Three engineers, six months
Roughly $250K of engineering capacity that does not go into the product your customers actually bought.
Embedded analytics for product teams.
Bring white-labeled dashboards and governed AI into your product in 1 to 4 weeks.
or start building free
- SOC 2 Type 2
- ISO 27001
- Live in 2 to 4 weeks
- Flat pricing from $999/mo
The dashboard is two sprints. The other five months are the problem.
Charts are the part your team can estimate. Multi-tenancy, row-level security, SSO and caching at scale are the part they cannot.
Static charts, not a real data experience
A two-sprint build ships a chart. It does not ship drill-down, cross-filtering, or the ability for a customer to ask the next question. That is usually the other five months, if it ever gets built at all.
Your customers open Excel instead
When the in-house dashboard cannot answer the next question, your customer exports to a spreadsheet or their own BI tool. Once they are doing real work outside your product, you have lost the reason they log in.
What you're actually shipping
Four capabilities your roadmap gets for free the day you embed instead of build.
Embed instead of build
Connect your warehouse and add analytics to your product without owning the infrastructure behind it.
- Tenant-ready architecture from day one
- Managed caching, query routing and scale
- SSO and embedding already built in
Let customers answer their own questions
Give customers flexible reporting inside the experience, definitions and permissions your team controls.
- Drag-and-drop report building
- SQL and plain-English exploration
- Governed metrics in your product theme
Keep every tenant inside their boundaries
Apply access rules before every query so each customer sees the right data without customer-specific code.
- Tenant-scoped guest tokens
- Row- and column-level policies
- SAML, OIDC and metadata-only storage
Uncover insights without hallucinations
Let customers ask questions in plain English and get answers grounded in the metrics your team has defined.
- Answers traced to your semantic layer
- Context scoped to the asking tenant
- Declines when no trusted definition exists
The build-vs-buy math, in four parts
Each of these is a line item your team stops owning the day you embed instead of build.
- 01
Faster time-to-market
Freightify replaced Metabase in one week, without pulling engineers off the roadmap.
- 02
No backend to maintain.
Caching, uptime, query routing and security patches run under the hood. Your team ships the embed, not the backend.
- 03
New metrics without a sprint.
Change a metric or a layout the day someone asks. Customers can build their own inside the guardrails you set.
- 04
Governance before the query runs.
Every tenant sees only their own data. No custom code, no per-customer filters to maintain.
Three steps between signing and your customers seeing it
Most teams are live in 2 to 4 weeks. The variable is the state of your data model, not our setup time.
Connect your warehouse
Read-only connection. Your data never moves, we never store it.
Define your metrics once
Your semantic layer and tenancy model, set up in days with our team.
Embed and ship
Install the package, add the component and go live white-labeled.
Customer stories
Product teams who stopped waiting on a build
EpochOS
- to production
- 2 weeks
- of engineering saved
- $100K
“We struggled with Power BI due to embedding issues and unpredictable pricing. Switching to Databrain streamlined everything with better customizations, predictable pricing, and AI features that let our mortgage brokers get insights through natural language.”
Evan WadeCo-founder, EpochOS
Spendflo
- saved
- $300K
- to go live
- 2 weeks
“We cut down 6 months of work for our data analysts and saved around $300K, all while keeping a smaller, more efficient team.”
AjayChief Technology Officer, Spendflo
SpotDraft
- saved
- $300K
- of engineering handed back
- 9 months
“Databrain helped us build customer-facing reporting on the SpotDraft platform, saving significant internal engineering resources in the long run.”
Rohith SChief Product Officer, SpotDraft
Questions product leads actually ask
What do product teams use embedded analytics for?
Product teams use embedded analytics to give customers dashboards and reports on their own data inside the product, instead of CSV exports, one-off report emails or support tickets. Common uses are customer-facing dashboards, metrics customers build and save themselves, scheduled email reports sent to each customer and plain-English questions answered as charts. With Databrain these run on your existing warehouse and ship white-labeled, so analytics goes on the roadmap without building the query, tenancy and charting layers. End-user report creation is part of the Enterprise plan.
How much engineering time does Databrain take from our roadmap?
Databrain customers typically go live in 2 to 4 weeks; Freightify went live in 1 week. The engineering work is connecting your warehouse with read-only credentials, mapping the column, schema or database that identifies each tenant, adding a backend endpoint that creates guest tokens, and placing a web component in your app. The variable is your data model: when each customer’s data is already cleanly identifiable, the integration is short.
Can product managers build and change dashboards without engineers?
Yes. In Databrain, product managers and analysts build metrics with drag and drop or custom SQL and arrange them into dashboards in the Databrain app. The embed in your product points at a dashboard by its ID, so changes published in Databrain reach customers without a code release. Freightify’s CPO put it this way: “Anybody in the org was now able to create metrics and share data with their tool.”
Do we need a data warehouse to use Databrain?
No, but your data needs to sit in a database Databrain can query. Databrain connects to warehouses such as Snowflake, BigQuery, Redshift, Databricks and ClickHouse, to databases such as Postgres, MySQL, SQL Server and MongoDB, and to Amazon Athena for data that sits in S3, and it queries those sources live. What matters more than the engine is a clear tenant identifier, meaning a column, schema or database per customer, so each customer’s data can be scoped.
What does Databrain take off our team’s plate after launch?
Databrain Cloud runs the analytics backend for you: query routing to your warehouse, result caching, the chart and dashboard runtime, monitoring and security patching. Your team still owns the data model in your warehouse, the guest-token endpoint and upgrades of the @databrainhq/plugin package in your app, which should stay current with the server. New chart types and builder features arrive through Databrain releases rather than your sprints.
See what your team would not have to build
30 minutes. We connect a warehouse, show you a white-labeled dashboard running against your schema, and tell you honestly whether we fit.
For developers
Want to see the SDK, the API and the token model first?