2026 Embedded Analytics Solution of the Year (Data Breakthrough Awards) & G2 High Performer
A business intelligence platform is software that connects to data, models it into governed metrics, and delivers dashboards and reports under access rules. For software teams, the first choice isn't the vendor but the audience: your own staff or your customers.
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Domo doesn't publish a price. It sells consumption credits with unlimited users and a custom quote, so your bill follows what Domo stores and runs for you, plus a daily charge per active End Customer Account if you embed with Domo Everywhere.
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Power BI competitors aren't one category. Most serve one of three jobs: internal business intelligence (BI), AI and natural-language analytics, or customer-facing analytics inside your product, so name the job and your reason for leaving before you shortlist. Sometimes the answer is to stay.
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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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Tableau lists Standard from $15 per user/month (the Viewer seat), Enterprise from $35 (Viewer) and Tableau Next from $40, all billed annually (tableau.com/pricing, checked 23 September 2026). That price list gives a defensible number for internal teams and no number at all for customer viewers.
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Domo sells two products, so "Domo alternatives" has two answers. If you're replacing internal BI, look at warehouse-native tools like Sigma Computing or Omni. If you're replacing Domo Everywhere, you need an embedded analytics platform instead.
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Choose Power BI for a Microsoft estate where per-seat cost matters, or Tableau for analyst-led exploration. Both are built first for internal BI: if the dashboards go to your customers inside your product, that is an architecture and cost-curve decision, not a BI tool choice.
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Find out how important it is to link metric layers to analytics. Create an SSOT that improves accuracy, consistency, and data-driven decisions.
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Learn why some dashboards load slow and how to achieve performant at scale for your embedded analytics, with the use of pre‑aggregations via Embeddable + Cube.
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We share our experience with a popular AI development tool, Lovable, and how it worked for our internal use.
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As OpenAI integrates Rockset into its portfolio, existing Rockset customers are being transitioned off the platform and will have to find an alternative.
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From Netflix to Amazon, a lot of the apps we use every day are built on databases. But data warehouses and data lakes also have their benefits, particularly if you need advanced analytics and reporting. In this article, we’ll dig into the pros, cons, and best use cases of databases, data warehouses, and data lakes.
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In this piece, we unravel the concept of headless BI, exploring its unique benefits and explaining how it stands apart from a semantic layer. We also explore how it’s beneficial for your embedded analytics project (if you want to achieve consistent results).
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If you want your analytics experience to load in real-time, you need a database that supports low latency and high concurrency–among other features. In this article, we explore the differences between Rockset, ClickHouse, Apache Druid, and Apache Pinot for you to choose the best one for your needs.
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