Embedding analytics directly into a software product makes data more accessible and actionable. Instead of exporting data to a separate business intelligence (BI) system, embedded analytics integrates visualizations, reporting and exploration into the application’s own interface. This reduces friction and improves adoption because users get insights exactly where they work. Surveys show that over 60% of product professionals who embed analytics see higher user engagement and 57% report revenue gains, while standalone dashboards often suffer from dashboard fatigue with adoption rates hovering around 25%. If you are new to embedded analytics, check out our primer on embedded analytics vs. traditional BI and our article on why SaaS vendors are embedding analytics.
Below are thirteen leading embedded analytics solutions. The first entry is MageMetrics, our AI‑driven platform. For each competitor we include a brief description and a relative link to its comparison page so that you can explore differences in more detail. We also reference relevant knowledge‑base articles where appropriate.
1. MageMetrics – AI‑Driven Embedded Analytics
MageMetrics was designed from day one for customer‑facing analytics. It delivers in‑context analytics that surface insights within a user’s workflow rather than forcing them into a separate dashboard. Users can ask questions in plain English and get answers through a conversational interface, and the platform auto‑maps your database into a semantic layer so that you can embed live analytics in under 30 minutes. Teams benefit from a white‑label experience and flexible deployment options including cloud‑hosted, API‑driven or bring‑your‑own‑cloud. MageMetrics also includes usage intelligence to surface adoption patterns and identify upsell opportunities. Learn more about typical use cases in our use‑case guide.
Embedded‑first solution – Architected for seamless integration into existing software workflows.
In‑context & conversational analytics – Insights and natural‑language questions delivered within the product.
Auto‑mapping semantic layer – Automatically discovers database relationships and shortens time‑to‑value to around 30 minutes.
White‑label & flexible deployment – Match your brand and deploy via cloud, components or your own infrastructure.
Usage intelligence – Built‑in logging and metrics help correlate analytics usage with churn reduction and revenue lift.
For guidance on measuring success with embedded analytics, see our article on key metrics to track.
2. Power BI Embedded
Power BI Embedded is Microsoft’s analytics service for software vendors. It provides robust dashboards and reporting and integrates deeply with the Azure ecosystem. Power BI is a good fit if your organization is committed to a single‑vendor strategy with Microsoft/Azure. However, it was designed as a general BI platform rather than an embedded‑first solution. Users often have to switch contexts between your application and Power BI dashboards, and conversational analytics or in‑context insights are not native features.
Strengths: rich visualization library, strong data modeling and governance, tight integration with Office 365 and Azure.
Limitations: dashboards reside outside the primary workflow, and self‑service embedding typically requires extra development and licensing.
Consider Power BI if you want to stay within the Microsoft stack and are comfortable with dashboard‑centric analytics.
3. Tableau Embedded
Tableau Embedded (now part of Salesforce) is known for its elegant dashboards and interactive visualizations. It is an excellent choice for teams already deeply invested in the Salesforce ecosystem. Tableau’s strengths lie in its visual exploration capabilities and broad community support. As with other traditional BI tools, the emphasis is on dashboard creation rather than delivering insights directly in the workflow. Integrating Tableau into a SaaS product often requires context switching and additional integration work.
Strengths: powerful visuals, extensive community and marketplace, strong data connectors.
Limitations: not built for embedded‑first experiences; context switching can impact adoption, and licensing costs can add up.
Consider Tableau if you are committed to Salesforce’s platform and need advanced dashboarding.
4. Looker Embedded
Looker Embedded, part of Google Cloud, uses a LookML data model and excels at creating governed data layers. If you already maintain a LookML model on Google Cloud and rely on dashboards for internal BI, Looker may make sense. It offers strong data modeling and an API for custom integrations. However, because it focuses on centralized dashboards, it lacks native in‑context analytics or conversational capabilities and may require significant development to embed into customer workflows.
Strengths: LookML modeling, strong governance and security, integration with BigQuery.
Limitations: dashboard‑centric; lacks embedded‑first features and natural‑language interfaces.
Consider Looker if you already have a LookML model and are primarily focused on dashboards.
5. ThoughtSpot Embedded
ThoughtSpot Embedded is known for its search‑driven analytics and natural‑language query interface. It enables business users to explore data through keywords without writing SQL. However, it is oriented toward internal BI; insights are delivered in separate dashboards rather than being embedded in workflow. The platform also requires significant modeling effort and does not provide a semantic layer that auto‑maps your data. ThoughtSpot makes sense when your main focus is internal BI and you see conversational or embedded analytics as nice‑to‑have features.
Strengths: search‑driven analytics and natural‑language query interface.
Limitations: separate dashboards and heavy data modeling; lacks in‑context insights and rapid embedding.
Consider ThoughtSpot if your primary goal is internal BI and not customer‑facing analytics.
6. Sisense
Sisense offers a full‑stack BI platform that combines data preparation, modeling and visualization. The company positions itself as an end‑to‑end analytics solution for internal business intelligence. It provides robust data connectors, caching and the ability to embed dashboards. Yet it was designed for internal BI use cases, so conversational and embedded analytics capabilities are limited. The comparison page recommends Sisense when your main focus is internal BI and conversational or embedded analytics are nice‑to‑have features.
Strengths: comprehensive BI platform with strong data modeling and prebuilt connectors.
