7 Best Omni Analytics Alternatives in 2026 for Embedded and Governed BI

Omni Analytics (often shortened to Omni or Omni BI) is a business intelligence platform founded by former Looker leaders. It combines a governed semantic layer with SQL-first, ad hoc exploration, and it has built a loyal following among internal data teams. Omni has also moved hard into embedded analytics since acquiring Explo in October 2025, so it's no longer fair to call embedding an afterthought. But for SaaS product teams building customer-facing analytics, Omni still shows real limits: sales-led pricing you can't model upfront, a steep semantic-layer learning curve and an architecture that grew out of internal BI rather than product embedding.

If you've been evaluating Omni, or you're already using it and hitting walls, this guide covers seven alternatives worth considering.

The 7 best Omni Analytics alternatives in 2026 are:

1. Luzmo: best overall alternative for embedded, customer-facing analytics
2. Metabase: best free and open-source option
3. Looker: best for enterprises standardized on Google Cloud
4. Sigma Computing: best spreadsheet-style analytics on cloud warehouses
5. GoodData: best for regulated enterprises with strict deployment needs
6. Holistics: best code-first BI with transparent pricing
7. Sisense: best for component-level embedding via Compose SDK

Omni Analytics alternatives at a glance

Tool	Best for	Embedded-first?	Public pricing?	AI capabilities
Luzmo	Customer-facing embedded analytics in SaaS products	Yes, built for embedding from day one	Yes, MAU-based plans	Luzmo AI + Luzmo IQ conversational analyst
Metabase	Free, open-source internal BI	No, embedding on Pro/Enterprise plans	Yes, plus free open-source version	Basic AI features
Looker	Google Cloud enterprises with LookML skills	No, internal BI first	No, quote-based	Gemini-powered conversational analytics
Sigma Computing	Spreadsheet-style warehouse exploration	No, embedding is an add-on	Partially, confirm embedded pricing with sales	AI query and formula assistance
GoodData	Regulated enterprises, self-hosted deployments	Yes, purpose-built for embedding	Partially, enterprise quotes	AI-assisted analytics
Holistics	Code-first, dbt-friendly BI teams	No, lightweight iframe embedding	Yes, fully public	Limited
Sisense	Component-level embedding for developer teams	Repositioned around embedding	No, quote-based	AI features in Fusion

What changed at Omni in 2025 and 2026

Omni moved fast over the last year, and a few of those moves change how you should evaluate it. Here's what's confirmed as of August 5, 2026:

• Omni acquired Explo, announced on October 22, 2025. Explo was an embedded-analytics specialist, and the acquisition marked Omni's serious entry into customer-facing analytics.
• Omni raised a $120 million Series C on April 23, 2026, at a $1.5 billion valuation. Whatever else you conclude, funding risk isn't the concern here.
• Embedded analytics is now actively marketed, not tucked away. Omni combines its semantic model with governed BI and embedded delivery, and supports embedded components including iframes, KPI tiles and other extensible components.

The practical takeaway: if your last look at Omni was in 2024, your mental model is out of date. Embedding is a genuine investment area now. The question isn't whether Omni does embedded analytics, it's whether an internal-BI-first architecture serves a product team as well as a platform built only for embedding.

Omni and Explo: what buyers should know

If you're an Explo customer, or you're evaluating Omni partly because of Explo, this is the part that matters.

Omni's customer FAQ says the majority of Explo customers are expected to migrate to Omni over a roughly 12-month transition, ahead of the planned sunset of the Explo platform. Existing pricing stays in place through each customer's current contract.

A few things worth being precise about, because this gets garbled in a lot of write-ups:

• Explo hasn't shut down. There's a planned sunset and a migration path, which is a different situation from a product disappearing.
• The acquisition was announced in October 2025, not 2026. Some comparison articles have this wrong.
• Migration is still migration. Even a well-run transition means rebuilding dashboards, revalidating access controls and retraining whoever maintained the old setup.

If you're weighing Omni specifically because Explo is winding down, it's worth pricing the migration effort as part of the decision rather than treating the destination as a given. Moving to a platform you'd have chosen anyway is very different from moving to the one your vendor was acquired by.

Why look for an Omni Analytics alternative?

Omni is a capable tool. The reviews make that clear: users praise its customer support, intuitive UI, and the elegance of combining a governed semantic layer with ad hoc SQL. But recurring complaints reveal real friction, especially for teams building analytics into products rather than internal dashboards.

