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Best Self-Service BI Tools: A Comparison

Last updated: December 27, 2024

What is self-service analytics and why is it important?

Are you looking for an self-service analytics BI tool, yet don’t know where to start and what to look for? This document provides a detailed comparison of popular self-service analytics tools, along with key criteria for evaluation.

The self-service analytics tools compared in this document are: Holistics, Looker, Tableau, Power BI, Metabase.

If you find any inaccuracies or have suggestions for improvement, please let us know using this form.

Popular Self-Service BI Tools: Detailed Comparison Table

Dimension
Holistics logo Holistics
Looker logo Looker
Tableau logo Tableau
Power BI logo Power BI
Metabase logo Metabase
Demo Playground
✅ Available
Status: This is a demo WIP status
Not available
✅ Available
✅ Available
Not available
Pricing Structure for Embedded
(Learn more)

Pricing is among the most important factors to consider when evaluating BI tools. A good embedded pricing should be easy to start when doing POC, as well as not be too expensive when you scale up.

Typically embedded BI tools have pricing structure that scales with the number of embed viewers or usage of your application. Common pricing structures are:

  • Seat-based pricing: Pricing by embed viewers
  • Platform pricing: One fixed price (usually quoted and negotiated) for unlimited viewers
  • Usage-based pricing: Pricing by some usage metric like report runs or active sessions
  • Additionally there might be pricing based on feature tiers
  • Some vendors may offer discounted pricing for your POC period.
Pricing Metric
Query runs/Query slots
Select between Query Runs or Concurrent Workers. With unlimited dashboard viewers. (source)
API calls
One production instance, 10 Standard Users, 2 Developer Users, upgrades, up to 500,000 query-based API calls per month, and up to 100,000 administrative API calls per month. (source)
Analytical Impressions
Pay for entitlements of Analytical Impressions. Applies to users with the Viewer role. (source)
Node Type & Node Instances
Pricing based on node types and node instances. To publish content to an embedded capacity, at least one user requires a Power BI Pro license. (source)
Feature-tiered
Full-powered embedding starts from Pro plan ($500/mo). (source)
Public Pricing
Pricing Transparency
Transparent for standard plans. Contact sales for embedded pricing.
Pricing Transparency
Opaque. Requires speaking to sales.
Pricing Transparency
Transparent for standard plans. Contact sales for embedded pricing.
Pricing Transparency
Transparent for embedded pricing.
Pricing Transparency
Transparent for embedded pricing.
Embedding Methods
(Learn more)

Embedding methods affect the customizability of your embedded apps. Typically with higher customization, the tradeoff is higher development effort.

Common embedding methods are:

  • iFrame: Embeds a pre-built dashboard or report into your app using an HTML iframe tag.
  • Web Components: Embeds analytics as reusable custom web components that integrate natively into your app's UI framework.
  • API integration: Uses APIs to fetch and render data dynamically, allowing custom-built dashboards within your app
  • SDK: Provides a software development kit (SDK), often in a specific programming language or framework (e.g., React SDK), to integrate and customize analytics. Each method has its pros and cons. You should evaluate embedding approaches based on your app's architecture, required level of customization, and available development resources.
✅ Supports iFrame embedding
Provide sandbox UI for generating the iFrame backend code (source)
✅ Supports iFrame, API and SDK
Allows developers flexibility but no Web Component features (source)
✅ Supports API embedding
Works best within Tableau ecosystem (source)
✅ Supports iFrame and API embedding
Seamless integration with Microsoft ecosystem (source)
✅ Static and SDK Embedding
Displaying a Metabase URL inside an iframe in your website. Allows developers to embed individual Metabase into React applications (source)
Public Embedding
Embeds a publicly accessible dashboard to websites without backend setup.
✅ Public Embedding
Allow users to create a "shareable link" of a dashboard, and embed it directly to websites. (source)
✅ Public Embedding
Allow users to share data visualizations publicly via embeddable URLs (source)
Permission & User Access Control
(Learn more)

Embedded tools should ensure each customer can only see their own data and prevents unauthorized modifications. This is called multi-tenancy. Most embedded BI tools support the basic version of this capability.

When evaluating, look into permission levels (e.g., column-level access, row-level access, password-protected sharing) and ensure permission settings are intuitive.

✅ Row-Level Access Control and Multi-tenancy
Supports row-level security via JWT tokens and multi-tenant deployments
✅ Dynamic Data Sources
Allow connecting to different data sources (databases) based on different customers (tenants). (source)
✅ Row-Level Access Control and Multi-tenancy
Comprehensive access controls and multi-tenant support
✅ Row-Level Access Control and Multi-tenancy
Row-level security via JWT tokens and multi-tenant deployments.
✅ Row-Level Access Control and Multi-tenancy
Row-level security via JWT tokens and multi-tenant deployments.
✅ Role-based Access Control and Multi-tenancy
Supports row-level security via JWT tokens and multi-tenant deployments.
Look & Feel
(Learn more)

The BI tools should allow developers to maintain a branding consistency, deliver customized dashboards and cater to diverse visualization needs.

