Best Embedded Analytics Platforms (2026): Top 10 Compared

Luzmo is an embedded analytics platform built for software companies that want to put dashboards and AI-driven answers in front of their own customers. The vendor's homepage describes one assistant whose answers can appear inside the customer's product, in Slack, in Claude, in a bot the customer builds, or on WhatsApp. Luzmo states that these answers are computed by its own query engine rather than by an LLM writing free-form SQL, and that each answer is scoped to the end customer's data, respects permissions and can be audited.

The pricing model is the clearest point of difference. Luzmo publishes a single plan starting at €1,995 a month, billed annually. The vendor says nothing is held back for a higher tier: white-labelling, self-service for end users, AI and APIs are included from the start, and internal users are unlimited. Three items are billed. The platform licence is a fixed subscription. AI usage covers governed conversations between end users and Luzmo AI, with 500 included each month and a unit rate fixed in the contract after that. Warp usage covers data acceleration above 100 million included rows, at €0.25 per extra million. Luzmo adds that the usage metric is agreed at signature and stays fixed for the contract term, and that some contracts scale on monthly active users instead.

This structure suits product teams that want to budget for embedded analytics as a platform fee plus a usage line, rather than counting seats. Buyers should model the AI conversation volume their customers are likely to generate, because that rate is set in the contract rather than on the public page.

Embeddable is an embedded analytics product aimed at developers. Its homepage describes native dashboards and conversational self-serve delivered inside the customer's product, with Embeddable handling security, permissions, performance and scale underneath. The vendor highlights four points in its own summary: SOC 2 Type II, flat pricing, white-labelling and no iframes. The last point matters to product teams that want embedded charts to behave like the rest of their application rather than like a separate page loaded in a frame.

The workflow is code-first. The vendor describes building locally and deploying globally, with developer SDKs, granular security and full extensibility listed as included features. End users can explore data through dashboards and through an AI chat that answers questions about their data. Embeddable states that its product is used by teams at companies ranging from 4 to 40,000 employees.

Pricing is a flat monthly subscription with unlimited usage and every feature included. The pricing page says the fee is based mainly on project scope, with larger or more complex deployments scoped separately. It also states that monthly active users, dashboard views and query volume are not the primary pricing drivers. No figures are published. Buyers speak to the sales team for a quote, which the vendor says can usually be given after a short scoping conversation. The page groups customers into three stages. Startups ship a first analytics use case. Scale-ups and mid-market companies need more governance and collaboration across products. Enterprises need SLAs, tailored rollout and compliance support. Embeddable also offers supported onboarding during a proof-of-concept phase, with an account manager and frontend and data engineering help.

The site reports a 2026 Data Breakthrough award for embedded analytics. That is a vendor-reported recognition.

GoodData describes itself as an intelligent embedded analytics platform for adding self-service, AI-assisted analytics to any application. Its embedded analytics page lists three things a product team can embed: single visualisations, whole dashboards, or the GoodData AI Assistant. White-labelling lets the result match the host product's colours, fonts, logos, buttons and tooltips.

Multitenancy is the core of the design. GoodData says analytics are built once and shared across the customer base, with each customer in an isolated workspace. Changes made in the admin environment roll out to all tenants automatically. Role-based access controls manage permissions, and a central dashboard monitors and audits automation activity. The pricing page adds a semantic layer for reusable metrics, a declarative API for all metadata definitions, and a Python SDK for backend management.

Three embedding options are offered. The React SDK is aimed at React applications that need a custom frontend or native embedding. Web Components suit Angular, Vue and other non-React apps. The iframe option embeds a dashboard by pasting an HTML snippet and is suggested for simple integrations or proofs of concept.

There are two plans. Professional, aimed at small and medium companies, is priced as a platform fee plus the number of workspaces, with unlimited users and data. It includes full embedding, multitenancy, branding, SSO, standard support and fully managed SaaS on AWS or Azure. Enterprise uses custom, use-case-based pricing and adds custom agents, Agent Builder, context management, AI governance, an MCP server, SLA support and dedicated customer success. No figures are published for either plan. A 30-day trial is available.

GoodData lists SOC 2 certification and GDPR and ISO 27001 compliance, with data centres in the US, EU and Australia and further regions on demand.

Reveal is an embedded analytics platform from Infragistics, a developer tools company. The vendor positions it as AI-native embedded analytics for SaaS applications and software products that need secure data experiences with predictable cost.

The architecture is SDK-based. Reveal states that analytics and AI are embedded where users work with no iframes, no redirects and no separate tool. Client SDKs support React, Angular, Blazor and Vue, and server SDKs are available for .NET Core, Java and Node. Deployment can be on-premises or in the cloud. The vendor says Reveal runs inside the customer's own application infrastructure and enforces that application's roles, permissions and policies. That design is relevant to teams with strict data residency or security requirements who do not want customer data flowing through a third-party hosted service.