Here's what keeps coming up:

Steep learning curve around topics and the semantic layer

Setting up Omni's topic model takes meaningful time and expertise. Users consistently flag that onboarding, especially for non-technical team members, is harder than expected. Documentation sometimes lags behind new releases, which makes self-directed setup even slower.

Missing features and product immaturity

Omni is a relatively young product, and reviewers are candid about it. Notifications, better report organization, and more polished chart types are recurring feature requests. As one G2 reviewer noted, it "doesn't yet match the feature set of some of the more established tools." The pace of releases is promising, but the gaps are real today.

Dashboard instability on complex models

Complex dashboards can crash, particularly when the underlying data models are large or intricate. Multiple reviewers have flagged this, noting that performance depends heavily on how well the data model is built. That's a significant burden for teams without dedicated BI engineers.

Chart customization requires JSON wrangling

Omni's charts use Vega-Lite under the hood. Going beyond the out-of-the-box options means editing JSON configuration by trial and error. Native visualizations like KPI tiles feel polished, but the inconsistency in chart quality is a genuine usability issue.

Embedded analytics is newer than the internal BI core

This is the most important consideration for SaaS teams, and it needs stating carefully, because the easy version of this criticism is now out of date.

Omni is investing in embedded analytics for real. It bought Explo in October 2025, it markets embedded delivery actively and it supports embedded components including iframes and KPI tiles. Anyone telling you Omni ignores embedding is describing the 2024 product.

The honest concern is architectural sequencing rather than intent. Omni's semantic model, exploration workflow and governance were designed for internal analysts first, and embedded delivery was layered on afterward. In practice that shows up in the things product teams care about most: how much semantic modeling you have to complete before your customers see anything, how far CSS-level branding goes, how multi-tenant isolation is expressed and whether pricing scales sensibly to viewer volume rather than analyst seats.

None of that makes Omni a bad embedded platform. It does mean that if customer-facing analytics is your product surface rather than one delivery mode among several, you should compare it against tools whose entire architecture starts from that assumption.

How much does Omni Analytics cost?

As of August 5, 2026, we couldn't verify a stable public pricing grid for Omni. Plans are sales-led, with cost quoted per deployment based on users, use case and scale, which makes it difficult to model how costs will grow as an embedded user base expands. We're deliberately not publishing a number here, because any figure floating around review sites is somebody's negotiated deal rather than a list price.

For teams that need stakeholder buy-in early, or simply want to compare total cost of ownership across vendors, this is real friction. Several alternatives on this list, including Luzmo, Metabase and Holistics, publish their pricing publicly, and Luzmo prices on monthly active users rather than provisioned seats. If transparent, publicly listed embedded pricing is a hard requirement for your evaluation, that alone narrows the field considerably.

How we evaluated these alternatives

We compared these seven platforms on the dimensions that actually separate them, rather than on feature checklists:

• Embedding orientation. Whether customer-facing analytics is the core architecture or one delivery mode, including white-labeling depth, multi-tenant isolation and SDK control.
• Semantic modeling. How much modeling work stands between connecting data and shipping something, and whether that model is defined in a GUI, in code or not at all.
• Pricing transparency. Whether you can model cost before contacting sales, and whether the meter runs on seats, viewers or monthly active users.
• Deployment flexibility. Managed cloud, self-hosted or both, since data residency often decides the shortlist before features do.
• AI capabilities. Whether natural-language querying is aimed at internal analysts or at your end users, and how it's governed.

Research date. Product, deployment and pricing facts were checked against current vendor documentation on August 5, 2026. Pricing and packaging in this category change often, so treat specifics as a starting point for your own quote.

Our bias, stated plainly. Luzmo competes directly with Omni in embedded analytics, so we're not a neutral referee and you should read our recommendation with that in mind. What we've tried to do is keep the claim narrow: we recommend Luzmo for customer-facing embedded analytics inside SaaS products, and we say clearly where Metabase, Looker, Sigma, GoodData, Holistics or Sisense is the better answer. Where Omni has genuinely improved, like its embedded investment after the Explo acquisition, we've said so rather than leaving a stale criticism in place.

Best alternatives to Omni Analytics

1. Luzmo: best overall Omni alternative for embedded analytics

If you're building analytics into your product, not alongside it, Luzmo is the strongest alternative to Omni.

Where Omni treats embedding as one of several delivery modes, Luzmo was designed from day one with a single purpose: analytics that lives inside your SaaS product, fully branded as your own.

