Preset

Name: Preset
Brand: Preset

Data analysis, exploration, and visualization, powered by Apache SupersetTM

Est 2018|Valuation $62.3M|Raised $55.7M|15 Ref Customers

LinkedIn 7K|Web traffic 95K|FTEs 67|HQ San Francisco, CA

MarketsData Visualization

Published Oct 14, 2026

01Common complaints

1. Organizations that already operate Superset reliably in-house - If internal platform engineering teams maintain Superset operations, the incremental value of Preset's managed services may be limited. - Source: https://docs.preset.io/docs/faq 2. Organizations that require very large-scale customer-facing embedding with predictable non-seat costs - Preset's embedded viewer licensing starts at $500/month for 50 viewers; for very large external user counts, this pricing model can be a significant expense. - Source: https://preset.io/pricing/ 3. Organizations that rely heavily on third-party review volume for procurement decisions - Public review volume for Preset Cloud was limited in this dataset, which can make it harder to benchmark support and stability versus more-reviewed tools. - Source: https://www.g2.com/products/preset/reviews 4. Organizations that require single-tenant SaaS by default - Preset Cloud uses a multi-tenant architecture; customers requiring single-tenant hosting should evaluate the Managed Private Cloud option or other vendors. - Source: https://preset.io/trust-and-security/

Deal disqualifiers (poor fit)

Requirement for extensive out-of-the-box, business-user modeling and governance similar to Looker's LookML.
Need for very large-scale external embedding where non-seat-based pricing is required.
Analysts not proficient in SQL who require spreadsheet-native modeling experiences.

Vendor-documented limitations / gating

- Audit logs are available only on the Enterprise plan and are retained for 30 days by default; longer retention requires manual export or API downloads. This may not meet default retention expectations for some regulated environments. - Source: https://docs.preset.io/docs/audit-logs - SSO (SAML) is available only on the Enterprise plan; documentation notes potential behavior around account creation and Just-In-Time provisioning unless SCIM is configured. - Source: https://docs.preset.io/docs/saml-sso-okta

Roadmap / "not yet available"

No dated public roadmap was captured in the sources for this research run. Organizations should request roadmap details and timelines during procurement discussions.

Security/compliance gaps

Preset documents SOC 2 Type II, PCI-DSS Level 2, and HIPAA claims. ISO 27001 (and related standards) were not documented in the captured sources; organizations that require ISO certification should confirm directly with the vendor.

Source: https://preset.io/trust-and-security/

Why some customers might leave (churn reasons — evidence limitations)

Direct churn reasons are not well documented in the captured sources. Typical churn drivers for managed BI platforms in this category (based on industry patterns) include: - Seat cost growth as external viewer counts increase (especially for embedded analytics). - Gaps in governance or semantic modeling compared with enterprise incumbents if centralized metric definitions and lineage are required. - Preference for a modeling layer tailored to business users rather than SQL-first exploration (e.g., LookML in Looker). These points are plausible but not strongly evidenced by public reviews in this dataset.

02Why they buy

Differentiators supported by vendor documentation

- Managed Apache Superset with frequent updates and management of operational components. - Support for multiple isolated workspaces for organizational separation. - Alignment with open-source foundations (Apache Superset) to reduce dependency on proprietary formats. Sources: https://preset.io/product/ https://preset.io/

Differentiators supported by available customer feedback

At least one reviewer indicated that Preset provides a managed Superset experience and that it makes the user experience more intuitive compared with self-managing Superset.

Source: https://www.g2.com/products/preset/reviews

Areas where competitors can have clearer advantages

Tableau: mature visualization capabilities and broader non-technical user adoption.
Looker: governed modeling and enterprise-scale semantic layer capabilities.
Metabase: perceived simplicity for non-technical users and packaged embedding features.

Sources: competitor product pages

03Field reports

Review platform signals (availability caveat)

G2 (Preset listing): Very limited review volume in the captured dataset. This sample size is insufficient to generalize sentiment.
Trustpilot: A Trustpilot listing associated with a similar domain appears to relate to a different entity and should not be used to assess the Preset BI product.

Direct quotes (G2)

Positive:

"Preset is a managed version of Superset which is a rapidly improving open-source data visualization tool. Preset features the latest updates to Superset usually and it makes the user experience very intuitive and convenient." (G2 reviewer, mid-market)

Feature gap request (mild negative / wish-list):

"...one thing I would wish for a new feature is a native integration with dbt..." (same G2 review)

Community / social signals

This research did not surface high-signal Reddit or Hacker News threads specifically about Preset Cloud adoption; many community discussions focus on Apache Superset itself. The available public customer feedback in this dataset is limited and therefore may not fully represent broader user experiences.

Why customers choose Preset over alternatives (inferred from positioning)

Based on Preset's documentation and positioning:

Organizations that want Superset capabilities without an internal operations commitment may select a managed offering to reduce platform engineering work related to queues, workers, caching, alert delivery, and monitoring.
Organizations preferring an open-source foundation and extensibility may select a Superset-based managed offering to retain compatibility with open formats.

