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clientId, not from a SQL clause your backend writes into every token.If Superset already serves your internal analysts well, keep it there. The two are not mutually exclusive: Databrain can serve the customer-facing layer while Superset stays internal.
No published Databrain case study replaced Apache Superset. These are the closest published stories: a team that replaced Metabase, another open-source BI tool, and a team that replaced in-house analytics. Figures and quotes are taken from each published case study. Two customers is a small sample.
Freight pricing platform, replaced Metabase
“Databrain allowed us to create a fully custom analytics module. Anybody in the org was now able to create metrics and share data with their tool”
SaaS, replaced in-house analytics
“We cut down on 6 months of work for our data analysts and saved around $300k by maintaining a smaller, more efficient team, avoiding the need to hire extra analysts just to handle ad-hoc reports.”
The short version of Databrain and Apache Superset side by side.
| Databrain | Apache Superset | |
|---|---|---|
| Built for | Multi-tenant B2B SaaS products embedding customer-facing analytics | A general data exploration and BI platform, with an embedding SDK for dashboards |
| How it embeds | Web components (Shadow DOM) for React, Next.js, Angular, Vue, Svelte, Solid and vanilla JS | An iframe of a Superset dashboard, mounted by the Embedded SDK |
| How tenants are scoped | clientId in a server-minted guest token; one data app serves every tenant | Row-level security clauses your backend writes into each guest token |
| Who needs a licence | Unlimited seats on every plan | No licence fee for the software. Preset: $20 per user per month (annual) on Professional, plus viewer licences for embedded dashboards |
| Pricing model | Flat and published: $999/mo Growth, $1,995/mo Pro, Enterprise custom | Free to self-host, and you run it. Preset: per user, plus embedded viewer licences from $500/mo for 50 viewers |
| Query model | Live queries against your warehouse | Queries your connected database, with an optional caching layer |
| Deployment | Databrain cloud, or self-hosted on Enterprise | Self-hosted, with Docker or the Superset Kubernetes Operator, or managed by Preset |
| Typical time to production | 1 to 4 weeks across five published customers; Spendflo 2 weeks, Freightify 1 week | We found no figure from Superset or Preset |
Databrain is an AI-native embedded analytics platform for B2B SaaS. You connect your warehouse with read-only access, define metrics, mint a guest token from your backend, and place a web component in your app.
Dashboards are white-labeled, and customers can ask questions in natural language.
Apache Superset is an open-source data exploration and visualization platform under the Apache 2.0 licence. It provides a no-code chart builder, a web-based SQL editor, a lightweight semantic layer, a caching layer, security roles and a REST API, and it connects to any SQL datastore with a Python driver.
To embed, you enable the Embedded SDK, which mounts an iframe of a Superset dashboard in your app, authenticated by a guest token from your backend. You run Superset yourself, or buy it managed from Preset, whose plans start with a free Starter tier.
Superset is software you deploy: the application, a metadata database, and an optional cache, worker and scheduler for some features, with upgrades and breaking-change notes handled by you. Databrain cloud is run by Databrain, and Enterprise can be self-hosted. Preset is the managed route for Superset.
In Superset a tenant is whatever row-level security clause your backend puts in a guest token, and the token also names the dashboards its holder can open. In Databrain a tenant is the clientId in a guest token, and one data app serves all tenants. Superset's model is flexible and documented; you write and own the clause.
Superset embeds a Superset dashboard page in an iframe, with options to hide the title and tabs, a per-dashboard theme (6.0 and later) and a light, dark or system theme mode. Databrain renders web components inside your page, configured through four theming layers.
The Superset software has no licence fee. Preset charges per user plus embedded viewer licences from $500 a month for 50 viewers. Databrain is a flat monthly price by feature, with unlimited seats, embeds and tenants.
Superset is a full BI application: a SQL editor, a no-code chart builder, a wide range of chart types, from bar charts to geospatial, and alerts and reports to email, Slack or a webhook. Databrain is a narrower embedding layer, which is the point if embedding is all you need.
