Compare / Embeddable

Draxlr vs Embeddable for embedded analytics

Both put white labeled analytics in your product. Draxlr skips the components and data models, and gives every customer a full analytics workspace.

No React components or Cube models to write
A full analytics workspace for each customer
AI generally available, no LLM key to connect
Published pricing and a 7-day free trial

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4.9/5 on G2

Short Answer

Is Draxlr a good Embeddable alternative?

Yes. Draxlr is an embedded analytics platform for SQL databases and a strong Embeddable alternative. Both put white labeled, multi-tenant analytics inside your product on a flat subscription. The difference is how much your team builds before a customer sees a chart. Embeddable is a developer toolkit, so your engineers write React chart components and Cube data models in your own repository and push them to Embeddable before dashboards can be assembled. Draxlr works on your tables as soon as the database is connected. Each customer gets a full analytics workspace with saved queries, a no-code query builder, a SQL editor and an AI assistant that is available today, and row-level security is a permission you set up in Draxlr.

Setup. Connect a SQL database and build dashboards on its tables straight away, with no components project or data models to write first.
Self-service. Customers build reports with a no-code query builder, SQL or AI and save them as their own queries, instead of picking from chart templates you prepare.
AI. The assistant is generally available inside the embed and credits come with the plan, so there is no LLM provider key to connect.
Reports. Customers download dashboards as PDF, Excel or CSV from the embed, and your team schedules email reports and alerts without code.
Evaluation. Published pricing from $75 a month and a 7-day free trial on the Power plan, with no credit card and no scoping call.

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At a Glance

How Do Draxlr and Embeddable Differ?

The eight rows most evaluations come down to. Pricing and trial terms sit in their own table further down.

Comparison	Draxlr	Embeddable
Work before the first customer chart	Connect a database and build on its tables	React components and Cube data models in your repository
What customers can build	Their own queries and dashboards, with a query builder, SQL and AI	Dashboards from the datasets and chart templates you enable
AI for customers	Generally available, credits included	Prototype, on your own LLM provider key
Tenant isolation	A row-level security permission configured in Draxlr	A security context in the token, applied by your data models
Dashboard PDF export	A download button in the embed	A beta export API your team calls from code
Where it runs	Hosted by Draxlr, or self-hosted on Enterprise	Embeddable's cloud in the US or EU, with your own Cube optional
Custom chart components	Built-in chart types, themed to your brand	Any React component your team writes
Data sources	14 SQL databases, plus Airtable, Excel and CSV	14 listed, including Elasticsearch and Trino, more via Cube

Embeddable details come from its own website, pricing page and developer documentation. Last verified 10 November 2026. Sources Embeddable pricing, Embeddable development docs, Embeddable data modeling, Embeddable Custom Canvas, Embeddable AI Chatbot, Embeddable dashboard export, Embeddable Tokens API, Embeddable data sources, Embeddable security, Draxlr pricing, Draxlr full app embedding, Draxlr dashboard embedding, Draxlr row-level security.

Embedded Analytics

What Draxlr and Embeddable each are

On paper the two overlap. Each embeds white labeled dashboards, keeps every customer to their own rows, lets customers build dashboards of their own and charges a flat subscription with unlimited end users. What separates them is where the work sits.

Draxlr is the finished product. It queries your SQL database in place, row-level security is a permission you set in Draxlr, and full app embedding gives each customer saved queries, a no-code query builder, a SQL editor and an AI assistant beside the dashboards you share.

Embeddable is a developer toolkit. Your engineers write React chart components and Cube data models in your own repository and push them to Embeddable, dashboards are assembled in its builder or as YAML, and a web component renders them in your app. It suits teams that want analytics to be part of their own codebase, down to every chart.

Key Differences

Where Draxlr and Embeddable Split

Embeddable is a capable toolkit for teams that want analytics built as code. These are the points where teams find a different fit, and what Draxlr does about each.

No components project to start

Embeddable begins in your codebase. Its development docs have your engineers keep React chart components and data models in your own repository and push them to Embeddable from the command line, and those components stay code your team reviews and redeploys. Draxlr ships the charts, filters and drill-downs ready to use and themes them to your brand in the embed builder, so the first dashboard is a configuration task rather than a frontend project.

