Gartner® Magic Quadrant™ for Analytics and BI Platforms The Best Cloud Native Embedded BI Tools: How to Pick the Right Platform for Your Business
Summary
This article explores what embedded cloud native BI is, how it differs from traditional tools, and why it's essential for modern applications. It highlights key features to look for, common pitfalls to avoid, real-world use cases across industries, and tips for selecting the right platform. It also explains how cloud native embedded analytics (EA) enables real-time insights directly within a product, streamlining decision-making for users and simplifying development for teams.
What Is Embedded Cloud Native BI?
Embedded cloud native BI is business intelligence that lives inside your applications. It brings data insights directly into user workflows instead of requiring users to switch to a separate tool.
Let’s look at the two parts of the term:
- Embedded means the analytics experience is integrated into your product. It is not a standalone dashboard or an external report. Instead, users can interact with visualizations, metrics, and reports exactly where they work.
- According to Gartner, cloud native refers to something created to fully leverage the core characteristics of cloud computing as a service. In the context of BI, this means the platform is inherently designed for the cloud, scalable, resilient, secure, and flexible by default.
So, a true embedded cloud-native analytics platform is made for developers, product teams, enterprise companies, and modern data use cases. It connects easily with cloud data warehouses, supports flexible deployment, and gives you full control over the user experience.
Embedded Dashboard Example
Embedded BI vs Traditional BI
While traditional BI focuses on internal reporting and decision-making, embedded cloud native BI is built to deliver real-time analytics directly within products. It supports highly customizable, interactive experiences for end users, with faster deployment, deeper integration, and better scalability for modern apps.
The table below highlights the key differences between the two approaches.
| Category | Traditional BI | Embedded Cloud Native BI |
|---|---|---|
| Primary Use Case | Internal reporting and dashboards | Internal reporting, customer-facing, or in-app analytics |
| Customization | Rigid visuals and static layouts | Fully brandable and UI-flexible |
| Integration Level | Sits outside your product | Built into your product's interface |
| Speed of Deployment | Slower to implement and update | Agile and developer-friendly |
| Multi-Tenant Support | Often requires complex workarounds, hard to maintain product versions | Core feature for SaaS or platforms, easy to update |
| User Access | Analysts and internal teams | End users, partners, or customers |
Why Embedded Cloud Native BI Tools Are a Smart Investment
Choosing an embedded cloud native BI solution is not just a technical decision. It is a strategic investment that impacts how your users engage with your product, how quickly you can innovate, and how effectively you compete.
Here’s why more companies are making the switch:
- Self-service and contextual insights: Embedded cloud native business analytics makes it easier for users to answer their own questions without relying on analysts or data exports. Surfacing data directly within the application supports faster decision-making and a more seamless product experience.
- Reduced engineering costs and faster delivery: While some traditional BI tools offer embedded options, they often require heavy customization or workarounds. In contrast, embedded cloud native analytics platforms are built for fast integration, with ready-to-use APIs, SDKs, and visual components. This allows product teams to add analytics faster, with less code and fewer engineering tradeoffs. The result is shorter development cycles, fewer bugs, and more time focused on core product features.
- Competitive advantage built in: Cloud native platforms provide a faster and more seamless way to deliver insights that feel like an integrated part of your product. They scale more easily, adapt to user behavior, and support deeper customization. By embedding cloud native analytics, you turn data into a powerful differentiator that you can personalize, monetize, and continuously improve as your product evolves.
- Enterprise-grade security: Cloud native BI can be deployed within your existing infrastructure or private cloud, ensuring sensitive data never leaves your controlled environment. This is especially critical for organizations in regulated industries or those handling confidential customer information, as it allows them to meet compliance requirements while still benefiting from modern, scalable analytics.
Real-World Use Cases Across Industries
Companies across all industries are using embedded cloud-native analytics to create better user experiences, unlock new value, and deliver insights exactly where they’re needed. These are just a few ways companies are putting cloud native analytics to work:
SaaS
SaaS companies embed white-labeled dashboards inside their applications. Customers can log in and instantly see their own metrics, trends, and KPIs, all styled to match the product’s brand. This seamless experience helps turn analytics into a core part of the product, not just a bonus feature.