Availability of independent, quantified case studies

Preset's website references customer stories and press materials mention early customers. Detailed case studies with independently validated ROI metrics or audited time-to-value data were not captured in the sources for this research run.

Critical evaluation

Vendor metrics risk: If Preset provides ROI claims in sales collateral, prospective customers should request the methodology, time horizon, and cost assumptions used.
Recommended evidence approach: Request reference customers with similar data stacks and usage patterns (internal BI vs embedded analytics), and examples of migration effort from Tableau/Looker/Metabase or from self-hosted Superset.

04Best fit

Company sizes (practical ranges)

Small teams (≤5 users): The free Starter tier may meet needs for small teams.
Mid-market (50–1,000 employees): Professional tier can address collaboration and governance requirements.
Enterprise: Enterprise tier or Managed Private Cloud suits organizations requiring SSO/SCIM/audit logs, dedicated SLAs, or single-tenant options.

Industries and verticals

No explicit vertical segmentation was present in the captured sources. Preset is likely to be relevant to data-driven SaaS firms, marketplaces, and organizations using modern cloud data warehouses (Snowflake, BigQuery, Redshift, Postgres).

Roles and personas

Decision makers: Head of Data, VP Engineering, Data Platform lead, BI/Analytics leader, Security/Compliance lead, Product leader.
End users: Data analysts, analytics engineers, data scientists, SQL-proficient business analysts, and dashboard consumers.

Qualification criteria (good fit)

Organizations that require Superset capabilities without the operational burden of running it.
Need for multiple isolated workspaces for teams or environments.
Preference for an open-source foundation and compatibility with Superset.
Comfort with SQL-first analytics workflows.

05The pitch

What the product does (objective core value proposition)

Preset is a managed Business Intelligence (BI) and data visualization platform built on Apache Superset™. In practical terms, it provides: - A hosted Superset experience (Preset Cloud) so teams can build dashboards, explore data with SQL and no-code chart building, and share insights without operating Superset infrastructure themselves. - Enterprise-oriented additions around governance, identity, reliability, multi-workspace management, and embedding (including viewer licenses for embedded dashboards). Preset is positioned as a BI platform that uses Superset as its foundation and emphasizes open formats and extensibility to reduce dependence on proprietary formats.

Target market and market positioning

Preset is primarily aimed at: - Modern data-stack organizations using SQL databases and cloud warehouses that require Superset capabilities without self-hosting. - Teams that prefer developer-friendly, open-source-aligned BI (Superset + APIs) rather than a proprietary semantic model and visualization stack. - Organizations that need multiple isolated workspaces (separating teams/environments) and governance features (RBAC/RLS, audit logs, SSO). It competes in the cloud BI and embedded analytics market against proprietary BI suites (Tableau, Looker) and modern BI tools (Metabase, Sigma), and also competes indirectly with self-managed Apache Superset deployments.

Get a comprehensive analysis of Preset including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Oct 14, 2026

01Common complaints

Organizations that already operate Superset reliably in-house
- If internal platform engineering teams maintain Superset operations, the incremental value of Preset's managed services may be limited.
- Source: https://docs.preset.io/docs/faq
Organizations that require very large-scale customer-facing embedding with predictable non-seat costs
- Preset's embedded viewer licensing starts at $500/month for 50 viewers; for very large external user counts, this pricing model can be a significant expense.
- Source: https://preset.io/pricing/
Organizations that rely heavily on third-party review volume for procurement decisions
- Public review volume for Preset Cloud was limited in this dataset, which can make it harder to benchmark support and stability versus more-reviewed tools.
- Source: https://www.g2.com/products/preset/reviews
Organizations that require single-tenant SaaS by default
- Preset Cloud uses a multi-tenant architecture; customers requiring single-tenant hosting should evaluate the Managed Private Cloud option or other vendors.
- Source: https://preset.io/trust-and-security/

Deal disqualifiers (poor fit)

Requirement for extensive out-of-the-box, business-user modeling and governance similar to Looker's LookML.
Need for very large-scale external embedding where non-seat-based pricing is required.
Analysts not proficient in SQL who require spreadsheet-native modeling experiences.

Vendor-documented limitations / gating

Audit logs are available only on the Enterprise plan and are retained for 30 days by default; longer retention requires manual export or API downloads. This may not meet default retention expectations for some regulated environments.
- Source: https://docs.preset.io/docs/audit-logs
SSO (SAML) is available only on the Enterprise plan; documentation notes potential behavior around account creation and Just-In-Time provisioning unless SCIM is configured.
- Source: https://docs.preset.io/docs/saml-sso-okta

Roadmap / "not yet available"

No dated public roadmap was captured in the sources for this research run. Organizations should request roadmap details and timelines during procurement discussions.

