Winner of the Embedded Analytics Solution of the Year at the Data Breakthrough Awards 2026
A semantic model is a set of definitions that gives tables business meaning, so a metric is calculated the same way wherever it's queried. Power BI, dbt and database theory use the term for versions of that idea; customer-facing models must also carry tenant access.
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Embedded reporting delivers reports inside or from your product: answers to questions that were asked in advance. It overlaps with embedded analytics but differs by purpose, and its scheduled and exported forms need tenant access enforced below the presentation layer.
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Power BI is sold three ways: Pro ($14) or PPU ($24) per user/month, Fabric F SKUs and Embedded A SKUs per hour, checked 23 September 2026. Under app-owns-data your customers need no licence on any SKU, but their usage and concurrency still size the capacity.
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Power BI row-level security filters which rows each viewer sees: define roles and filter rules in Desktop, assign members in the Service, and test. When viewers are your customers, your application supplies the identity in the embed token, so test that path with real tokens.
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Power BI embedded analytics names three separate things: the Azure A SKU resource, Fabric capacity, and two embedding scenarios. Once you split them, the real decision is how much of the governed runtime your team agrees to own.
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Customer-facing analytics is analytics you ship to paying customers inside your product, under your own service-level agreement. It commits you to four layers: query performance, governed metrics and permissions, an embedded runtime, and bounded self-serve.
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Embedded dashboards name two different jobs. In your own internal tool it's a UI task. In a product customers pay for it's a multi-tenant production system whose hard parts sit server-side.
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CLI dashboards mean two different things to engineers. Learn the difference, what AI agents can scaffold from a CLI, and what breaks before you can ship
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AI-generated dashboards: AI tools can generate a working dashboard in minutes. Shipping it safely inside a customer-facing product is a different problem.
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Snowflake + dashboards as code is powerful — but production-ready customer-facing analytics requires tenant enforcement, RLS, environment promotion, rollback
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When an AI agent queries your data instead of a human, the semantic layer becomes governed infrastructure.
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AI predictive analytics forecasts outcomes from historical data. Learn how it works end-to-end, which algorithms to use when, and the governed delivery layer
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Taking an AI agent from prototype to production fails not because the model is bad, but because the data layer was never built for real customers.
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Not all dashboard builders are equal. Learn the real difference between internal BI tools and customer-facing analytics, what breaks at scale, and how AI
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An honest map of the top agentic analytics vendors using generative AI in 2026: general BI platforms, AI-native analyst tools, and embedded analytics.
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AI dashboard generators collapse hours of chart-building into seconds. But generated is not production-ready.
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AI tools can scaffold a dashboard fast—but customer-facing analytics still needs multi-tenant security, trusted metrics, and auditability.
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Agentic BI lets AI agents autonomously query data and build dashboards. How it works, where it breaks, and what production-ready actually requires.
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AI agent observability goes beyond model traces. What to instrument across the agent stack, from tool calls to customer outputs, and why governance matters.
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AI analytics for security means three different things. This guide untangles them and what governed, auditable analytics needs inside a product.
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Dashboards as code brings version control, CI/CD, and code review to analytics. What it solves, what it misses, and what production-grade analytics needs.
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Agentic analytics uses AI agents that monitor enterprise data, plan multi-step analysis, query governed sources and deliver narrative insights or actions. It speeds decisions, but multi-tenant SaaS products are safer running it on a governed layer that enforces tenant scope beneath the agent.
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A technical comparison of Embeddable and Preset for embedded analytics, focused on SaaS use cases, performance, and pricing.
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Learn why most self-serve analytics implementations fail in multi-tenant environments, and how Embeddable's data model architecture makes security identical for static and self-serve dashboards—no special cases, just scalable row-level security.
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Most teams think picking a charting library is the hard part. It isn't. The real work is everything around the chart. Here's why we built Remarkable.
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Embeddable vs Omni: a clear comparison for SaaS teams evaluating embedded analytics, covering UX control, performance, multi-tenancy, and pricing.
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A technical comparison of Embeddable vs Yellowfin for embedded analytics, covering architecture, customization, security, and SaaS scalability.
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Many customers of SaaS products request custom report features, but what they are actually asking for is the ability to explore their own data. Self-serve analytics enable this without needing your team to build and maintain endless bespoke dashboards. Let’s explore who self-serve dashboards are actually for, where common approaches break down, and where custom solutions make sense.
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While building the Custom Canvas feature, we encountered an interesting architectural decision about handling states across large multi-tenant user bases, where we had to consider concurrent state management.
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