Twenty-four capabilities that decide a customer-facing analytics build, Databrain against Apache Superset. Superset cells cite Superset's own documentation and README, and Preset's pricing page, all opened on 22 October 2026. Databrain cells cite our pricing page, Trust Center and product docs. Where Superset is stronger, the cell says so.
| Capability | Databrain | Apache Superset |
|---|---|---|
| Embedding and branding | ||
| Yes: Web components in your page | Yes: Iframe of a Superset dashboard, via the Embedded SDK | |
| Whether the analytics surface is part of your page or a document your page hosts. | <dbn-dashboard> and <dbn-metric> from @databrainhq/plugin render inside Shadow DOM. An iframe route exists and our docs mark it as not recommended. | The @superset-ui/embedded-sdk package inserts an iframe containing a Superset page into your app, using your app's authentication. You activate the EMBEDDED_SUPERSET feature flag and set a strong GUEST_TOKEN_JWT_SECRET. Superset also documents a public direct link in an iframe, and notes that everybody who knows the link can access the dashboard. |
| Configured through component options | Yes: UI config, URL params, sandbox and referrer options | |
| How much of the hosted page you can hide or adapt without touching Superset's code. | You configure the embed through the component options and four theming layers. | dashboardUiConfig accepts hideTitle, hideTab, hideChartControls and filter-bar visibility, plus URL parameters. The SDK also takes extra iframe sandbox attributes, Permissions Policy features, a referrerPolicy, and a resolvePermalinkUrl callback so shared links point at your domain. |
| Yes: Seven framework guides | Yes: One npm SDK, any container element | |
| How much glue code your team writes per framework. | Guides for React, Next.js, Angular, Vue, Svelte, Solid and vanilla JS, all against the same component. The npm package lists react and react-dom as peer dependencies, so non-React hosts install them too. | The SDK is an npm package, also loadable from a CDN, and mounts into any HTML element that can contain an iframe. We found no per-framework guides in its README. |
| Yes: Four theming layers, per-customer themes | Yes: Ant Design token themes per dashboard (6.0 and later) | |
| Whether "matches our design system" is a token map you set once or a theme file per dashboard. | Chart appearance, admin theme options, custom chart settings and saved metric settings resolve in a fixed priority order. White-labeling and custom UI themes per customer are on every plan; email white-labeling is on Pro and Enterprise. | From apache-superset 6.0, themes are Ant Design v5 token sets, with custom fonts, ECharts option overrides and a theme assigned per dashboard. Embedded dashboards take a themeMode of default, dark or system. We found no theme-per-token option in the docs: a theme is assigned to a dashboard. |
| Server-minted guest token with an expiry | Guest token from your backend, 5-minute default | |
| Both use a server-minted token. Check the expiry and refresh path against your session model. | Your backend mints the guest token with an expiry time. Databrain validates every request: token lookup, expiry, the request origin against your allowed domains, and permissions. | Your backend requests a guest token from Superset's guest token endpoint, using an account with the can_grant_guest_token permission, and hands it to the SDK; the SDK README also documents signing a guest token in your own app with the shared secret. Tokens expire (default 5 minutes), so your app must refresh them. Allowed domains are set per dashboard and checked against the Referer header; an empty list allows any origin. |
| Multi-tenancy and security | ||
| Yes: One data app, scoped by clientId | Yes: Guest-token RLS clauses you write | |
| Whether adding a tenant is a value in a token or a SQL clause your backend has to get right. | One data app serves every tenant, and you do not need a data app per tenant. Tenancy can be a shared table with a tenant column, or a schema or database per tenant. Most teams run a single workspace. | We found no tenant object in Superset's docs. Tenancy is built from guest tokens: your backend writes row-level security rules (SQL clauses, such as publisher = 'Nintendo' in Superset's own example) into each token, and the token lists the dashboards its holder can open. |
Pass a clientId | Write that tenant's clause into its token | |
| The per-tenant step your onboarding automation has to perform, and what breaks at the edges. | A new tenant's data is scoped as soon as your backend mints a guest token with its clientId, provided your tenant column, schema or database mapping covers it. | The embedding docs describe no per-tenant object to create: the rule travels in the token. Large rule lists make large tokens, and Superset's docs warn that proxies can reject oversized tokens (they cite a non-adjustable 16 K single-header limit on AWS Application Load Balancers) and suggest an entitlements-table subquery in place of long inline ID lists. |
| Partial: One shared dashboard; variants are copies | Token names the dashboards; assets API for copies | |