Your tables, not a semantic layer

Every Embeddable chart reads from a Cube data model, so each metric, dimension and join is defined, tested and pushed before it can reach a dashboard, and again when your schema changes. Draxlr queries your tables as they are. Add a column and it is already available to your team, to the AI assistant and to customers exploring on their own.

A workspace, not a template picker

Embeddable's Custom Canvas lets customers build dashboards from the datasets and chart templates you enable, and its docs describe no way to write a query or keep one. Draxlr's full app embedding gives each customer a workspace where they build queries with a no-code builder, SQL or AI, save them and turn them into dashboards beside the ones you share. You choose which of those tools each embed exposes.

AI customers can use today

Embeddable's AI Chatbot docs say the feature "is in Prototype and will be released very soon", and it runs on an LLM provider key you supply, so that provider bills the usage. Draxlr AI is generally available inside the embed. It writes the SQL for a question, shows the query it ran and lets customers edit it, with row-level security on every answer. Power includes 300 credits a month, 200 more cost $10, and the query builder and SQL editor keep working if credits run out.

Reports that leave the embed on their own

In Embeddable, customers download single charts as CSV, Excel or PNG. A whole dashboard becomes a PDF only through an export API that is still in beta, and its docs describe no scheduler or alerts. Draxlr customers download dashboards as PDF, Excel or CSV from the embed, and your team emails reports on a daily or weekly schedule from Premium up, sends Slack and email alerts when data changes and turns saved queries into API endpoints.

A price you can read before the first call

Embeddable's pricing page describes a flat subscription priced on project scope after a scoping conversation, with no figures shown and AI billed separately by your LLM provider. Draxlr publishes every plan with its database, user and AI limits and never charges for the customers who view your dashboards. The embedded analytics pricing comparison sets those plans against the wider market.

Feature by Feature

Draxlr vs Embeddable, Feature by Feature

The two overlap on embedding basics. Draxlr pulls ahead on setup, customer self-service, AI and automation, and Embeddable on custom components, analytics as code and data source breadth.

Feature	Draxlr	Embeddable
Embed a single dashboard	Yes	Yes
Build charts without writing a data model	Yes	No, charts query Cube models
Customers build and save their own dashboards	Yes	Yes, from your chart templates
Customers save their own queries	Yes	Not described
No-code query builder for customers	Yes	Measures and dimensions per chart template
SQL editor for customers	Yes	Not described
AI assistant inside the embed	Yes, generally available	Prototype
AI works without your own LLM account	Yes	No
Row-level security for each customer	Yes, configured as a permission	Yes, written into your data models
Dashboards and models versioned as code	No, built in the Draxlr app	Yes, with development, staging and production versions
Custom React chart components	No	Yes
White labeled branding in the embed	Yes	Yes
Dashboard PDF download for customers	Yes	Through a beta API you call
Scheduled email reports	Yes, Premium and above	Not described
Slack and email data alerts	Yes	Not described
Saved queries as API endpoints	Yes	Not described
Self-hosted deployment	Yes, on Enterprise	Cube data layer only
Elasticsearch, Athena and Trino sources	No	Yes

Scored from each vendor's public pages and developer documentation. Not described means Embeddable's pages do not mention it. Last verified 10 November 2026. Sources Embeddable pricing, Embeddable development docs, Embeddable data modeling, Embeddable Custom Canvas, Embeddable AI Chatbot, Embeddable dashboard export, Embeddable Tokens API, Embeddable data sources, Embeddable security, Draxlr pricing, Draxlr full app embedding, Draxlr dashboard embedding, Draxlr row-level security.

Integration

How Each One Gets Into Your Product

The biggest practical difference between the two is how much code sits between your database and your customer.

