The best Luzmo alternatives for embedded analytics in 2026 are Embeddable, Qrvey, Reveal, and Knowi. The right choice depends on whether you want frontend control with a code-first semantic layer, multi-tenant infrastructure inside your own cloud, SDK-first embedding across web and desktop, or No-ETL querying across SQL, NoSQL, and APIs.
Key Takeaways
- Luzmo in 2026 is not the low-code iframe tool it was in 2023. It ships web components, a headless Flex SDK, and a full AI layer. Compare it as it is now.
- Embeddable is the closest philosophical peer: code-first, Cube-based models that live in your repo, native web components.
- Qrvey deploys into your own AWS, Azure, or GCP account, which matters when data residency is a customer requirement.
- Reveal has the widest connector list of the four and a bring-your-own-model AI architecture.
- The real dividing line is not embedding quality. It is what each platform assumes about your data before the first chart exists.
Why SaaS Teams Look for Luzmo Alternatives
Luzmo is a capable platform. It connects live to databases including MongoDB, embeds through web components and a headless Flex SDK rather than iframes alone, and its Luzmo IQ layer now includes an embeddable chat component, AI summaries, search, chart generation, Agent APIs, and an MCP server. Any comparison that describes Luzmo as iframe-based low-code tooling is describing an older product.
Teams still move, and usually for one of three reasons.
Data shape. Luzmo’s documentation notes that for MongoDB datasets it “infers the structure of a dataset and the data types of the columns from the first 1000 records.” That works well for consistent collections. It becomes a maintenance problem when documents are genuinely heterogeneous, because a field that first appears in document 4,000 will not surface in the inferred schema. Teams then keep a normalization step alive purely to keep the analytics layer’s view of the data honest.
Deployment control. Luzmo runs as managed cloud, with its Enterprise plan adding “dedicated infrastructure with custom SLAs and flexible hosting options.” If your own customers contractually require that data stay in a specific environment, you need to know exactly what that means before signing.
Pricing shape at scale. Luzmo publishes Starter from €495 per month and Premium from €1,995 per month billed annually. Session and viewer-based models are predictable early and less so once an embedded deployment reaches tens of thousands of end users.
A note on Explo
Explo appears on most 2026 alternatives lists and is worth one clarification. Omni acquired Explo on October 22, 2025. Explo became a wholly owned subsidiary, and Omni stated the platform would keep operating for roughly twelve months while Explo’s founders supported customer migrations onto Omni. Existing customers retained full access during the transition. If you are selecting for a multi-year roadmap, you are effectively selecting Omni, so evaluate it on that basis.
The Four Alternatives, Compared Honestly
Embeddable
Embeddable is the closest peer to Luzmo in philosophy and the furthest from it in workflow. Analytics are defined as code: data models are Cube-based, live in your own repository, and go through version control and review like any other application code. Dashboards embed as native HTML web components into React, Vue, or plain HTML rather than as iframes.
It connects to Athena, BigQuery, ClickHouse, Elasticsearch, MSSQL, Snowflake, and Trino. That list tells you the target: teams whose analytics data already lives in a warehouse or an analytics-optimized store. Embeddable runs in eu-central-1 and us-east-1, with PrivateLink available for private connectivity when your database sits in the same region.
Pricing is unusually transparent for this category: a free tier with 3 dashboards and 200 sessions per month, Lite at $499 per month, and flat-rate custom pricing above that.
Pick it if your engineers want analytics in the codebase and your data is already modeled in a warehouse. Skip it if your source of truth is a document database and you do not want to maintain a pipeline into a warehouse first.
Qrvey
Qrvey’s distinguishing choice is where it runs. Rather than a vendor-hosted SaaS, Qrvey deploys into your own cloud account using a containerized Kubernetes architecture, and supports AWS, Azure, and GCP. For SaaS vendors whose enterprise customers impose data residency terms, that removes an entire category of objection.
It also ships its own analytic database. Qrvey Ultra includes an embedded analytic engine supporting both co-mingled and segregated multi-tenant data models, while Qrvey Pro lets you bring your own engine for a lighter footprint. Pricing is flat-rate with unlimited users, dashboards, instances, data, and connections, and a perpetual license option is available.
Pick it if multi-tenancy and data residency are your hard constraints. Skip it if you do not want to operate infrastructure in your own cloud account.