Limitations: dashboard‑centric; limited in‑context analytics and no built‑in conversational layer.
Consider Sisense if you need an end‑to‑end BI stack for internal analytics.
7. Preset Embedded
Preset Embedded is a hosted version of the open‑source Apache Superset project. It allows developers to create dashboards quickly and deploy them in the cloud. Preset is suitable for teams that want an open‑source BI tool but do not want to manage infrastructure. Like Sisense, Preset is focused on internal BI: its comparison page suggests choosing Preset when your main focus is internal BI and conversational or embedded analytics are optional.
Strengths: open‑source origins, quick dashboard creation, lower cost.
Limitations: dashboards are separate from the application; limited personalization and self‑service for end users.
Consider Preset if you need a managed Superset instance for internal dashboards.
8. Omni Embedded
Omni Embedded is a modern BI platform built by former Looker engineers. It offers cloud‑native data modeling and dashboarding. Omni is primarily used for internal analytics and may appeal to teams that want a modern alternative to Looker or Mode. According to the comparison page, it is best suited for BI projects where conversational and embedded analytics are secondary to internal dashboards.
Strengths: modern interface, easy modeling and dashboards, good data governance.
Limitations: not an embedded‑first platform; lacks conversational analytics and in‑context insights.
Consider Omni if your main focus is internal BI and you are not prioritizing embedded analytics.
9. Embeddable
Embeddable (formerly Lightdash) provides APIs and components to embed charts and dashboards into your application. It is designed primarily for teams that need prebuilt dashboards and straightforward embedding. Embeddable lacks conversational analytics and automated data mapping, and the comparison page suggests it is best for teams that only want prebuilt dashboards while conversational and embedded analytics are nice‑to‑have features.
Strengths: easy embedding of dashboards and charts; developer‑friendly APIs.
Limitations: no semantic layer or AI integration; limited customization and no conversational capabilities.
Consider Embeddable if you only need prebuilt dashboards and do not require in‑context or conversational analytics.
10. Explo
Explo is an embedded analytics toolkit that allows developers to create dashboards and embed them into SaaS applications. Like Embeddable, it focuses on prebuilt dashboards and charts. According to the comparison page, Explo is appropriate when you only need prebuilt dashboards and consider conversational and in‑context analytics optional. It offers a simple API and interactive dashboard builder, but lacks the automated semantic layer and natural‑language interface found in MageMetrics.
Strengths: developer‑friendly toolkit with ready‑made dashboards; easy embedding.
Limitations: dashboard‑centric; no AI or conversation features; limited self‑service analytics.
Consider Explo if you want quick dashboards without advanced in‑workflow analytics.
11. Luzmo
Luzmo (formerly Cumul.io) offers embedded dashboards and basic analytics components. Its strength lies in quick visualization creation for SaaS vendors. However, the platform focuses on prebuilt dashboards, and its comparison page notes that it is best suited for teams that prioritize dashboards while conversational and embedded analytics are optional. Luzmo does not provide a semantic layer or natural‑language querying.
Strengths: rapid dashboard creation and simple embedding.
Limitations: limited customization; no conversational or automated data mapping.
Consider Luzmo if you only need dashboards and are not focused on context‑aware analytics.
12. Inventive
Inventive takes a unique approach by enabling teams to build custom AI analysts for each customer. This can be attractive if you want to offer a dedicated AI agent per client. According to the comparison page, Inventive makes sense when you explicitly want to build a custom AI analyst for each customer. The trade‑off is that Inventive focuses less on prebuilt analytics components and more on bespoke AI; embedding and conversational features may require additional development.
Strengths: ability to create bespoke AI analysts for each customer; flexible API.
Limitations: requires more engineering investment and lacks ready‑made dashboards or semantic layer.
Consider Inventive if you want to build custom AI analysts for each customer.
13. Upsolve
Upsolve is a lightweight analytics tool aimed at small teams. It offers simple prebuilt dashboards but does not support self‑service analytics or in‑workflow insights. The comparison page notes that Upsolve is suitable when you are a small team only interested in prebuilt dashboards and not self‑service analytics. Because it lacks a semantic layer or AI capabilities, customization and scaling can be challenging.
Strengths: quick setup and basic dashboards for small teams.
Limitations: minimal customization; no conversational analytics; limited scalability.
Consider Upsolve if you are a small team that only needs prebuilt dashboards.
Choosing the Right Embedded Analytics Solution
When evaluating embedded analytics vendors, consider how quickly you need to go live and whether the platform provides a semantic layer that auto‑maps your data. Building analytics in‑house can take months and often faces delays, whereas purpose‑built platforms like MageMetrics let you ship contextual insights in under 30 minutes. You should also think about your users: do they prefer self‑service, conversational interfaces and insights delivered directly in their workflow? Knowledge‑base resources such as how embedded analytics reduces dashboard fatigue and metrics to track adoption and business impact can help you make an informed decision.
Ultimately, your choice of embedded analytics solution should align with your product strategy. If you want to delight users with in‑context insights, natural‑language questions and rapid time‑to‑value, MageMetrics offers an AI‑driven platform built specifically for customer‑facing analytics. For teams focused on internal BI or simple dashboards, competitors like Power BI, Tableau or Sisense may suffice. Use the links above to explore each option in more depth.