Built for embedding, not adapted to it

The architectural difference matters. Luzmo's embedded analytics studio gives product teams a no-code dashboard builder that drops directly into any application. There's no semantic layer to model, no Vega-Lite JSON to debug, and no internal BI scaffolding to work around. You connect your data, build your dashboards, and embed them: typically within days, not months.

Full CSS control and custom domain support mean Luzmo disappears into your product. End users never know they're looking at a third-party analytics layer. That level of white-labeling is not available out of the box with Omni.

Self-service analytics that scales to your users

Luzmo's self-service analytics layer gives your end users the ability to build, modify, and save their own dashboards, without needing SQL knowledge or analyst support. This is the capability that product teams consistently underestimate: embedding a static dashboard is step one, but giving users the tools to answer their own questions is what drives actual retention and expansion revenue.

Omni has a "Create Mode" for end users, but it's oriented around internal analysts. Luzmo's self-service is designed for customers: non-technical users who need to answer business questions in the context of your product.

Composable analytics for developer teams

For engineering teams that want full control, Luzmo offers composable analytics, a modular approach where analytics UI components are assembled rather than configured inside a monolithic dashboard tool. This matters when your product has opinionated UI patterns, existing design systems, or complex interaction requirements that generic dashboard builders simply can't accommodate.

The Flex SDK takes this further: it exposes Luzmo's rendering and data layer as low-code building blocks, giving developers the ability to create fully custom data interactions without rebuilding analytics infrastructure from scratch.

Luzmo AI and Luzmo IQ: analytics that answers questions

Luzmo's AI capabilities are embedded-first by design. Luzmo AI brings AI-powered insight generation, automated anomaly detection, and natural language querying across all of Luzmo's capabilities, not just as a standalone chat interface, but woven into the dashboard and exploration experience.

The flagship AI feature, Luzmo IQ, is a conversational AI analyst agent that sits inside your product. End users can ask questions in plain language and get accurate, governed answers, without leaving your application, without seeing your data stack, and without requiring any data literacy. This is meaningfully different from Omni's NLQ, which operates within the constraints of the internal semantic layer and is aimed primarily at analyst users.

Transparent pricing built for embedded scale

Omni's pricing is entirely sales-led, with no public plans. Luzmo publishes its plans and prices on monthly active users (MAUs) rather than provisioned seats, so your cost tracks the customers who actually use analytics each month, and you can see it upfront. For products with large or fluctuating end-user bases, that transparency and MAU basis make total cost of ownership far easier to predict than a sales-only quote.

Luzmo vs. Omni, side by side

The Luzmo compare page at luzmo.com/compare/omni covers every feature dimension in detail, including multi-tenancy, security, localization, and mobile responsiveness.

Best for: software product teams building customer-facing analytics, companies that need full white-labeling, engineering-led teams who want SDK-level control, and any organization where per-viewer pricing economics are unsustainable.

2. Metabase

Metabase is one of the most widely deployed BI tools in the world, largely because its open-source version is free. The interface is genuinely beginner-friendly: you can connect a database and build a dashboard in under an hour without writing SQL. Non-technical users can query data using a point-and-click question builder, and the charting options cover most standard business intelligence needs.

For embedding, Metabase offers two modes: signed embeds (for public or semi-public content) and full-app embedding (for authenticated, multi-user product analytics). The latter is limited to Pro and Enterprise plans. Multi-tenant implementations are possible but require careful permissions architecture and more engineering work than purpose-built platforms.

The open-source foundation is both Metabase's biggest selling point and its main limitation. You can self-host for free, but product-grade embedded analytics, with SSO, row-level security, white-labeling and tenant isolation, still requires significant engineering investment on top of the base product. Teams that start from the self-hosting requirement rather than the vendor usually end up comparing self-hosted Metabase alternatives against managed platforms on total engineering cost, not license cost.

Best for: teams with limited budgets who need basic internal BI, developers who want to self-host and extend an open-source platform, and organizations that don't require sophisticated multi-tenancy.

3. Looker

Looker is the platform that Omni was explicitly designed to improve on. The Omni founding team came from Looker, and the products share DNA in their semantic layer approach. Looker remains the most mature LookML-based analytics platform available, with deep integration across the Google Cloud ecosystem (BigQuery, Vertex AI, Dataplex).

For enterprise teams already standardized on Google Cloud, Looker's semantic layer is genuinely powerful: centralized metric definitions, version-controlled data models, and robust governance. Embedded analytics is supported through Looker's Embed SDK and iFrame approach, and it can be made to work well, but it requires LookML expertise and careful administration. Looker has also added Gemini-powered conversational analytics and a Conversational Analytics API, so AI is no longer a gap.

