Steep

Name: Steep
Brand: Steep
Rating: 4.8 (5 reviews)

AI Native

Metrics-first BI for everyone | Hiring 🚀

Est 2021|Valuation $17.6M|Raised $5.5M

LinkedIn 3K|Website|Web traffic 68K|FTEs 28|HQ Stockholm, 01, SE

MarketsData Visualization Self-Service Analytics

Published Sep 26, 2026

01Common complaints

- Organizations that require a fully mature enterprise access control stack immediately (e.g., very granular RBAC/ABAC, SCIM-driven lifecycle automation, comprehensive audit logs), as reviewers noted gaps in enterprise feature completeness. - Teams that primarily require advanced visualization and customization for executive dashboards; competitors with deeper visualization toolsets may be more appropriate. - Organizations without an established metrics governance discipline and no plan to invest in metric definitions and processes; the product relies on a governed metrics layer to deliver consistent results. - Procurement models that depend on low-cost viewer licenses for hundreds or thousands of consumers, since the vendor’s pricing approach uses a single license type without lower-priced viewer tiers.

Deal disqualifiers (poor fit)

Requirement for extensive bespoke visualization, pixel-perfect dashboards, or advanced charting capabilities
Heavy reliance on large numbers of low-cost viewer seats for consumer users
Requirement for publicly documented compliance certifications (SOC 2, ISO 27001) as a precondition to procurement

Product maturity

Multiple reviewer comments indicate the product is under active development, which may mean some features are incomplete or subject to change.

Filtering and table output limitations

Reviewers requested additional filter capabilities (e.g., negation, numeric comparisons) and improved table-style outputs for certain investigative tasks.

Enterprise feature gaps

Some reviewers reported missing or limited enterprise-grade features for granular access control.

Vendor-documented constraints (terms)

The vendor’s terms include restrictions specifying that customer data should not include certain classes of sensitive personal data (examples listed in vendor terms). Organizations in regulated sectors (e.g., healthcare) should review these restrictions carefully.

02Why they buy

Unique selling points supported by customer evidence

Ease of adoption and usability: reviewers reported that the interface allows business stakeholders to perform self-service analysis with reduced need for analyst intervention.
Metrics-centered workflow: reviewers and vendor materials indicate that the platform places governed metric definitions at the center of the analytics workflow rather than dashboards.

Claims that require validation

Statements implying elimination of dashboard work or removal of analyst involvement should be validated, as most organizations still require governance, education, and edge-case analyst support.

Areas where competitors hold advantages

Tableau and Power BI: broader visualization breadth and established ecosystems
Looker: mature modeling and governance patterns (with steeper learning curve)
Metabase: open-source deployment and cost flexibility

03Field reports

Review availability snapshot

G2: very highly rated with a small review footprint
Gartner Peer Insights: no clearly accessible listing was found in the captured sources
TrustRadius: no readily accessible product reviews were found in the sources examined
Community channels: limited community references were observed; the product name is used for multiple unrelated products, which complicates search results

Positive user feedback (paraphrased)

- Reviewers reported the interface is easy for business stakeholders to use and that non-technical users could perform self-service analysis with less analyst intervention. - Several reviewers described the UI as straightforward for a broad range of users and reported responsive vendor support. - Reviewers and vendor stories indicate increased metric consumption and engagement after deployment; these claims are primarily vendor- or reviewer-reported and should be verified with quantitative usage metrics where possible.

Negative/critical user feedback (paraphrased)

- Several reviews note that the product is still maturing and that some features are limited or under development. - Reviewers indicated that organizations lacking a well-governed metrics layer may not get optimal results from the product without additional setup and governance work. - Some reviewers requested additional filter operators (e.g., negation, numeric comparisons) and improved table/record-level outputs for investigative use cases. - There are comments about needing more granular enterprise access control features for some large organizations.