Fintech
With embedded cloud native analytics, fintech platforms can deliver live performance data, client reports, and transaction insights directly inside secure portals. This improves trust, saves time, and makes the platform feel smarter and more responsive.
Pharmaceutical
Pharmaceutical companies embed analytics into research, manufacturing, and distribution platforms to monitor clinical trial data, production quality, and supply chain performance. Real-time insights help ensure regulatory compliance, improve operational efficiency, and accelerate the delivery of life-saving medicines.
Healthcare
Hospitals and healthcare providers use embedded cloud native business intelligence to track key metrics such as patient wait times, appointment volumes, and clinical performance. These solutions drive efficiency and support compliance through capabilities tailored to the healthcare industry.
Embedded Healthcare Dashboard Example
Key Features of the Top Embedded Cloud Native Analytics Platforms
Choosing the best embedded cloud native analytics software involves more than checking off features. You need to understand how the platform is built, how it fits into your architecture, and how well it supports your users. This guide to the best embedded analytics platforms offers a detailed comparison. But for now, here is a summary of the key capabilities to look for, followed by a closer look at each one.
| Feature | Why It Matters |
|---|---|
| Fully Cloud Native | Built to scale in modern cloud environments using elastic infrastructure |
| Seamless Embedding Options | Fits smoothly into your product with flexibility in how you display analytics |
| Multi-Tenant Architecture | Keeps customer data securely separated and easy to manage at scale |
| Semantic Data Modeling | Makes data logic reusable, consistent, and easier to maintain |
| Data Connectivity and Performance | Connects directly to cloud data sources and supports fast, responsive dashboards |
| Role-Based Access Control | Controls who can see or do what based on their role |
| White Labeling | Makes analytics match your product’s branding and UI |
| Real-Time or Streaming Data | Supports fast-changing data and time-sensitive decisions |
| Headless BI and API-First Design | Gives developers full control and flexibility through APIs |
| Enterprise Security and Compliance | Protects data and helps meet standards like GDPR, HIPAA, and SOC 2 |
| Embeddable AI | Enables integration of AI-driven insights, such as predictions and recommendations, directly into your product experience |
Fully cloud native
A platform that is fully cloud native is built specifically for cloud environments. It uses modern tools like containers and autoscaling to handle traffic changes without slowing down. This makes deployment easier and performance more reliable as you grow.
Seamless embedding options
Analytics should blend into your product, not feel separate. The best platforms support a range of different embedding options, from simple iframes to more advanced approaches like SDKs and custom components.
Multi-tenant architecture
If your product supports multiple customers, each needs a secure view of their data. Multi-tenant architecture keeps everything isolated without requiring complex setups. It also helps you manage permissions and updates more easily across your customer base.
Workspace Hierarchy in a Multi-tenant Platform
Semantic data modeling
Semantic data modeling lets your team define key business concepts (like revenue or churn) once and reuse them consistently across all reports, dashboards, and tools. This reduces errors and saves time by eliminating redundant logic.
This modeling approach forms the foundation of the semantic layer, which acts as a bridge between raw data and AI systems. By making data understandable and consistent, this abstraction layer is becoming essential for AI-driven insights, prompting a recent Forbes article to call it “AI’s best friend.”
Data connectivity and performance
The platform should connect directly to cloud data warehouses such as Snowflake, BigQuery, or Redshift. Some tools also support federated queries or API-based data sources, which can bring together data from multiple systems without moving it.
For performance, look for support for caching, pre-aggregation, or live querying. These features help keep dashboards fast and responsive, even when working with large volumes of data or real-time updates.
Role-based access control
Not every user should see every piece of data. Role-based access control lets you decide what each person or team can view, explore, or edit based on their role. This keeps data secure and helps users focus on what’s relevant to them.
White labeling
Your analytics should look like your product. White labeling lets you style dashboards and charts to match your branding. This includes colors, fonts, and layouts.
Real-Time or streaming data
Some users need up-to-the-minute information. Whether it’s tracking usage, sales, or delivery routes, a platform that supports real-time or near real-time data can help users react quickly and make better decisions.
Headless BI and API-first design
A headless BI platform separates the back-end analytics engine from the front-end experience. This allows developers to build fully custom interfaces using APIs. You control how data is shown and where it appears in the app.