The downsides are significant: Looker is expensive, administratively heavy, and carries a steep onboarding curve even for experienced data teams. It's also explicitly designed around internal BI use cases; customer-facing embedded analytics is achievable but not the primary design orientation.

Best for: enterprise teams with in-house LookML skills, a Google Cloud center of gravity, and the resources to invest in platform administration.

4. Sigma Computing

Sigma Computing occupies an interesting niche: it's a cloud warehouse analytics tool built around a spreadsheet-style interface. Users interact with live data from Snowflake, BigQuery, or Redshift using formulas and pivot-table logic that feels familiar to anyone coming from Excel. The result is a tool that data analysts love: the analytical power of a cloud warehouse without having to learn SQL from scratch.

Sigma's embedding capabilities exist but are positioned as an add-on rather than a core product pillar. Its licensing is oriented around creators and analysts, so if you plan to embed at viewer scale it's worth confirming current pricing directly. Costs can add up, and the embedding experience is geared toward analysts sharing dashboards rather than product teams delivering customer-facing analytics at scale.

Where Sigma excels is in live, exploratory analysis on large datasets. If your team lives in spreadsheets and needs direct warehouse access with a familiar interface, Sigma is genuinely excellent. If you need to embed that analysis into a multi-tenant SaaS product at viewer scale, it becomes a more complex and expensive proposition.

Best for: data-heavy analytics teams who are comfortable in spreadsheets and need live warehouse querying. Less suited for customer-facing embedded analytics products.

5. GoodData

GoodData is one of the few platforms in this market that was purpose-built for embedded analytics, a meaningful distinction from tools like Looker and Sigma that adapted internal BI platforms for external delivery. Its multi-tenant architecture supports complex enterprise deployments, and its governance and security features (RBAC, row-level security, data access policies) are genuinely enterprise-grade.

GoodData supports iframe embedding, Web Components and a React SDK, and its embedded offering covers white-labeling, multitenancy and role-based access controls. Deployment runs either managed cloud or self-hosted, which matters for regulated industries or companies with strict data residency requirements. Pricing on the public page is structured as a platform fee plus per-workspace pricing with unlimited users and data, and the actual figure is quote-based, so compare architecture and total deployment cost rather than a headline rate.

The analytics modeling layer is powerful but carries complexity. Implementation typically requires a dedicated data engineering engagement, and time-to-value is measured in weeks rather than days. If a composable, warehouse-adjacent semantic layer is the part you actually care about, it's worth reviewing GoodData alternatives with a composable semantic layer side by side, since that architecture choice tends to outlast the dashboard layer built on top of it.

GoodData is a strong choice for enterprises that need deployment flexibility, advanced governance, and are willing to invest in a substantial implementation project. For SaaS teams who need to move quickly, the overhead may be prohibitive.

Best for: large enterprises in regulated industries, organizations with strict data residency or deployment requirements, and teams with dedicated BI engineering capacity.

6. Holistics BI

Holistics takes a code-first approach to analytics: the semantic layer is defined in a modeling language rather than a GUI, making it appealing to data engineering teams who are comfortable with version-controlled, code-based workflows. It integrates cleanly with dbt and supports standard SQL without requiring a proprietary modeling language.

Transparent pricing is a genuine differentiator, with one caveat. Holistics publishes its internal BI plans publicly, starting with a platform fee plus per-user licensing, which makes total cost far easier to evaluate without a sales conversation. Embedded pricing is a separate, custom conversation though, so don't assume the public numbers cover a customer-facing deployment.

The embedding story is functional but lighter than a dedicated platform's, and it isn't positioned as a full embedded analytics product. For teams that primarily need BI with some external sharing capability, it works well. For SaaS teams building customer-facing analytics as a core product feature, the toolset is limiting. Our roundup of Holistics alternatives for code-first teams goes deeper if the modeling-in-code approach is what appeals to you.

Best for: SQL-first data engineering teams who want transparent pricing and code-based semantic modeling, and organizations with lighter embedding requirements.

7. Sisense

Sisense has repositioned itself around embedded analytics and its Compose SDK, a component-based embedding approach that gives frontend developers significant control over how analytics elements are rendered inside applications. This is different from iframe-based embedding: individual charts, filters, and KPIs can be embedded as independent components and integrated into a product's existing UI rather than delivered as a standalone dashboard.