Why customers selected Steep (as evidenced)

Faster adoption among non-technical stakeholders reported in reviews and vendor stories
Reduction in dashboard maintenance overhead by centralizing metric definitions
Alignment on business terminology through a governed metric catalog

Churn / switching evidence

A vendor-published case study notes that one customer replaced legacy dashboards with Steep to increase engagement and reduce costs. This is a vendor-originated case study and should be validated independently; the report recommends requesting pre/post metrics and procurement details for confirmation.

Deal-breakers / likely reasons to decline

Based on available review content and product positioning, likely deal-breakers include:

Requirement for extensive visualization customization that exceeds the product’s current capabilities
Need for mature enterprise governance controls at procurement (e.g., SCIM, very granular RBAC, advanced auditability) that are not documented as fully available
Organizations without a metrics-governance discipline that are not prepared to invest in building it

Voi Technology (vendor-published case study)

Vendor documentation describes Voi replacing Tableau dashboards with Steep, with objectives to increase engagement, support decision-making, and reduce costs. An executive quote in the vendor case asserts very high BI adoption following deployment. Critical evaluation: - The case originates from vendor channels. Specific quantitative details (percentage changes, baseline comparisons, pre/post user counts) were not present in the excerpted materials. - Buyers should request the following to validate outcomes: active user counts before and after adoption, definitions and measurement windows for "adoption," seat counts and cost comparisons (Tableau vs Steep) and impacts on data-team workload.

Juni (vendor blog reference)

Vendor materials state that Juni switched from Metabase to Steep and reported a substantial reduction in data requests. Critical evaluation: - The reported reduction appears in vendor-published materials. The methodology for measuring "data requests" is not described; buyers should ask the vendor or customer to clarify whether this refers to Slack tickets, help-desk requests, ad-hoc SQL queries, or another metric and request the measurement period and baseline.

04Best fit

Company sizes

Observed reviewers and customer references suggest a target focus on small-to-mid and mid-market organizations:

Organizations larger than early-stage startups but below large enterprise scale
Existing data function (analytics engineers or a BI/analytics lead) and warehouse-first analytics

Industries / verticals

Mobility/transportation (example customer: Voi)
Digital products, consumer apps, subscription businesses
Media and technology organizations

Roles and personas

Decision makers: Head/Director of Data, BI/Analytics leaders, CTO/CDO in smaller organizations
Implementers: analytics engineers, data analysts
End users: product managers, finance, operations, commercial teams, and leadership

Qualification criteria (good fit)

Organizations aiming to standardize KPI definitions across teams
Teams willing to invest in semantic/metrics governance
Organizations seeking broad adoption and mobile-capable analytics consumption

05The pitch

What the product does (core value proposition)

Steep is a metrics-focused business intelligence (BI) platform built around a governed semantic model (metrics, dimensions, entities, joins) that business users can explore without SQL, while data teams retain centralized control. The product emphasizes prioritizing centrally defined metrics over maintaining many dashboard artifacts, and provides an exploration surface for broader organizational report building, subscriptions, and sharing. Core capabilities documented in product materials include: - Central semantic model (native semantic layer; plus integrations with dbt Semantic Layer and Cube) - Metric catalog and exploration UI (filters, breakdowns, comparisons, cohorts, pivot/cohort tables) - Reports implemented as collaborative documents (charts, tables, numeric summaries, text blocks; PDF export; Slack subscriptions) - Caching layer intended to improve responsiveness and reduce warehouse load - Multi-platform delivery (web, Mac app, iOS, Android)

Target market and positioning

Steep is primarily positioned for: - Modern data-stack companies (cloud warehouse + dbt/ELT, with governance needs) - Organizations aiming to increase BI adoption among non-technical stakeholders - Teams that require consistent, centrally defined metrics for recurring KPI tracking The vendor messaging emphasizes adoption and user experience as a differentiator versus tools that require extensive training or specialized modeling (e.g., LookML) and versus approaches that result in many dashboards with inconsistent metric definitions.

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