Features listed on the site include customisable dashboards and reports, drill-down and filtering, self-service for end users, role-based access controls and full white-labelling. Conversational analytics lets users ask questions in natural language and receive KPI summaries, anomaly detection and suggested actions. On AI cost, Reveal says the customer chooses the model and owns the billing, with Reveal providing visibility and guardrails.

The pricing page asks buyers to request a personalised quote and describes a flat-rate structure. Its FAQ states that there are no additional embed costs as a customer's own user base grows, that all data connectors are free, that machine learning and dashboard linking carry no extra fee, and that white-labelling is included. A 30-day free trial of the SDK is available. The FAQ also cites an estimate that building analytics in-house can take seven months or more and cost up to $350,000. This is the vendor's own figure.

Yellowfin offers business intelligence for both internal and embedded use, and its embedded analytics page is aimed at software vendors that want to add white-label analytics to their product. The vendor describes three ways to embed. Pre-built dashboards can be plugged into the application, or customers can build their own. Self-service reporting lets end customers run ad hoc analysis with Guided Natural Language Query. Contextual analytics places charts and dashboards directly in application workflows through an API with minimal code.

Several capabilities extend beyond dashboards. Signals automatically analyses customer data and notifies users when something significant changes. Stories and Present combine charts and tables with narrative, which the vendor suggests for regular customer reports and summaries. Custom JavaScript visualisations cover chart types outside the standard set. Yellowfin also offers a high-performance in-memory analytical database that can run on-premises, in the cloud or both.

The commercial model is the main point of interest for ISVs. The pricing page lists three options for software vendors. The aligned utility model prices per unit, with the unit matched to how the vendor sells, for example by site, app, bus or device. The revenue share model prices on the revenue earned from an add-on analytics module. The server core model is a fixed price based on server deployment, such as a four-core installation, and scales only as deployment needs grow. The vendor also lists unlimited compute power, cloud, on-premises or hybrid deployment, no limits on development and test licences, and no restriction on access to functionality across users. No figures are published. Pricing is by quote.

Deployment can be self-managed or run as a fully managed hosted service.

Metabase is an open-source business intelligence tool that also sells embedded analytics for software products. The vendor states it is used by more than 100,000 companies. Its embedded analytics page focuses on speed to a proof of concept: teams connect their data, build visualisations and embed them in their product, with the claim that this is quicker than adopting a charting library.

There are two embedding approaches. Low-code implementation is aimed at teams that want results quickly. The Modular Embedding SDK gives more control over how individual components are integrated and customised inside the host application. Non-engineering staff can build queries and dashboards, including custom click behaviour, in the Metabase interface, which the vendor presents as a way to move dashboard changes from engineering to customer success teams. Multi-tenant security is handled through permissions at organisation, user, table, row and column level, managed with SSO, and permission management is available in the UI.

Metabase is one of the few vendors on this page that publishes full list prices. The Open Source edition is free. Starter costs $100 a month with five users included. Pro costs $575 a month with 10 users included, then $12 per user a month, and is the first plan the pricing page lists with embedded analytics: unlimited embedded charts and dashboards, AI questions for customers, multi-tenant data segregation with row and column security, SSO, usage analytics and white-labelling. Enterprise starts at $20,000 a year with custom pricing, a dedicated success engineer, air-gapped deployment and a one-day support SLA. Yearly billing saves 10%. Optional usage-based charges apply to AI, transforms and storage.

One pricing detail matters for embedding. Metabase states that both the internal team and users of the embeds, accessed through the customer's product, count as users. Products with large numbers of end users should model per-user cost carefully.

Sisense is a long-established analytics vendor that now positions itself mainly around embedding AI analytics in software applications. Its pricing page presents two plans, both focused on connecting data, modelling it and embedding analytics in an app.

The Self-Serve plan is aimed at startups and growing teams. The vendor states it connects to any warehouse, database or cloud app, includes built-in AI for natural-language queries, auto-narratives and an assistant that end customers can use, and supports embedding by iframe or through the Compose SDK with any tech stack.

The Enterprise plan is aimed at enterprises and regulated industries. Sisense lists a native multi-tenant architecture that isolates each customer's data across environments, advanced connectivity including SSH and VPN, and deployment on Sisense SaaS, the buyer's own AWS, Azure or GCP account, or on-premises for data sovereignty needs. It also lists HIPAA readiness, column-level security, SSO and custom security policies, a 99.99% Premium SLA written into the contract, auto-scaling, 30-day backup, the full Sisense Intelligence AI suite and the option to bring an organisation's own LLM. Sisense engineers work with the customer's team on development, data modelling and implementation, supported by a dedicated success manager.