How Draxlr is embedded

Your team connects a SQL database with read-only access and builds dashboards in Draxlr with the query builder, SQL or AI. An embed builder sets appearance, exports and allowed domains against a live preview, plus filters when you embed a single dashboard. For full app embedding it also picks the tools customers get, the dashboards every customer sees and the row-level security group that keeps each one to their own rows. Your backend calls Draxlr's embed URL API with your API key and secret, Draxlr returns an embed URL, and an HTML snippet renders it in your app. Single dashboards can also go in through Draxlr's React and Vue packages on npm. Draxlr runs the platform and ships every update.

How Embeddable is embedded

Your engineers write Cube data models for the tables you want to chart and React chart components in your own repository, then push both to Embeddable with its command line scripts. Dashboards are assembled in Embeddable's builder or written as YAML files, and saved versions are tagged development, staging or production. At runtime your server requests a token from Embeddable's Tokens API, passing a security context your data models use to filter each customer's rows, and the em-beddable web component renders the dashboard in a plain HTML, React, Angular or Vue app.

Connectors

Live on the SQL database you already run

Draxlr connects to PostgreSQL, MySQL, MariaDB, PlanetScale, CockroachDB, YugabyteDB, Microsoft SQL Server, Redshift, BigQuery, Supabase, Neon, ClickHouse, Databricks and Snowflake with read-only access.

Pricing Snapshot

What Each One Costs

Both charge a flat subscription with unlimited end users. Draxlr publishes its plans and includes AI, while Embeddable quotes on project scope and runs AI on your own LLM account.

Pricing	Draxlr	Embeddable
Starting price	$75 / month with embedding	Quote after a scoping call
Pricing model	Flat rate per plan	Flat monthly subscription, priced by project
End users	Unlimited on every plan	Unlimited usage
AI usage	300 credits a month on Power, 200 more for $10	Billed by the LLM provider you connect
Published pricing	Every plan	None, quote by request
Free trial	7 days on Power, no credit card	Supported proof of concept after a call

Embeddable's subscription covers the platform. AI chatbot usage is billed to your own LLM provider account. Last verified 10 November 2026. Sources Embeddable pricing, Embeddable development docs, Embeddable data modeling, Embeddable Custom Canvas, Embeddable AI Chatbot, Embeddable dashboard export, Embeddable Tokens API, Embeddable data sources, Embeddable security, Draxlr pricing, Draxlr full app embedding, Draxlr dashboard embedding, Draxlr row-level security.

Draxlr Pricing

What Draxlr Costs

Premium covers white labeled dashboard embedding, Power adds the full app embedding and row-level security an Embeddable evaluation usually turns on, and Enterprise brings self-hosting, SSO and audit logs.

Premium

$75/ month

or $600 a year

White labeled dashboard embedding for teams putting analytics in front of customers.

Embed with an HTML snippet or React and Vue packages
White labeling included
Filters, drill-downs and PDF, Excel or CSV exports
Unlimited external viewers
2 databases, 10 internal users

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Power

Best fit for Embeddable teams

$250/ month

or $2,000 a year

Full app embedding with row-level security, the multi-tenant self-service an Embeddable build would otherwise take.

Everything in Premium
Full app embedding for each customer
Row-level security at table and row level
Customer-owned dashboards and saved queries
5 databases, 30 internal users

Start free trial

Enterprise

$500/ month

or $3,800 a year

For teams that need analytics inside their own network, with governance to match.

Everything in Power
Dedicated server or self-hosting
SSO and audit logs
Custom databases, users and AI credits
SLA support

Every plan includes unlimited external viewers. See the full plan comparison for database, user and AI credit limits, or the Enterprise page for governance and deployment details.

Decision Guide

Which One Should You Pick?

Both handle multi-tenant embedding properly, so the choice rests on how much your team wants to build and how far customers should go on their own. When Embeddable is one of several names on your list, the roundup of Embeddable alternatives scores Draxlr beside Luzmo, Qrvey, Reveal, Bold BI, Sisense and GoodData on setup work, self-service, AI and price.