Security/compliance gaps

Source: https://preset.io/trust-and-security/

Why some customers might leave (churn reasons — evidence limitations)

Direct churn reasons are not well documented in the captured sources. Typical churn drivers for managed BI platforms in this category (based on industry patterns) include:

Seat cost growth as external viewer counts increase (especially for embedded analytics).
Gaps in governance or semantic modeling compared with enterprise incumbents if centralized metric definitions and lineage are required.
Preference for a modeling layer tailored to business users rather than SQL-first exploration (e.g., LookML in Looker).

These points are plausible but not strongly evidenced by public reviews in this dataset.

02Why they buy

Differentiators supported by vendor documentation

Managed Apache Superset with frequent updates and management of operational components.
Support for multiple isolated workspaces for organizational separation.
Alignment with open-source foundations (Apache Superset) to reduce dependency on proprietary formats.

Sources: https://preset.io/product/ https://preset.io/

Differentiators supported by available customer feedback

At least one reviewer indicated that Preset provides a managed Superset experience and that it makes the user experience more intuitive compared with self-managing Superset.

Source: https://www.g2.com/products/preset/reviews

Areas where competitors can have clearer advantages

Tableau: mature visualization capabilities and broader non-technical user adoption.
Looker: governed modeling and enterprise-scale semantic layer capabilities.
Metabase: perceived simplicity for non-technical users and packaged embedding features.

Sources: competitor product pages

03Field reports

Review platform signals (availability caveat)

G2 (Preset listing): Very limited review volume in the captured dataset. This sample size is insufficient to generalize sentiment.
Trustpilot: A Trustpilot listing associated with a similar domain appears to relate to a different entity and should not be used to assess the Preset BI product.

Direct quotes (G2)

Positive:

"Preset is a managed version of Superset which is a rapidly improving open-source data visualization tool. Preset features the latest updates to Superset usually and it makes the user experience very intuitive and convenient." (G2 reviewer, mid-market)

Feature gap request (mild negative / wish-list):

"...one thing I would wish for a new feature is a native integration with dbt..." (same G2 review)

Community / social signals

Why customers choose Preset over alternatives (inferred from positioning)

Based on Preset's documentation and positioning:

Organizations that want Superset capabilities without an internal operations commitment may select a managed offering to reduce platform engineering work related to queues, workers, caching, alert delivery, and monitoring.
Organizations preferring an open-source foundation and extensibility may select a Superset-based managed offering to retain compatibility with open formats.

Availability of independent, quantified case studies

Critical evaluation

Vendor metrics risk: If Preset provides ROI claims in sales collateral, prospective customers should request the methodology, time horizon, and cost assumptions used.
Recommended evidence approach: Request reference customers with similar data stacks and usage patterns (internal BI vs embedded analytics), and examples of migration effort from Tableau/Looker/Metabase or from self-hosted Superset.

04Best fit

Company sizes (practical ranges)

Small teams (≤5 users): The free Starter tier may meet needs for small teams.
Mid-market (50–1,000 employees): Professional tier can address collaboration and governance requirements.
Enterprise: Enterprise tier or Managed Private Cloud suits organizations requiring SSO/SCIM/audit logs, dedicated SLAs, or single-tenant options.

Industries and verticals

Roles and personas

Decision makers: Head of Data, VP Engineering, Data Platform lead, BI/Analytics leader, Security/Compliance lead, Product leader.
End users: Data analysts, analytics engineers, data scientists, SQL-proficient business analysts, and dashboard consumers.

Qualification criteria (good fit)

Organizations that require Superset capabilities without the operational burden of running it.
Need for multiple isolated workspaces for teams or environments.
Preference for an open-source foundation and compatibility with Superset.
Comfort with SQL-first analytics workflows.

05The pitch

What the product does (objective core value proposition)

Preset is a managed Business Intelligence (BI) and data visualization platform built on Apache Superset™. In practical terms, it provides:

A hosted Superset experience (Preset Cloud) so teams can build dashboards, explore data with SQL and no-code chart building, and share insights without operating Superset infrastructure themselves.
Enterprise-oriented additions around governance, identity, reliability, multi-workspace management, and embedding (including viewer licenses for embedded dashboards).

Preset is positioned as a BI platform that uses Superset as its foundation and emphasizes open formats and extensibility to reduce dependence on proprietary formats.

Target market and market positioning

Preset is primarily aimed at:

Modern data-stack organizations using SQL databases and cloud warehouses that require Superset capabilities without self-hosting.
Teams that prefer developer-friendly, open-source-aligned BI (Superset + APIs) rather than a proprietary semantic model and visualization stack.
Organizations that need multiple isolated workspaces (separating teams/environments) and governance features (RBAC/RLS, audit logs, SSO).

It competes in the cloud BI and embedded analytics market against proprietary BI suites (Tableau, Looker) and modern BI tools (Metabase, Sigma), and also competes indirectly with self-managed Apache Superset deployments.

Get a comprehensive analysis of Preset including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →