Steep
Steep is metric-based BI for non-technical teams — metrics defined once, explored visually or asked in natural language with Steep AI. No SQL required.
What It Is
Steep is a metric-based BI tool. Metrics are defined once — typically by a data lead or analyst — and business users explore them through a visual interface or Steep AI, which answers natural-language questions directly against the governed metrics. The metric-first model means every chart and every AI answer shares the same definition of "revenue" or "active user", no matter who built it. Particularly strong for companies where the primary data consumers are in marketing, sales, or operations rather than engineering.
Steep has its own native semantic layer for metric definitions. It can also plug into dbt's Semantic Layer if the client already maintains metrics there — one metrics model, two surfaces.
Why We Chose It
Not every company needs LookML's governance or dbt-driven metrics. When the audience is non-technical and the primary need is consistent, explorable metrics without training everyone on SQL, Steep delivers at a fraction of Looker's cost. Metrics stay consistent; exploration stays accessible. Steep AI credits (natural-language Q&A) are included in every paid tier.
How We Use It
Connect Steep to BigQuery and configure dataset access
Define the metrics layer in Steep — revenue, active users, conversion, whatever your business runs on
Plug Steep into the dbt Semantic Layer when the client already maintains metrics there
Train business users on metric exploration, dashboard creation, and Steep AI — no SQL required
Set up scheduled report delivery for regular stakeholder updates
Implement Steep alongside Lightdash, Metabase, or Looker in organizations with mixed technical audiences
When Steep is the right BI tool — and when it isn't
Choose Steep when:
- Primary users are non-technical (marketing, ops, sales)
- You want shared metric definitions without maintaining a full semantic layer
- Budget is under €500/month for a small team
- SQL access is not a requirement
Choose Metabase instead when:
- You need more than metric exploration (dashboards, embedding, SQL access)
- Your team mixes technical and non-technical users
- You want self-host optionality with a real AI assistant included
Choose Lightdash instead when:
- You want version-controlled metrics in code (dbt YAML or Lightdash YAML)
- SQL access for power users matters
Choose Looker instead when:
- Enterprise governance and a centralized semantic layer are required
- You have 20+ users across multiple departments