Enterprise Security and Compliance
Security features are essential if you handle sensitive or regulated data. Look for support for single sign-on, encryption, permission management, and audit logs. Compliance with standards like GDPR, HIPAA, or SOC 2 should be built in, not added later. You should also have the flexibility to deploy within your company’s own infrastructure, such as on-premises servers, private clouds, or other controlled environments, ensuring sensitive information never leaves your secure perimeter.
Embeddable AI
Embeddable AI refers to the integration of machine learning–powered insights directly into your product’s interface. Rather than relying solely on historical data, these tools surface predictions, automated recommendations, or intelligent alerts in context. When combined with cloud native architecture, embeddable AI can scale efficiently, adapt to real-time data, and offer a smarter, more responsive experience for both end users and product teams.
Common Mistakes to Avoid When Selecting Embedded Cloud Native BI Tools
Most teams start the buying process with the right intentions. But even when a platform checks all the boxes on paper, the evaluation process itself can lead to costly missteps. Here are some of the most common process-related pitfalls to watch out for.
- Relying too much on demos and not enough on real product context: Cloud native embedded analytics platforms often shine in polished demos. But performance, responsiveness, and integration can vary once you introduce real-world complexity. You need to see how the tool behaves with your data, your front end, and your user roles, not just a sample dashboard in a sandbox.
- Choosing based on features, not future flexibility: Many teams select tools based on what they need today without considering how their product or data will evolve. The best embedded cloud native BI tools are built to scale with you, so it’s crucial to assess how flexible the architecture, APIs, and data modeling truly are.
- Excluding developers from the evaluation: Embedded cloud native analytics is not just a product decision; it’s a developer experience decision. If engineers are not part of the process, you risk choosing a platform that lacks proper APIs and SDKs, has poor documentation, or can’t be integrated easily into your deployment flow.
- Not involving real end users early on: Embedded analytics only delivers value if people actually use it. That includes customers, internal users, and partners. Choosing a platform without understanding how your users will explore, filter, or interact with data often leads to low adoption, even if the dashboards look good.
- Skipping performance and data integration testing: Cloud native platforms should connect directly to cloud data warehouses like Snowflake, BigQuery, or Redshift. But not all platforms handle live data, caching, or high query loads the same way. Performance issues often show up only when tested under real data volumes and access patterns.
- Assuming all cloud native tools are open and portable: Just because a platform is cloud native does not mean it is easy to migrate or extend. Some rely on proprietary formats, rigid deployment models, or tightly coupled infrastructure. This can lead to vendor lock-in if you're not careful about how data, logic, and dashboards are stored and accessed.
Making Cloud Native Embedded Analytics a Core Part of Your Product Strategy
Cloud native embedded analytics tools aren’t just technical infrastructure; they shape how users experience your product, how teams deliver value, and how your business grows.
The right platform lets you build analytics directly into your application, where it becomes part of the product, not just an add-on. It supports scale, security, and speed without slowing your team down.
GoodData is a cloud native embedded analytics platform built for exactly this. It is scalable, developer-friendly, and designed to fit seamlessly into modern applications. Want to see it in action? Get a demo and explore how embedded analytics can help power your product roadmap.
FAQs About Cloud Native Embedded Analytics Tools
Yes, especially for applications that need integrated, highly secure user-facing analytics. They offer more scalability, better user experience, and are easier to maintain over time.
Yes. Many platforms support responsive design or provide APIs that work with mobile frameworks like React Native.
Cloud native analytics tools are self hosted, meaning you can deploy them anywhere such as on your chosen cloud provider, within your company’s own infrastructure, or in a private cloud. This approach offers maximum control and security because deployment and ongoing maintenance are handled by your own team.
Cloud hosted analytics tools are run and maintained by the vendor on public cloud platforms such as AWS or Azure. Your team does not manage the infrastructure or updates. Instead, you focus on connecting data warehouses, creating dashboards, scaling usage, customizing features, embedding analytics, and provisioning users.
They are designed to scale horizontally and vertically as demand grows. Whether you need to support a handful of users or tens of thousands, you can allocate more computing resources, add nodes, or adjust configurations without downtime, ensuring consistent performance.
Yes. Many platforms offer drag-and-drop dashboards, simple configuration for embeds, and no-code data modeling tools. These make it easier to build and manage analytics without relying heavily on engineering.
