Qrly vs Redash

Redash does one thing: you write SQL, it runs it, you pin the result on a dashboard. There is no modelling layer, no visual query builder and no reusable metric definition, so the same revenue calculation gets retyped in fourteen queries and drifts in nine of them. Everybody who wants a number either writes SQL or queues behind somebody who can.

Teams paper over it by copying an existing query and editing it, which is how an instance ends up with nine hundred queries and no idea which four matter. Rename a column in the warehouse and an unknown number of them break, quietly, in whichever dashboard somebody opens next.

Qrly keeps the raw-SQL path exactly as it is — Monaco, schema-aware autocomplete, EXPLAIN — and puts a visual query builder, calculated fields, question-as-view and OLAP models beside it. A flat SELECT converts into a visual definition, so SQL is a starting point rather than a permanent dependency.

Redash development has been largely dormant since the Databricks acquisition. Releases are rare and the capabilities a modern BI stack is expected to have — a semantic layer, AI assistance, governed metrics, embedded analytics with per-viewer scoping — are not arriving, because there is no roadmap for them to arrive on.

That is survivable while Redash keeps doing the one job it is good at. It stops being survivable the moment a database driver, an auth requirement or a compliance rule moves on and the upstream project does not move with it. At that point the maintenance is yours.

Qrly is actively developed, compiles to 12 SQL dialects across 40 connection types, and comes from one vendor with one contract, one upgrade path and one support line.

Self-hosting is the one thing Redash genuinely gives you: Docker, your server, your data. What it does not give you is the scaffolding around it — no OIDC provisioning, no SCIM, no per-user query budget, no query governor, no read-only guarantee beyond the database user you remembered to configure, and no on-prem AI because there is no AI at all.

For a regulated team — finance, health, public sector, defence suppliers, anyone with DORA, NIS2 or a sovereign-cloud clause — the DPO conversation is fine and the security review is not. Every control the auditor asks about has to be built around Redash rather than found inside it.

Qrly self-hosts on any Linux box or Kubernetes cluster and brings the controls with it: OIDC SSO, a sanitizer that accepts SELECT and WITH only, read-only pooled connections, per-user daily query budgets, a query governor, and AI running locally through Ollama or LM Studio so prompts never leave the perimeter.

Customer-facing analytics — per-tenant scoping, signed tokens, locked parameters, a portal your customers log into — is not something Redash attempts. A public dashboard link is the extent of it, and a public link is exactly what you cannot hand a customer when somebody else's data is one filter away.

In practice teams that need it bolt a second product alongside Redash — two tools, two bills, two data models, and every metric defined twice, differently, by two teams who never compare notes until a customer does it for them.

Qrly ships embedding with signed JWT and locked parameters, multi-tenant separation from Tenant down to Collection, dashboard cascade filters where a viewer may only supply a filter id and a value, and scheduled subscriptions — in the same product that runs your internal dashboards, with one audit trail and one permission model.