiers, per-question result materialisation with a refresh interval, and a query governor — rather than an architecture that assumes a copy.
Sisense's AI sits behind a separate add-on and runs against models and infrastructure the vendor 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 it off, which removes the reason it 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, with row access an explicit per-connection opt-in. Ask writes SQL you can read before you run it, and the MCP server publishes read-only tools with no write path at all.
Our own list price in full, and an honest account of how Sisense 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 database connection, the SQL, and the definitions you rebuild on top of them.
For most self-service and embedded analytics work — yes. Qrly reaches 40 connection types, compiles the same question to 12 SQL dialects, builds dashboards with cascade filters and live push, and embeds them with a signed JWT and locked parameters. Where Sisense stays ahead is the depth of its embedded and OEM tooling and the raw speed of a well-built Elasticube over very large models. If you are shipping analytics as a product feature to thousands of end customers, that is a real argument for staying, and we would rather say so than wave it away.
Not as objects — Elasticube models and Sisense widgets have no portable form, and no BI vendor imports a competitor's content honestly whatever the migration page claims. What moves is the layer underneath. Point Qrly at the same databases and the tables are already there. Paste saved SQL into native questions, and Qrly reverse-engineers a flat SELECT into a visual definition covering joins, filters, group by, having, order by with NULL ordering and limit/offset, reporting CTEs, UNION and subqueries as explicit lossy-feature warnings. An Elasticube model rebuilds as a Qrly OLAP star or snowflake model with its own dimension and measure catalogue and named hierarchies.
Sisense genuinely wins on depth here, and it is the main reason we would not tell an analytics-as-a-product company to switch on a whim. Its embedding SDKs, white-labelling and OEM tooling are the most complete in the category. Qrly's embedding is simpler: a dashboard, a signed JWT, locked parameters, and per-tenant isolation from the tenant level down. What it has going for it is that it is included in the base licence, needs no OEM agreement, and runs inside your own perimeter — so your customers' data never reaches a vendor at all.
No. Qrly's native SQL path is a Monaco editor with schema-aware autocomplete, dot completion on aliases, a function catalogue with signatures, EXPLAIN, an AI explain, and template variables bound as prepared-statement parameters rather than interpolated as strings. Native SQL must begin with SELECT or WITH and around thirty keywords are blocked outright, so handing the editor to an analyst is not a risk decision. And any flat SELECT converts to a visual definition, which means a question written in SQL stays editable by someone who does not write SQL.
Yes. Dashboards embed with a signed JWT carrying locked parameters, so an embedded view is scoped before it ever reaches the browser, and multi-tenancy runs from tenant through organisation, project and collection rather than being a filter bolted on at the end. Each customer, reseller or internal business unit gets its own space with its own content. The honest limit is worth stating: Qrly has no general row-level security or data sandboxing — locked embed parameters and always-applied OLAP model filters are the mechanisms it offers.
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. Sisense does not publish a price list, so we are not going to invent one: it is quoted per user by tier, with AI, additional environments, premium support and marketplace add-ons priced on top. The comparison worth making is structural — one of those two numbers is public and fixed for the term, and the other is renegotiated at every renewal.
Self-hostable. Flat pricing. EU data residency by default. Made in Belgium.