When evaluating, look into the level of customization for colors, fonts, layout. Assess the variety of chart types and their customizability.

✅ Custom Visualizations
Supports custom charts via Vega-Lite. (source)
✅ Canvas-based Dashboard
Support canvas-based dashboard that allows more fluid dashboard design. (source)
✅ Custom Visualizations
Available through Looker marketplace. (source)
Custom Layout
Via LookML. Limited to single-visual tiles (source)
✅ Extensive Visualization Options
Well known for providing a rich visualization options. (source)
✅ Custom Visualizations
Supported with Viz Extensions API. (source)
✅ Custom Visualizations
Supported with Power BI visuals SDK, limited native theme customization (source)
Custom Visualizations
Requires external tools or programming languages (e.g., Python with libraries like Seaborn or D3.js). (source)
CSS & Custom Styles
✅ Custom Themes
Allow developers to build and customize dashboard themes. (source)
Custom Themes & CSS Styling
Preview
Allow admin users to build and customize themes. (source)
Custom Themes & CSS Styling
In development (source)
Custom Themes & CSS Styling
Custom themes are available natively; CSS styling requires workarounds. (source)
✅ Custom Themes & CSS Styling
Available natively. (source)
Performance & Data Storage
(Learn more)

Tools should ensures reasonable performance, and should scale well with increasing viewers and data volumes.

There are typically 2 methods that affect report performance:

  • Caching: Embedded vendors load data into their own caching layer to speed up serving of reports.
  • Direct Querying: BI tool querying your database/data warehouse directly.
✅ Query Caching
Stores query results on demand for configurable durations. (source)
✅ Query Optimization
Via Aggregate Awareness. Automatically pick the most optimal aggregated tables per each query for maximal query performance. (source)
✅ Query Caching
Stores query results and leverages persistent derived tables (PDTs) to precompute and cache aggregated or intermediate query results. (source)
✅ Query Optimization
Via Aggregate Awareness. Automatically pick the most optimal aggregated tables per each query for maximal query performance. (source)
✅ Query Caching
Supports loading data into its in-memory caching layer, with periodic sync refreshes. (source)
✅ Query Optimization
Supports pre-aggregated extracts and Level of Detail (LOD) calculations to optimize queries. (source)
✅ Query Caching
Supports storing initial query results locally for semantic models in Import mode. (source)
Query Optimization
Via Power BI user-defined aggregation, requiring users to manually define aggregation rules and mappings. (source)
✅ Direct Querying
Supports querying data directly from the database.
Query Optimization
Via query caching and manual database optimization. (source)
Self-service Report Creation
(Learn more)

Embed viewers should be able to customize and build their own reports based on a predefined set of data dimensions and metrics.

Viewers should also be able to interact, download, and share custom reports with other embed viewers.

When evaluating:

  • Assess the depth and flexibility of self-serve features like filtering, drill-through, grouping, and visualization options.
  • Look for embedded report builders and assess how easily users can build reports without assistance.
✅ Self-Service Data Exploration
Support interactive dashboard exploration with granular filtering, native period comparison, drill-down capabilities. (source)
✅ Alert and Scheduling
Condition-based automated alert. Email/Slack scheduling is available in PDF/CSV format. (source)
Embedded Report Builder
WIP feature.
✅ Self-Service Data Exploration
Supports filtering, and drill-down capabilities for interactive exploration. No native period comparison. (source)
✅ Alert and Scheduling
Available via Looker Scheduler. (source)
Embedded Report Builder
Not available yet.
✅ Self-Service Data Exploration
Supports filtering, and drill-down capabilities for interactive exploration. No native period comparison. (source)
✅ Alert and Scheduling
Allows users to set up automated alerts, schedule extract refreshes and report deliveries. (source)
✅ Embedded Report Builder
Through Embedded Web Authoring feature that enables end-users to create and modify reports within the embedded environment. (source)
✅ Self-Service Data Exploration
Supports filtering, and drill-down capabilities for interactive exploration. (source)
✅ Alert and Scheduling
Allows users to set up automated alerts, schedule extract refreshes and report deliveries. (source)
✅ Embedded Report Builder
Not available yet
✅ Self-Service Data Exploration
Allows self-service exploration via filtering and drilling-down. (source)
✅ Alert and Scheduling
Enables users to configure notifications for specific data conditions, delivering updates via email or Slack at defined intervals. (source)
✅ Embedded Report Builder
Through Interactive Embedding (Pro and Enterprise plans). (source)
Security & Compliance
(Learn more)

Should protect your customers' sensitive data and builds trust by meeting industry security standards.