| If each customer gets their own layout, check how variants stay in step with a template. | By default every tenant sees the same dashboards, scoped by the guest token. For per-customer layouts, Databrain documents a master dashboard with client dashboards derived from it, provisioned through the Import Dashboard API or the MCP server. Import and clone are copy operations: later edits to the master do not propagate to client dashboards already created. | A guest token lists the dashboards its holder may open, so one dashboard can serve many tenants with different clauses, or tenants can hold different dashboards. Superset's REST API includes asset import and export. We found no documented master-and-derived dashboard inheritance. |
| Yes: Tenant filter injected into the SQL | Yes: RLS filters, clauses and Jinja | |
| Both are real row-level security. The difference is where the rule lives and who maintains it. | For builder metrics Databrain injects the tenant filter into the generated query server-side. Custom SQL metrics are not auto-injected: the author references the tenant variable, or the metric runs unscoped. Per-session constraints go in rlsSettings in the guest token, enforced server-side. A separate row and column level security feature is listed on Enterprise. | Row Level Security filters attach a SQL clause to a dataset and a set of users, groups or roles, and the clause is added to the generated query's WHERE clause. Regular and Base filter types and group keys control how filters combine. Clauses can use Jinja, such as current_username(), when template processing is enabled. Guest tokens carry their own rules. |
| Partial: SOC 2 and GDPR; ISO 27001 in progress | Software, so it follows how you run it | |
| Procurement will ask who holds the audit report for the deployment. | Databrain's Trust Center lists SOC 2 and GDPR as compliant and ISO 27001 as in progress. Our pricing page lists HIPAA as coming soon on Pro and Enterprise. Ask for the current report before you rely on any of them. | Apache Superset is open-source software, so the certifications of a deployment depend on how you host and operate it. We did not compare Preset's certifications. |
| Hosting and operations | ||
| Yes: Cloud, or self-hosted on Enterprise | Yes: Self-host anywhere, no plan tierApache Superset leads here | |
| If self-hosting is a hard requirement and you do not want it tied to a plan, Superset gives it to you. | Cloud, or self-hosted in your own infrastructure on Enterprise, with VPC peering and a bastion host. Multi-region deployment and auto-scaling with Kubernetes are also listed on Enterprise. | The software is Apache 2.0, so you can run it on your own infrastructure at no licence cost. Superset documents Docker, and the official Superset Kubernetes Operator for Kubernetes; the Helm chart is marked deprecated. Preset offers it managed, including multi-region support on Professional and Managed Private Cloud on Enterprise. |
| Databrain runs the cloud service | You run the app, metadata database and workers | |
| Who is paged when a dashboard stops loading, and who owns the upgrade. | On Databrain cloud, Databrain operates the platform, and queries run live against your warehouse. If you turn on Databrain's managed cache, cached query results are held in Databrain's Redis; bring your own Redis to keep them in your infrastructure. On the self-hosted Enterprise option you run it in your own infrastructure. | A deployment is the application, a metadata database, an optional caching layer, and an optional worker and beat scheduler that some features need. Installing from PyPI creates only the application. Upgrades mean superset db upgrade and superset init, a metadata backup, and reading the version-by-version breaking-change notes in UPDATING.md. |
| Data and semantic layer | ||
| Yes: Warehouses and databases, live | Yes: Any SQL datastore with a Python driverApache Superset leads here | |
| Confirm your exact database in both lists. Superset's list is longer and documented per engine. | Databrain's pricing page names Redshift, Snowflake, MySQL, Postgres and ClickHouse, and says it supports all the popular data stores. | Superset can query data from any SQL-speaking datastore or data engine that has a Python DB-API driver and a SQLAlchemy dialect, and its README shows dozens of named databases. |
| Partial: Live queries; caching on Pro and up | Yes: Queries your database; optional caching layer | |
| Heavy dashboards need a caching plan on either side. | Every surface queries your warehouse live through the same tenant-scoped path. Caching is available on Pro and Enterprise, in Databrain's managed Redis or your own. | Superset lists a lightweight, configurable caching layer to ease database load. The architecture page calls the cache optional but necessary for some features, and you deploy and configure it yourself. |
| Partial: Semantic layer on Enterprise | Yes: Lightweight semantic layer includedApache Superset leads here | |
| Reusable metric definitions are available in every Superset install, and on Databrain's Enterprise plan. | Datasets, datamarts and the semantic layer are listed under Data studio on the Enterprise plan. On Growth and Pro, metrics are built directly with the builder or custom SQL. | Superset's README lists a lightweight semantic layer for quickly defining custom dimensions and metrics, as part of the open-source software. |
| Builder, custom SQL, natural language | No-code chart builder and SQL editor | |
| Who on your team can ship a new metric without help. | Metrics are built by drag and drop, custom SQL or natural language. A Python and SQL environment is on every plan. | Superset provides a no-code interface for building charts quickly and a web-based SQL editor for advanced querying. |