Compose SDK isn't React-only either: it supports React, Angular and Vue with TypeScript, which matters if your frontend isn't on the default stack. The platform has strong governance, row-level security and a mature feature set built up over years of enterprise deployments, plus cloud, dedicated and on-premises deployment options with white-labeling, SSO and multitenancy. Sisense's Fusion product also includes AI-powered analytics capabilities.

The main friction points are plan structure and pricing. Sisense offers self-service and enterprise plans, with enterprise pricing supplied through sales rather than a public numeric grid, so cost comparisons need a quote rather than a list price. Implementation complexity is also higher than lighter-weight alternatives. Teams that get this far usually care most about frontend control, which is the same reason they end up comparing purpose-built Embeddable alternatives that expose components rather than dashboards.

Best for: developer-led teams that want component-level embedding control, and organizations with existing Sisense relationships or deep front-end customization needs.

Best option by architecture

Feature lists blur together. Architecture doesn't, and it's the thing you can't change later, so it's worth choosing on this axis first.

• Embedded-first platforms (Luzmo, GoodData): customer-facing delivery, white-labeling and tenant isolation are structural rather than configurable. Fastest path to analytics your customers use, and the right default if analytics is part of what you sell.
• Internal-BI-first platforms (Omni, Looker, Sigma): a governed modeling layer and analyst exploration come first, with embedding available on top. Best when your primary audience is your own team.
• Warehouse-native tools (Sigma, Holistics): compute stays in Snowflake, BigQuery or Databricks and the tool is a query and presentation layer. Excellent if your warehouse is already well modeled, and a real prerequisite if it isn't.
• Headless semantic layers (Cube and similar): the metrics layer is decoupled from any UI, and you build the frontend. Maximum control, most engineering. If you're drawn to this pattern, compare the Cube alternatives for a headless semantic layer before committing, because the build burden is easy to underestimate.
• Open-source self-hosted (Metabase): lowest license cost, highest operational cost. The trade you're making is money for engineering time.

One distinction is worth calling out separately, because teams conflate it constantly: customer-facing analytics is not the same problem as internal operational tooling. If what you actually need is a fast admin panel or an internal CRUD interface over your database, an analytics platform is the wrong shape of tool entirely, and the Basedash alternatives for internal tools are a better place to start. Analytics platforms optimize for reading and understanding data at scale. Internal tools optimize for editing records and running operations. Buying one to do the other is a common and expensive mistake.

How to choose the right Omni alternative

The right alternative depends almost entirely on what you're building analytics for.

The internal BI vs. embedded analytics distinction isn't a feature difference. It's an architectural orientation that affects pricing, scalability, white-labeling, multi-tenancy, and developer experience across the entire platform.

Use this as a starting framework:

• Building customer-facing embedded analytics as a core product feature? → Luzmo. It wins on speed to embed, SDK depth, white-labeling, and embedded AI.
• Need a free or self-hosted option for internal BI? → Metabase. The open-source version costs nothing and covers standard reporting needs.
• Enterprise standardized on Google Cloud with LookML skills in-house? → Looker. The semantic layer and BigQuery integration are unmatched in that ecosystem.
• Analysts who live in spreadsheets and need direct warehouse access? → Sigma Computing.
• Regulated industry with data residency or self-hosting requirements? → GoodData.
• Code-first data team that wants public pricing and dbt-friendly workflows? → Holistics.
• Want AI-powered analytics embedded directly in your product? → Luzmo IQ is a purpose-built conversational AI for customer-facing analytics. Omni's NLQ is better suited to internal analyst workflows.

The bottom line

Omni Analytics is a well-built BI tool that works best for internal data teams who want a modern Looker alternative. It's also a more serious embedded contender than it was a year ago: the Explo acquisition in October 2025 and the $120 million Series C in April 2026 say the investment is real. Give it credit for that.

But if your roadmap centers on analytics inside your product, dashboards your customers interact with, self-service exploration and AI-powered insights delivered at viewer scale, you're asking an internal-BI architecture to do a job it wasn't originally shaped for. Add pricing you can't model without a sales call, and the comparison gets harder to justify.

Luzmo is. It was built from the ground up for SaaS teams who need embedded analytics that disappears into their product, self-service that scales to thousands of non-technical users, and AI-powered querying grounded in governed data, all under a pricing model that doesn't penalize you for growing.

→ See how Luzmo compares to Omni, or start a free trial today.