Choose Draxlr if

You want the first customer dashboard live without a components project or data models to write
Each customer should get their own analytics workspace, with a no-code query builder, SQL editor, saved queries and AI
Customer-facing AI should be generally available now, with no LLM provider key to manage
Customers need dashboard PDFs, and your team needs scheduled reports, alerts and API endpoints
Data may have to stay in your own network, with self-hosting on Enterprise
You want to see pricing and start building before any sales conversation

Choose Embeddable if

Every chart should be a React component your frontend team writes and owns
Dashboards and models should live in Git, with development, staging and production versions
Your data team already maintains a Cube semantic layer
Key data lives in Elasticsearch, Athena, Trino, Druid or MotherDuck
AI must run on a model provider you choose and pay for directly
A scoped proof of concept with vendor onboarding suits how you buy

Migration

Moving embedded analytics from Embeddable to Draxlr

Most of the work in a move is retiring code. These are the four steps.

Connect the databases Embeddable reads

Point Draxlr at the SQL databases behind your Embeddable connections, with read-only access. There is no Cube layer to set up in between.

Rebuild the dashboards you ship

The SQL in your Cube models is a ready reference. The no-code query builder covers most charts, SQL takes the rest, and the AI assistant can draft a dashboard from one prompt. The Draxlr team can also build them with you through Professional Services.

Turn the security context into a permission

The tables and row filters your models applied from the token's security context become a row-level security permission. Send each customer's group and attribute values with the embed URL request.

Swap the web component, then retire the repo

Replace the em-beddable web component with Draxlr's HTML snippet, or its React or Vue package. Once the last customer has moved, the components project and data models can be archived.

FAQ

Draxlr and Embeddable, Answered

Setup, data models, security, AI, data sources, hosting, migration and pricing questions from teams weighing the two.

Is Draxlr a good Embeddable alternative?

Yes, especially for teams that want customer-facing analytics without a components project and a semantic layer to maintain. Draxlr is an embedded analytics platform for SQL databases that covers the same core ground as Embeddable, meaning white labeled analytics in your app, row-level security for each customer, dashboards customers can build themselves and a flat subscription with unlimited end users. It goes further on what customers can do, because full app embedding gives each one a workspace with saved queries, a no-code query builder, a SQL editor and an AI assistant that is generally available today. It also asks less of your engineers, since Draxlr works on your tables as soon as the database is connected. Embeddable is the stronger fit when every chart must be your own React component, when dashboards and models should be versioned in your repository, or when your data team already maintains Cube models.

Should I choose Draxlr or Embeddable?

Choose Draxlr if your data sits in a SQL database, you want the first customer dashboard live without writing components or data models, customers should explore data themselves with saved queries, SQL and AI, and you want scheduled reports, alerts and published pricing. Choose Embeddable if your frontend engineers want every chart written as your own React component, you want dashboards and models in Git with development, staging and production versions, you already run a Cube semantic layer, or you need sources such as Elasticsearch, Athena or Trino. Both offer white labeling, multi-tenant security, customer-built dashboards and flat pricing, so the decision usually turns on how much your team wants to build and how far customer self-service should go.

What is the difference between Draxlr and Embeddable?

Embeddable is a developer toolkit for customer-facing analytics. Engineers define React chart components and Cube data models in their own repository and push them to Embeddable, dashboards are built in a no-code builder or written as YAML, and they embed as a web component authorized by a token your server requests. Draxlr is a hosted embedded analytics platform. You connect a SQL database, build dashboards on its tables and configure row-level security in Draxlr, then embed them with a snippet and one backend API call that returns an embed URL. With full app embedding, Draxlr also puts a complete analytics workspace in front of each customer, with saved queries, a query builder, a SQL editor and an AI assistant.

Do I need a data model to use Draxlr like I do with Embeddable?

No. Embeddable charts query Cube data models, so each metric, dimension and join is written in Cube's modeling language and pushed before it can appear on a dashboard, and an AI model builder can draft models from SQL queries. Draxlr queries your database tables directly. Your team and your customers build charts from tables and columns with the no-code query builder, SQL or the AI assistant, and new columns are available as soon as they exist in the database. Dashboard results are cached so dashboards load faster.

How does multi-tenant security work in Draxlr and Embeddable?