| Chart library in the metric builder | Yes: 40+ chart types on PresetApache Superset leads here | |
| If you need an unusual chart type, check Superset's gallery first. | Databrain builds charts through its metric builder. We did not count Databrain's chart types for this page. | Preset lists 40+ visualization types, and the Superset README describes a wide array of visualizations from simple bar charts to geospatial ones. |
| What your customer gets | ||
| Yes: Natural-language search on every plan | Partial: MCP server for AI assistants; Preset chatbot add-on | |
| Whether your customers ask questions inside your product, or an analyst connects an assistant. | Natural-language search is included on every plan, and follow-up chat on Pro and Enterprise. Connect OpenAI, Azure OpenAI, Claude, Gemini, Llama or Mixtral. Questions run through the same tenant-scoped query path as dashboards. | Superset's docs describe AI through an MCP server (Superset 5.0 or later) that an admin deploys as a separate process. Clients such as Claude or ChatGPT connect to it to explore datasets, build charts and dashboards, and run SQL under Superset's roles. We found no documented in-dashboard question box for embedded viewers. Preset lists an AI Chatbot, with a 14-day trial on Professional and, on Enterprise, a bring-your-own OpenRouter API key, plus MCP server integration. |
| Partial: Enterprise plan | Read-only guest role by default | |
| Whether every customer request for a new chart becomes a ticket for your team. | End-user report creation is an Enterprise feature, and also needs the workspace and permissions configured to allow it. | The guest user defaults to the Public role (GUEST_ROLE_NAME), which Superset describes as minimal read-only access that includes reading embedded dashboards. Preset describes its embedded viewer licences as giving limited interactive capabilities to view and explore data. |
| Yes: Scheduled email reports, per client | Yes: Alerts and reports to email, Slack or a webhookApache Superset leads here | |
| If customers need Slack or webhook delivery, or condition-based alerts, Superset lists them. | Databrain's docs describe email reports with charts attached, sent daily, weekly or monthly to chosen recipients and clients, and a flow where an end user schedules their own email report from a shared dashboard link. Email settings need to be configured first. The pages we read describe email delivery only. | Reports are sent on a schedule and alerts when a SQL condition is reached, to an email recipient or a Slack channel, or to any HTTP endpoint once the webhook feature flag is on. Dashboards go out as PDF or PNG; chart data can go as CSV, XLSX or text. They are disabled by default; enabling them takes configuration, a headless browser, and a worker and scheduler. |
| Token and API management | Yes: OpenAPI REST API with asset import | |
| Relevant if you manage analytics through a release pipeline. | Server-to-server API keys mint guest tokens, and dashboards can be exported as JSON and imported through the UI or the Data App APIs, including client-specific export and import. | Superset's REST API follows the OpenAPI specification, authenticates with JWT tokens, and covers dashboards, charts and datasets, with an endpoint to import multiple assets. |
| Commercial | ||
| Yes: Flat and published | Free software; Preset is per user plus viewer licences | |
| One of these bills is a number you can read today for your customer count; the other depends on how you host it. | Growth $999/mo, Pro $1,995/mo, Enterprise custom. Seats, embeds and tenants are unlimited on every plan. | The Apache Superset software is free under Apache 2.0, and running it needs the components listed under Hosting and operations. Preset lists a free Starter tier (1 workspace, up to 5 users), Professional at $20 per user per month billed annually or $25 billed monthly, and Enterprise at custom pricing. Embedded dashboards are an add-on on Professional and Enterprise, and need viewer licences starting at $500 a month for 50 viewers. |
| Slack, Teams or Google Spaces chat; SLA on Enterprise | Community, or Preset support tiers | |
| Check what the support tier you can afford actually includes. | Support is a Slack, Microsoft Teams or Google Spaces channel plus in-app chat on every plan. Enterprise adds 24x7 premium support, a 99.5% uptime SLA and a dedicated account manager. | The Superset README points to Stack Overflow, a community Slack and a mailing list. Preset lists standard customer support on Professional, and an enterprise SLA and, as an add-on, dedicated support via Slack on Enterprise. |
| Commercial product, priced by plan | Yes: Apache 2.0, readable and forkableApache Superset leads here | |
| If owning the code matters to you or your customers, Superset gives you it. | Databrain is a commercial product with published plans. | Superset is licensed under Apache 2.0 on GitHub. You can read the source and modify it for your own needs. |
Tenant isolation is the hardest part of customer-facing analytics, and Databrain and Apache Superset solve it in different places. Superset puts it in the row-level security rules your backend writes into each guest token. Databrain puts it in the token's clientId and the generated query.