Reveal
Reveal, from Infragistics, is the most developer-native of the four. Its SDK covers React, Angular, Vue, Blazor, .NET, Java, and Node, plus desktop and mobile targets, which no other platform here matches. It carries 30+ connectors including MongoDB, REST API, OData, Snowflake, Databricks, BigQuery, Redshift, and the usual relational set, and it deploys on-premises or in the cloud.
Its AI architecture deserves attention. Reveal has you supply the AI endpoint and choose the models, and states that it does not host your AI, store your data, or operate hidden vector databases. AI-generated insights come in summary, analysis, and forecast forms, and it reads models from Azure ML and BigQuery ML. Pricing is flat-rate with unlimited end users; third-party listings report annual licenses in the $30,000 to $50,000 range, though Infragistics does not publish figures.
Pick it if you need one analytics SDK across web, desktop, and mobile, or you want to run your own LLM endpoint. Skip it if you want a hosted, low-configuration setup.
Knowi
Knowi queries SQL, NoSQL, and REST API sources directly and joins across them in a single virtual layer, without first moving data into a warehouse. It lists 70+ data sources including native MongoDB analytics, along with Elasticsearch, Cassandra, and InfluxDB, and handles nested JSON without flattening.
Two things separate it in this specific group. Cross-source joins mean a single query can combine a MongoDB collection, a Postgres table, and a REST API without a pipeline in between. And Private AI means the platform runs its own models rather than routing your data to a third-party LLM, which is a different guarantee from bringing your own model endpoint. Deployment spans cloud, on-premises, and VPC.
Pick it if your product data spans multiple source types, your documents are deeply nested, or your customers impose deployment and AI-governance constraints. Skip it if your data already sits cleanly in one warehouse, where a warehouse-native tool will be simpler.
Comparison Table
| Feature | Luzmo | Embeddable | Qrvey | Reveal | Knowi |
|---|---|---|---|---|---|
| Data connectivity | Live SQL connections plus native MongoDB; schema inferred from first 1,000 documents. | Athena, BigQuery, ClickHouse, Elasticsearch, MSSQL, Snowflake, Trino. | Built-in analytic engine (Ultra) or bring your own (Pro). | 30+ connectors including MongoDB, REST API, OData, and major warehouses. | 70+ sources across SQL, NoSQL, and REST APIs with cross-source joins, no ETL. |
| Modeling approach | Dataset-level modeling on connected sources. | Cube-based models as code, version-controlled in your repo. | Data prep and automation built into the platform. | SDK and dashboard-level modeling. | Optional modeling; NLQ can run on unmodeled data. |
| AI approach | Luzmo IQ: chat, summaries, search, chart generation, Agent APIs, MCP server. | AI-assisted development workflow around the models. | Built-in ML within automation workflows. | Bring your own AI endpoint and models; Reveal does not host or store. | Agentic NLQ with Private AI; platform runs its own models. |
| Deployment | Managed cloud; Enterprise adds dedicated infrastructure and flexible hosting. | Managed cloud in eu-central-1 and us-east-1; PrivateLink available. | Your own AWS, Azure, or GCP account via Kubernetes. | On-premises or cloud. | Cloud, on-premises, or VPC. |
| Multi-tenancy | Filter and parameter-based isolation. | Environments map models to per-tenant database connections. | Co-mingled or segregated tenant data models in the engine. | Data source filtering and user-based access control. | Isolation at the query and data source level. |
| Embedding method | Web components, headless Flex SDK, React/Angular/Vue/React Native, iframe. | Native HTML web components into React, Vue, or plain HTML. | Web components and a comprehensive API. | SDKs for React, Angular, Vue, Blazor, .NET, Java, Node, desktop, mobile. | White-label SDK, secure URL, and SSO-integrated embedding. |
| Published pricing | Starter from €495/mo; Premium from €1,995/mo, billed annually. | Free tier; Lite $499/mo; flat-rate custom above. | Flat-rate, unlimited users; perpetual license option. | Flat-rate, unlimited end users; figures not published. | Custom, scoped to data volume and features. |
Embed Analytics That Reach Your Whole Stack.
Most embedded platforms assume your data is already modeled in one warehouse. Knowi assumes it is not. Connect MongoDB, Postgres, Elasticsearch, Snowflake, and REST APIs, join across them in a single query, and embed the result in your product. No pipeline in between, no flattening of nested documents, and no schema migration before the first dashboard ships.