The developer documentation describes two embedding routes. Compose SDK is code-first and modular, built for React, Angular and Vue with TypeScript, and embeds data, charts and individual filters using Sisense or custom visualisations. Embed SDK is a JavaScript library for embedding entire Sisense dashboards. REST APIs cover data, data models and security. A free trial is available.

No prices are published for either plan. Buyers contact sales.

ThoughtSpot sells an agentic analytics platform with a separate ThoughtSpot Embedded product line for software companies. The embedded product page centres on Spotter, ThoughtSpot's AI analyst. Buyers can embed Spotter white-labelled inside their product, or expose its skills to their own agents through MCP and APIs. ThoughtSpot states that in both cases answers are checked against a governed semantic layer, so numbers stay consistent across every surface and customer.

Dashboards are described as AI-augmented: they surface anomalies and what changed without being asked, with unlimited drill-downs under each chart. Custom actions, writebacks and workflows can be embedded so that a user can update a record or start an approval from a chart. Developers embed components with the Visual Embed SDK and use REST APIs for other tasks. SpotterCode, an AI coding agent that works in the developer's IDE, generates embed code and components. An AI Theme Builder applies a brand described in plain language to every component.

For multi-tenant products, each customer gets its own Org with personalised content and security, and ThoughtSpot says onboarding a new customer is configuration rather than a redeployment. Row-level security controls what each user sees. The vendor also highlights monetisation: Spotter and advanced analytics can sit behind a premium tier, and usage can be priced by seat, organisation or capability.

The pricing page lists two ThoughtSpot Embedded tiers. Both use flexible pricing models aligned to the buyer's use case and go-to-market needs, rather than list prices. One tier covers embeddable AI dashboards, natural-language search, Spotter agents with unlimited LLM tokens, Analyst Studio and the API and SDK. The other covers dashboards, search and the API and SDK with a predictable cost per user or customer. ThoughtSpot states it does not meter or charge for LLM tokens under its subscriptions.

Power BI Embedded is Microsoft's Azure service for putting Power BI reports, dashboards and tiles into websites and web applications. Microsoft's documentation describes two solutions. Embed for your customers, also called app-owns-data, lets a developer build an application that authenticates to Power BI without user interaction, so external customers view embedded content without Power BI credentials. Microsoft says this is typically used by independent software vendors building applications for third parties. Embed for your organisation, or user-owns-data, is for internal users who sign in with their own Power BI accounts. A simpler no-code secure embed option also exists, but every viewer then needs an appropriate Power BI licence.

Content in a custom app is embedded through JavaScript or .NET SDKs or the REST APIs, which lets the developer control and customise the user experience. The Azure product page describes white-labelling Power BI to provide customer-facing dashboards and analytics.

Embedding requires capacity, which Microsoft describes as a distinct pool of computing resources. Power BI Embedded ships with A SKUs. Power BI Premium uses P and EM SKUs, and Microsoft Fabric uses F SKUs, which also support embedding Power BI items. Publishing content requires a Power BI Pro or Premium Per User account, unless a service principal publishes through the REST API.

The Azure pricing page lists A1 to A8 nodes, from 1 virtual core with 3 GB of RAM up to 128 virtual cores with 400 GB. Microsoft states that billing is at an hourly rate based on the nodes deployed, computed to the second and billed hourly, and that no charge applies while the service is paused. Prices depend on region, currency and agreement type. When this page was checked, the rate table did not display figures, so buyers should use the Azure pricing calculator or request a quote.

Omni is a business intelligence and embedded analytics platform built around a semantic model. Its embedded analytics page is aimed at product teams building customer-facing reports and data products. The central idea is a single platform: metrics and business context are defined once and reused across internal BI and external, customer-facing deployments.

For customer-facing use, Omni lists fast dashboard loading through modern processing and caching, and governed metrics defined once and applied across every customer instance. Branding is handled with CSS and markdown. Security features include role-based access and audit logs, and the vendor lists SOC 2, HIPAA and GDPR compliance. End customers can build and share their own reports inside the host product using AI, Excel-style calculations, SQL or a point-and-click interface. APIs and an MCP server allow Omni's querying to be used in chatbots and other AI features the software company is building.

Omni's distinguishing emphasis is change management. The vendor lists version control, branches, CI/CD and testing environments, so that updates to embedded analytics can be tested before release and customers do not see a broken report. This is relevant to teams that treat customer-facing dashboards as part of their shipped product.

In October 2025 Omni announced the acquisition of Explo, an embedded analytics company. Omni's announcement states that Explo would keep operating for 12 months while customers are migrated to Omni, after which the Explo platform is to be sunset. Buyers who had shortlisted Explo should evaluate Omni instead. The vendor cites customer examples, including Uscreen launching revamped analytics in about a month. These are vendor-published results.

No public pricing page was found. Buyers contact the sales team.