Your backend mints a guest token that carries the tenant clientId. The embed sends that token with each request. Databrain looks it up server-side, checks expiry, permissions and the allowed origin, then generates SQL with the tenant filter applied before the query reaches your warehouse. One data app serves every tenant, and extra per-session constraints go in rlsSettings.
One sharp edge: in custom SQL metrics the tenant filter is not auto-injected, so the author must reference the tenant variable. A custom metric that omits it runs unscoped.
Databrain's Trust Center lists SOC 2 and GDPR as compliant and ISO 27001 as in progress; HIPAA is listed as coming soon on our pricing page. Compare the lists above against your buyers' requirements.
Superset's Embedded SDK mounts a Superset dashboard in an iframe and authenticates it with a guest token. Your backend creates the token by calling Superset's guest token endpoint with an account that holds the can_grant_guest_token permission. The token names the dashboards its holder can open, and it can carry row-level security rules: SQL clauses that filter data for the user carrying the token.
We found no tenant object in Superset's docs. A tenant is whatever clause your backend writes: the clause is plain SQL text, so its correctness is yours to test. Superset also has dataset-level Row Level Security filters, assigned to users, groups or roles, with Regular and Base types, group keys, and Jinja templating such as current_username().
Details from Superset's own docs: guest tokens expire (5 minutes by default); allowed domains are set per dashboard and checked against the Referer header, and an empty list allows any origin, so set it explicitly; the guest user defaults to the Public role; and an optional datasets allowlist in the token limits which SQL datasets are reachable. Large rule lists make large tokens that proxies can reject, and Superset suggests an entitlements-table subquery instead.
Compliance follows your hosting: Superset is software, so a self-hosted deployment is as compliant as you make it. We did not compare Preset's certifications.
clientId and your data app name.POST /api/v2/guest-token/createrls clause.Referer against the dashboard's allowed domains.Both can isolate tenants. The difference is whether the tenant rule is a SQL clause your backend writes into every token or a clientId mapped to a column, schema or database.
Both lists are genuine. If your situation matches the Superset side, that is the right call.
Match your situation to the row, then check the feature comparison for the detail.
| If your question is | Lean toward | Because |
|---|---|---|
| The viewer is your customer, and you do not want to run an analytics stack | Databrain | Databrain cloud is operated for you and has no per-viewer cost. With Superset you run it, or pay Preset per user plus viewer licences. |
| Many tenants with self-serve onboarding | Databrain | Tenant scope comes from a guest token, and one data app serves all tenants. In Superset your backend writes and tests a clause for each token. |
| Customers should ask questions in natural language inside the product | Databrain | Natural-language search is on every Databrain plan. Superset's documented AI path is an MCP server for assistants. |
| You want to read, modify or fork the source | Apache Superset | Superset is Apache 2.0 on GitHub. |
| You already run Superset for internal analysts | Either | Keep it for that. Databrain can serve the customer-facing layer while Superset stays internal. |
| You need a database engine outside Databrain's list | Apache Superset | Superset works with any SQL datastore that has a Python driver and a SQLAlchemy dialect. Confirm yours in both lists. |
| Customers need Slack or webhook delivery, or condition-based alerts | Apache Superset | Superset lists alerts and reports to email, Slack or a webhook. The Databrain pages we read describe email delivery. |
| You need forecastable spend from a published price | Databrain | A flat monthly plan with unlimited seats, embeds and tenants. |
| You need to self-host without a plan tier | Apache Superset | Superset is free to self-host. Databrain self-hosting is an Enterprise feature. |
A flat plan and a free-software-plus-hosting model are different shapes of cost, and neither is cheaper in every case. Compare Databrain with your own hosting cost for Superset, or with a Preset quote at your viewer count.
Growth includes multi-tenancy and 1 data source. Pro adds unlimited data sources, SSO and roles and permissions. Enterprise adds self-hosting, multi-region deployment, VPC peering and bastion host, and a dedicated account manager.
Self-hosting Superset has no licence fee, and you pay for the infrastructure and the engineers that run it. We do not put a figure on that, because neither Superset nor Preset publishes one. Preset's embedded dashboards need viewer licences, which give access without a Preset user account and limited interactive capabilities. At the listed starting rate, 50 viewers is $500 a month. Preset lists viewer licence discounts and unlimited licensing on Enterprise.
Superset itself has no price, so the Superset column shows Preset's list prices as stated on its pricing page. Databrain prices are from the pricing page. Both were read on 22 October 2026.
