Qrly vs Sisense
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.
Who wins where
No marketing fluff. Here is where each tool is genuinely stronger.
BI + AI in one self-hosted platform — flat pricing
- AI Ask (NL→SQL) with AI anomaly detection & performance analyzer
- BYO LLM — local Ollama / LM Studio or cloud Claude, Gemini, OpenAI, Azure
- Embedded analytics with signed-JWT — no separate AI add-on
- QQL across 12+ database dialects, OLAP star/snowflake models
- Self-hostable on-prem, air-gapped, or Kubernetes
- Multi-tenant from tenant to project, OIDC SSO, transparent flat pricing
Core BI capabilities
- Both model dimensions and measures over your data
- Both ship dashboards, drill-down and scheduled delivery
- Both expose REST APIs for automation
- Both embed dashboards into another application
Embedded analytics and the Elasticube
- Best-in-class embedded and OEM analytics SDKs
- Elasticube serves fast queries over very large models
- JAQL gives developers programmatic query access
- Deep white-labelling for analytics you resell
- Long enterprise track record and partner network
Feature-by-feature comparison
The features most teams actually evaluate when switching from Sisense.
| Feature | RecommendedQrlySelf-hosted · Belgium | SisenseSisense |
|---|---|---|
| Self-hostable on your own infra | Included | Self-managed or cloud |
| Same saved question runs on 12 SQL dialects | Included | Model bound to the Elasticube |
| Built-in customer embed portal | Included | Core product |
| Native Alert with auto-escalation | Included | Strong |
| Native scheduled subscription (4 providers) | Included | Scheduled delivery |
| Visual query builder and raw SQL, round-trip convertible | SQL ⇄ visual | Elasticube / JAQL, one-way |
| Azure AD + Google + LDAP + Basic simultaneously | Included | SSO available, no LDAP |
| OIDC SSO user provisioning | Included | Enterprise tier only |
| AI with on-prem option (Ollama, LM Studio) | Included | AI add-on, vendor models |
| Multi-tenant architecture out of the box | Included | Multi-tenancy for embedded and OEM, no tenant hierarchy |
| Connects to your existing warehouse on day 1 | 40 connectors | Live connection or Elasticube |
| Flat pricing (unlimited users) | Included | Per user, quoted; add-ons on top |
| Productive in under 5 minutes | Included | Core yes, full config no |
| EU data residency (native, not a tier) | Included | Region by arrangement |
| No marketplace plugin required for basics | Included | Marketplace often required |
| Dashboards, charts and geographic maps | Included | Dashboards included |
| REST API + webhooks | Included | Included |
| OLAP models with rollup and period-over-period | Included | Elasticube aggregations |
Four reasons teams leave Sisense
From real migration conversations with data and engineering leaders.
Sisense embeds beautifully — once the contract is signed
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.
The bill grows with the audience
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.
The Elasticube is another place your data lives
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.
The AI is an add-on, and it is not yours
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.
Pricing, honestly
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.
Qrly
Annual licence · priced on your revenue · unlimited users, one organisation, one installation
- Annual licence at €15M revenue €6,300
- Per analyst / per viewer €0
- Per query, per credit, per capacity unit €0
- Embedded dashboard consumers €0
- Embedding, alerts, OIDC/SSO, row-level rules Included at every tier
- Self-hosting and bring-your-own LLM Included at every tier
Three years of licence: €18,900
That is our published schedule evaluated at €15M of revenue — 0.18% of the first €500k, then 0.12%, 0.0525% and 0.027% on each tranche above it. It does not move when you add users. The whole rate card, from the €900 floor upward, is on the pricing page.
Sisense
Per user, by tier · quoted, not published
- How it charges Per user, by tier, on an annual agreement
- List prices Not published — quote only
- Deployment Sisense cloud or self-managed, priced separately
- Embedded and OEM use Its own commercial arrangement
- AI and add-on modules Quoted on top of the platform
- Renewal Renegotiated each term — see sisense.com/pricing
Sisense does not publish a price list, so nobody outside a live sales cycle can tell you what it costs — us included, which is why there is no number in this column. What you can establish before the first call: the meter is people, the modules are quoted separately, and the figure is renegotiated at every renewal. Ask them for a quote; ask us for the rate card, which is already public.
Moving from Sisense to Qrly
Most teams are up and running on Qrly within a working week.
The standard migration path
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.
- Point Qrly at the same databases. Qrly speaks 40 connection types and compiles to 12 SQL dialects — Postgres, MySQL, MariaDB, SQL Server, BigQuery, Snowflake, Redshift, SQLite, H2, DB2, Sybase and Pervasive — so the sources feeding your Elasticubes are available on day one, read-only, with a per-connection row cap and statement timeout.
- Bring the SQL across. Paste a saved SELECT into a native question and Qrly reverse-engineers it into a visual definition — joins, filters, group by, having, order by with NULL ordering, limit and offset — reporting CTEs, UNION and subqueries as explicit lossy-feature warnings rather than dropping them quietly.
- Rebuild the cube as an OLAP model. An Elasticube becomes a Qrly star or snowflake model: fact table, dimension joins, a dimension and measure catalogue, named hierarchies, and always-applied model filters acting as a security boundary. Rollup, cube and grouping sets, a year-to-hour time hierarchy, prior-period and prior-year comparison and Top-N within group come with it — queried in place, with no build schedule to babysit.
- Rebuild dashboards and wire the filters once. Cascade filters are authored at dashboard level and mapped per card in a card-by-filter matrix, with name matching guessing the obvious ones. Viewers supply a filter id and a value — never a column, never an operator.
- Cut over. Re-point your embedded views at Qrly's signed-JWT endpoints and keep Sisense read-only for a 30-day overlap while people rebuild the dashboards they actually open, then let the licence lapse at renewal.
Common questions
Can Qrly replace Sisense?
Can Qrly import our Sisense dashboards?
What about embedded and OEM analytics?
Do we still need a separate SQL tool?
Can external viewers see only their own data?
How much is Qrly vs Sisense for 50 users over 3 years?
The full alternatives matrix
Evaluating Qrly against another tool? Jump to the detailed side-by-side.