Common security compliance certificates are: SOC2, HIPAA/BAA (for health tech companies), and GDPR.

Compliance
✅ SOC2, BAA, and GDPR compliant
✅ SOC2, BAA, HIPPA, and GDPR compliant
✅ SOC2, HIPAA, BAA and GDPR compliant
✅ SOC2, BAA, HIPPA, and GDPR compliant
✅ SOC2, CCPA, and GDPR compliant
Server Location
Servers in US (San Francisco), Europe (Germany), and APAC (Singapore). (source)
Multiple locations in US, EU, APAC and Middle East. (source)
Multiple locations in North America, Europe and APAC (source)
Multiple locations across the world based on Azure regions. Default based on the region of signup. (source)
US (East Coast), EU (Frankfurt), LATAM (São Paulo), and APAC (Singapore) (source)
Maintainability
(Learn more)

Should allow the reuse of analytics logic and components across customers to reduce the maintenance burden for developers and product engineers.

Common functionalities to support this are:

  • Semantic layer to allow defining reusable, centralized metrics
  • Ability to define logic using code language (analytics as code)
  • 2-way integration with Git version control
Semantic Modeling Layer
✅ Semantic Modeling Layer
Centralizes business logic in modular, reusable data models, allowing consistent definitions across reports. (source)
✅ LookML Data Modeling
Enables defining centralized, modular data logic that can be reused across reports. (source)
Basic modeling
Some modeling capabilities exist (like calculated fields or joins). (source)
Robust modeling
Robust modeling layer that supports joins, DAX, calculated tables, etc. (source)
Analytics as Code
✅ Code-based definition & querying languages (AMQL)
Designed natively for 'analytics as code' workflow. Define models and dashboards using code, enabling components to be parameterized and reused. (source)
✅ Code-based definition language (LookML)
Enables defining centralized, modular data logic that can be reused across reports. (source)
Not supported
No code-based definition language
Supported via TMDL format
An object model definition syntax for tabular data models at compatibility level 1200 or higher. (source)
Not supported.
Product not designed to support code-based definition natively. Workaround using Serialization. (source)
Version Control
✅ Native Git integration
Support Git version control for both dashboards and models. (source)
✅ Git-based Version Control
Git-based version control for data models, proprietary dashboard versioning. (source)
Limited Version Control
Non-Git based version control. Properitaty dashboard versioning. (source)
No native Git integration
No native Git integration. But new text-based format (TMDL) makes it easy for developers to set up manual integration with Git version control. (source)
No native version control
Proprietary. Allows one-way Git-based version control through Serialization, available starting with the Pro Plan. (source)

Community Discussions

Discover what other practitioners are discussing about this topic.

r/BusinessIntelligence
Posted on Oct 2022
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Best BI tool for Embedded Analytics
What is the best BI for embedded analytics? The answer is probably subjective, but I'm curious to know what tool I'd need to pick if a company is building customer facing embedded dashboards.
MrPeeps28 Oct 2022

This is an interesting topic with no straightforward answer. There are a lot of factors involved with a decision like this:

  • Where does your data live? If you use Google cloud services, you may get better deals/have an easier time using a Google BI product, etc.
  • What OS do you and your company primarily use? If most people use macOS, then PowerBI is not a good option for you.
  • What is your budget?
  • What level of devops and data engineering support will you have?

There are open source tools that work well, but the setup and support might require a lot more engineering on your end. PowerBI is popular but a pain to use on macOS. Tableau/Looker/etc offer user control and embedded functionality built into their software, but the cost of user licenses can get very expensive depending on how many customers you have.

InitiativeOk6728 Sep 2024

You might want to take a look at Holistics.io. It's a cloud-hosted, SQL-first BI platform with strong support for version control (Git) and a software dev workflow (CI/CD, dev/prod environments). The platform is highly programmable, and we offer pixel-perfect dashboards for precise reporting.

We also have usage-based pricing (not user-based) and built-in email/Slack report scheduling. If you're considering alternatives to PBI or Qlik Sense, Holistics could be a solid fit for embedded analytics.

Check out the playground: https://hooli.getholistics.com/

r/BusinessIntelligence
Posted on Nov 2024
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Which embedded analytics?

Hi, we'd like to offer interactive dashboards for our customers. Each project will be quite unique...

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jessillions Nov 2024

Metabase does all the things you list (I work here) www.metabase.com/product/embedded-analytics

Based on your requirements and the approx. number of clients needing access, I'd say <$10k is very realistic.

We just recently added an option to use Metabase with built-in storage, so you can also continue working with your data in excel.

r/BusinessIntelligence
Posted on Feb 2022
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Which is the best embedded analytics tool for the modern data stack?

Hi all, I'm currently researching for an embedded BI tool for my company use case. We need to provide embedded analytics to our customers (OOTB dashboards + self-serving capabilities).