Keep Superset for internal reporting if it works there. The move is for the customer-facing layer. Five published customers went live in 1 to 4 weeks (Freightify 1, Spendflo 2, EpochOS 2, BerryBox 3, SpotDraft 4), and you can run both side by side until you cut over.
List the dashboards your guest tokens expose and the datasets and metrics behind them.
Connect to Databrain with read-only access.
Choose how tenants are separated: a tenant column, or a schema or database per tenant. The clauses your backend writes into Superset guest tokens tell you which column that is.
Rebuild them as Databrain metrics, using drag and drop, custom SQL or natural language.
From your backend, create a token with each customer's clientId.
Replace the Superset iframe with a Databrain web component for a pilot group and run both.
Move the remaining tenants, then retire the Superset embedded dashboards and the service account that mints guest tokens.
Plan to recreate dashboards rather than import them: we document no automated Superset importer. No published Databrain customer has migrated from Superset. The range comes from the five published case studies.
Yes, for customer-facing analytics inside a SaaS product. Databrain renders as web components, scopes each tenant from a guest token and charges a flat monthly plan. It is not a replacement for Superset as an open-source BI tool for your internal analysts, so keep Superset for that if you rely on it. For the wider shortlist, see our Apache Superset alternatives guide.
Not as a tenant object that we found in its docs, but it can isolate tenants. Superset's Embedded SDK uses guest tokens that carry row-level security rules, and it also has dataset-level Row Level Security filters. You write the clause for each tenant, and its correctness is yours to test. Databrain takes the tenant from a clientId in the guest token, and builder metrics get the tenant filter injected automatically.
Yes. The Embedded SDK mounts a Superset dashboard in an iframe using your app's authentication. You enable the EMBEDDED_SUPERSET feature flag, set a strong GUEST_TOKEN_JWT_SECRET, create guest tokens from your backend, and set allowed domains per dashboard.
The Superset software is free under Apache 2.0, and you run it yourself. Preset, the managed service, lists $20 per user per month billed annually on Professional, with embedded dashboards as an add-on that needs viewer licences from $500 a month for 50 viewers. Databrain is flat and published: $999 per month for Growth and $1,995 for Pro, with unlimited seats, embeds and tenants. Add your own hosting and engineering cost to the Superset side.
Superset's docs describe AI through an MCP server, available from Superset 5.0, that lets assistants such as Claude or ChatGPT explore data, build charts and run SQL under Superset's roles. We found no documented in-dashboard question box for embedded viewers. Preset lists an AI Chatbot on Professional (14-day trial) and Enterprise. Databrain includes natural-language search on every plan and follow-up chat on Pro and Enterprise, with your choice of LLM.
Yes, on the Enterprise plan, with VPC peering and a bastion host. Superset is free to self-host with no plan tier, and the project documents Docker and a Kubernetes Operator. Databrain also runs as a managed cloud service. More on the trade-offs in our guide to self-hosted embedded analytics.
Yes. Databrain includes white-labeling and per-customer themes on every plan. Superset 6.0 and later supports Ant Design token themes, custom fonts and ECharts overrides, assigned per dashboard, and embedded dashboards take a light, dark or system theme mode. Compare both against your design system before you decide.
Five published Databrain customers went live in 1 to 4 weeks (Freightify 1, Spendflo 2, EpochOS 2, BerryBox 3, SpotDraft 4). Spendflo replaced in-house analytics in 2 weeks and Freightify replaced Metabase in 1, according to their published case studies. No published customer has switched from Superset, so treat this as Databrain's published range, not a Superset migration benchmark.
We check every Apache Superset claim against Superset's own documentation and GitHub README, and every Preset claim against Preset's pricing page, and we date the check: every Superset and Preset source below was opened on 22 October 2026. Superset's docs site currently labels these pages as the Next version, so where the docs tie a feature to a release (theming to 6.0, the MCP server to 5.0) the page says so. Check your version's docs before you build.
Databrain claims come from our published pricing page, Trust Center, product documentation and customer case studies. Superset is software, not a priced service, so we do not state a Superset price, a Superset implementation time or a total cost of ownership; neither the project nor Preset publishes one. Where we found no documentation of a capability, the cell says so, not that it is impossible. We compared Preset only on its pricing page and its embedded add-on, and not on its security certifications, support terms, performance or scale.
Where Superset is stronger, we say so. If you find something here that is out of date or wrong, tell us and we will fix it.
Thirty minutes. Bring your architecture and your warehouse, and we will show a white-labeled dashboard running against your schema and tell you plainly if you are a bad fit.