What you can do with Knowi in your application:
- Join a MongoDB collection, a SQL table, and an API response in one query, without ETL.
- Query nested arrays and irregular documents natively, with no schema inference limits.
- Give end users natural language search over their own data, running on unmodeled sources.
- Isolate tenants at the query and data source level, not per dashboard.
- Embed white-labeled dashboards through SDK, secure URL, or SSO.
- Deploy in the cloud, on-premises, or in your VPC when customer contracts require it.
Knowi runs its own AI models rather than routing queries to a third-party LLM provider, which matters when your customers ask where their data is processed and which models see it.
How to Choose
Work backwards from your constraints rather than forwards from feature lists.
If a customer contract dictates where data lives, that eliminates most of the field immediately and points to Qrvey or an on-premises deployment. If your engineering culture demands that everything sit in version control, Embeddable is built for exactly that. If you ship a desktop or mobile client alongside your web app, Reveal is the only option here that covers all three from one SDK. If your data is spread across a document store, a relational database, and a handful of APIs, the question becomes whether you want to build pipelines to unify it or query it where it lives.
One practical note for teams on MongoDB: the legacy MongoDB Connector for BI reaches end of life after September 2026, replaced by MongoDB’s SQL Interface. If your current analytics stack reaches MongoDB through that connector, you have migration work coming regardless of which platform you choose, which makes this a sensible moment to reassess rather than re-plumb.
For a wider view across the category, including enterprise BI platforms, see our roundup of the best embedded analytics platforms for B2B SaaS. For the multi-tenant architecture questions specifically, we cover the tradeoffs in embedded analytics for SaaS, and teams working with search-backed data can see how this applies to Elasticsearch embedded analytics.
Whichever way you lean, run the proof of concept against your messiest real collection rather than a clean sample. Architectural mismatches surface within a day on real data and stay hidden for months on demo data.
If your data spans SQL, NoSQL, and APIs and you want to see what querying it without a pipeline actually looks like, book a technical demo with our team.
Frequently Asked Questions
Is Luzmo iframe-only for embedding?
No. Luzmo supports iframe and URL-based embedding, but also offers native web component embedding, a headless Flex SDK for component-level control, and libraries for React, Angular, Vue, and React Native. Comparisons describing Luzmo as primarily iframe-based are describing an earlier version of the product.
Does Luzmo support direct connectivity to MongoDB?
Yes. Luzmo has a native MongoDB connector and connects to live MongoDB data without requiring a SQL warehouse. The practical constraint is schema inference: Luzmo determines column structure and data types from the first 1,000 records in a collection, so fields appearing only in later or irregular documents may not surface. Collections with highly variable document shapes typically need additional modeling.
What is the difference between Embeddable and Luzmo?
Both embed through native web components rather than iframes. The difference is workflow and data assumptions. Embeddable defines analytics as Cube-based code in your own repository under version control, and connects to warehouses and analytics stores such as BigQuery, Snowflake, ClickHouse, and Trino. Luzmo offers a visual editor alongside its SDK and connects to operational databases including MongoDB directly.
Which embedded analytics platforms can be self-hosted?
Qrvey deploys into your own AWS, Azure, or GCP account using Kubernetes. Reveal supports on-premises deployment. Knowi supports on-premises and VPC deployment alongside managed cloud. Luzmo runs as managed cloud, with dedicated infrastructure and flexible hosting available on its Enterprise plan. Embeddable is hosted in eu-central-1 and us-east-1 with PrivateLink for private database connectivity.
What happened to Explo?
Omni acquired Explo on October 22, 2025. Explo operates as a wholly owned subsidiary, and Omni said the platform would keep running for roughly twelve months while Explo’s founders helped customers migrate onto Omni. Teams evaluating Explo in 2026 should evaluate Omni’s roadmap alongside it.
What is Private AI in embedded analytics?
Private AI means the analytics platform runs its own AI models rather than routing your data to a third-party LLM provider, so proprietary data is not sent to an external model vendor or used to train public models. It can run in cloud or on-premises environments. It differs from bring-your-own-endpoint architectures, where you supply and pay for an external model but control which one.
How should I evaluate these platforms before committing?
Run a proof of concept against your most irregular real data, not a clean sample. Connect the collection with the most variable document structure, embed one dashboard inside your actual application shell, and test tenant isolation by deliberately attempting cross-tenant access. Then confirm in writing where data is processed and which models see it, because that answer is what most often fails an enterprise security review later.