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trafalger Feb 2022

Hello! I'm a domo consultant and I have a client with an extremely similar use case - they're a software company who embeds Domo into their product and gives their customers more reporting using an embedded Domo.

They sound extremely similar, they don't have a giant team but do most of the work themselves but reach out to us when they have questions. Only downside is the data refresh depending on how large/complex your data is, 15 mins is the standard refresh time. Happy to setup a quick phone call if you want to know more!

r/BusinessIntelligence
Posted on Apr 2024
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Qlik for embedded analytics

I searched far and wide but found nothing. I know how most people feel about Qlik, but what about using it for embedded analytics?

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InitiativeOk6728 Sep 2024

Totally get your interest in Qlik for embedded analytics, but if you're open to alternatives, I'd suggest checking out Holistics.io (I work here).

Easy Embedding: You can embed interactive, pixel-perfect dashboards into your application with just a few lines of code. It's designed for a quick setup, usually taking only 30 minutes to an hour.

User Experience: Users can drill down into data and perform ad-hoc analysis, giving them more control and insights. Additionally, our unique canvas layout allows you to create beautiful, customized dashboards that match your application’s look and feel.

Pricing: We offer usage-based pricing, which can be more cost-effective compared to flat fees or consumption-based models, especially as you scale. You get unlimited users and only pay based on consumption.

Check out the playground and experience it for yourself: https://hooli.getholistics.com/

r/BusinessIntelligence
Posted on Oct 2021
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Embedded analytics solution for the modern data stack

tl;dr: Questions: Which providers would you recommend / what is your experience with them and does it make sense to combine the BI tool with the embedded analytics solution?

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nvqh Sep 2024

Probably late to the game, but you can check out Holistics.io (I work here).

Version control with Git. Support software development process (CI/CD, dev prod environment, etc)

Nice visualizations and flexible dashboarding

Entire platform very programmable, you can do programming stuff with it

Cloud-hosted

Usage-based pricing instead of user-based.

Email/Slack report schedules

r/tableau
Posted on Apr 2023
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Tableau embed question

My goal is to embed a tableau visualization I made from my tableau server onto my website and allow anyone who comes to my site to be able to view it. I have a tableau server creator's license and when I try to add the embed now, it works perfectly for me, but for everyone else, it wants a password to be able to view it.

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86AMR Apr 2023

Tableau recently introduced a consumption-based licensing model for embedded analytics that is meant for external-facing use cases. I think this is what you need. If you are looking for something to truly be free then you will need to look at some of the open-source viz tools that are out there.

https://help.tableau.com/current/online/en-us/licenseproductkeys.htm

getdbt.slack.com: #bi-tools-general
Posted on Sep 2024
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A.
hi all! curious to know what embedded analytics solutions others are currently using is it good and would you recommend it? looking to implement an embedded solution for 1,000-2,000 users would really appreciate hearing thoughts/opinions from people who are using the software rather than vendor calls if possible :sweat_smile: thanks!
S. R. Sep 2024

I'm using Metabase for embedded analytics and it's been great. It's easy to set up and has a lot of features that make it a good choice for embedded analytics.

getdbt.slack.com: #bi-tools-general
Posted on Aug 2023
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M.
Are there any BI tools people can recommend with white label embedding at a low cost?
Jack L. Aug 2023

We have tried quite a few tools for this. In production used Tableau but ran into issues: performance, dev complexity (write in desktop, not actual code!) even security bugs (leaking other customer data in filter choices!). Now we do double approach: in parallel implement prototypes with Quicksights, and for real average end-user use dont relay on any ready BI, but build "pixel perfect UX" in code using lower level UX dev (react) components. There are opensource ones, and we have created own inhouse set on top of these. One key learning is that the BI drill-downs and other fancy things are just too complex for embedded use cases in case of beginner and average users. Advanced users anyway dont want any UX, just "can i download my data" so they will use their looker/tableau/dwh stack or whatever.

r/BusinessIntelligence
Posted on Feb 2019
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Looker vs Sisense for embedded analytics?

Hey all, looking for some insight from those with experience of Sisense and Looker specifically in the world of embeddeding & monetizing within an enterprise web application. Here are some key things I’m looking for:

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GeneralDouglasMac Feb 2019

Of the two: Sisense has the most functionality in multi-tenancy, row level security, and embedding. It is a bit dull on it's standard package but function over form there. Another suggestion I would consider (if you're asking) is Tibco Spotfire. Row level security, excellent embedding and server, the visuals are nothing to sneeze at at first but if you enable the D3.JS repository boy are the doors thrown wide open! It has about as capable GIS capabilities as any tool I've yet to toy with. I would NOT suggest DOMO. It has fallen by the wayside in support, feature matching, and costs compared to every other tool out there.