In Draxlr, row-level security is a permission you set up in the product. For each database it defines which tables a group can access and which row filters apply. Your backend sends the customer's group and attribute values when it requests the embed URL, and Draxlr applies them to every dashboard, saved query, SQL statement and AI answer that customer sees, on the Power plan and above. In Embeddable, your server passes a security context, any JSON such as a customer id, when it requests a token, and your Cube data models use that context to filter rows, tables or schemas. The approach is flexible, and the filtering logic lives in models your team writes and maintains.

Does Embeddable have AI for customers, and do I need my own LLM key?

Embeddable's AI Chatbot answers plain-language questions with text and live charts and can edit charts on a customer's Custom Canvas. As of November 2026 its docs mark it as a prototype that will be released very soon, with APIs and behaviour that may change. It runs on an LLM provider you connect with your own API key, such as Anthropic, OpenAI, Google, Mistral, Amazon Bedrock or Ollama, so that provider bills the usage. Draxlr's AI assistant is generally available inside the embed and credits come with the plan, 300 a month on Power with 200 more for $10, shared across your organization. Credits cover only AI messages, so the query builder and SQL editor keep working if they run out.

How does Draxlr's pricing compare with Embeddable's?

Draxlr publishes every plan. Dashboard embedding starts on Premium at $75 a month, full app embedding and row-level security come with Power at $250 a month, and Enterprise starts at $500 a month with self-hosting, SSO and audit logs. Every plan bills monthly or annually and includes unlimited external viewers. Embeddable publishes no prices. Its pricing page describes a flat monthly subscription with unlimited usage and every feature included, in Startup, Scale-up and Enterprise packages priced mainly on project scope after a scoping conversation. Budget separately for the LLM provider its AI chatbot runs on.

Which data sources do Draxlr and Embeddable connect to?

Draxlr connects live to PostgreSQL, MySQL, MariaDB, PlanetScale, CockroachDB, YugabyteDB, Microsoft SQL Server, Redshift, BigQuery, Supabase, Neon, ClickHouse, Databricks and Snowflake with read-only access, and also connects Airtable and accepts Excel or CSV uploads. Embeddable's docs list 14 databases, Athena, BigQuery, ClickHouse, Databricks, Druid, Elasticsearch, MotherDuck, SQL Server, Azure Fabric, MySQL and MariaDB, Postgres, Redshift, Snowflake and Trino, and point to Cube's list for more, since Embeddable connects through Cube. If your analytics depends on Elasticsearch, Athena, Trino or Druid, Embeddable covers them and Draxlr does not.

Can I self-host Draxlr or Embeddable?

Draxlr's Enterprise plan self-hosts with Docker on your own cloud or servers, or runs on a dedicated server in the region you choose. Embeddable runs its platform on AWS in a US or EU region. You can run its Cube data layer yourself, with Cube Cloud or self-hosted Cube Core, and its documentation describes no option for hosting the rest of the platform in your own environment.

Can I migrate from Embeddable to Draxlr?

Yes. Connect Draxlr with read-only access to the databases your Embeddable connections use. Rebuild the dashboards you ship with the query builder, SQL or the AI assistant, using the SQL in your Cube models as a reference, or with help from Draxlr's Professional Services team. Move the filters your models apply from the security context into a Draxlr row-level security permission and send each customer's group and attribute values when you request the embed URL. Then replace the em-beddable web component with Draxlr's HTML snippet or its React or Vue package, and retire the components project and data models once the last customer has moved.

Can I try Draxlr without talking to sales?

Yes. Every new account starts with a 7-day free trial with access to the Power plan, with no credit card and no sales call, so you can embed a working dashboard on your own database before you commit. Embeddable describes no self-serve trial on its pricing page. Evaluation starts with a scoping conversation and a supported proof-of-concept phase.

How we compare these tools

Every Embeddable detail on this page comes from Embeddable's own website, its pricing and security pages and its developer documentation at docs.embeddable.com, and every Draxlr figure matches our published plans. Where Embeddable does something Draxlr does not, the tables say so. Where Embeddable's pages are silent we write not described, because silence on a page does not prove a feature is missing. Figures were last checked on 10 November 2026. Embeddable labels its AI Chatbot as a prototype and its dashboard export API as beta, so check the docs for their current status.

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