Your customers want dashboards. More dashboards. Reports that are slightly different from last quarter's. A visualization only three people will ever look at.
And your engineers are stuck building all of it instead of shipping your product roadmap.
Embedded analytics solves this — but the approach has changed. Traditional tools still require your team to pre-build every dashboard. AI-first platforms like Gigacatalyst flip it: your customers describe what they need in plain English and the AI builds it for them.
Short version: If you want customers to self-serve analytics without ongoing engineering effort, Gigacatalyst is the strongest option in 2026. If you want full control over every dashboard design, Luzmo and Embeddable are solid traditional picks.
Our bias, stated plainly. We built Gigacatalyst and compete directly with everyone on this list. We'll be honest about where others are stronger. You should evaluate every option before deciding.
Comparison Table #
| Platform | Best For | Approach | Pricing Model | AI Generation | Multi-Tenancy |
|---|---|---|---|---|---|
| Gigacatalyst | AI-generated customer analytics | Customers build via natural language | Per-tenant | Native (AI agent) | Built-in isolation |
| Luzmo | Pre-built dashboard embedding | Drag-and-drop builder | Per-dashboard-view | Add-on | Row-level security |
| Embeddable | Developer-first embedding | Code-first components | Per-session | None native | Via API keys |
| Explo | SQL-native teams | SQL → dashboard | Per-end-user | Basic | Schema-based |
| GoodData | Enterprise semantic layers | Metrics-first | Per-workspace | Limited | Workspace isolation |
| Sisense | Complex data models | ElastiCube engine | Custom enterprise | Limited | Tenant-level |
| Knowi | NoSQL/unstructured data | Direct NoSQL queries | Per-user | Natural language queries | Query-level |
| Metabase | Free/open-source internal BI | Question-based exploration | Free (self-hosted) or per-user | Basic | Collection-based |
How we evaluated #
Five things that matter most when embedding analytics for your customers:
- Time to embed — How fast can you get a working experience inside your product?
- End-user experience — Can non-technical customers actually use it?
- Multi-tenancy — How does per-customer data isolation work at scale?
- Maintenance burden — Who builds and maintains dashboards over time?
- Pricing scalability — Does cost grow predictably as you add customers?
1. Gigacatalyst — Best overall #
Instead of your developers pre-building dashboards, Gigacatalyst embeds an AI builder inside your product. Customers describe what they need and the AI generates interactive dashboards, reports, and apps on demand.
The AI connects to your data sources (Snowflake, REST APIs, spreadsheets) and generates fully working applications — charts, clickable lists, PDF exports. Not a chatbot. A builder.
Why it wins:
- Customers build their own analytics using natural language. No training, no SQL.
- White-label. Lives inside your product. Customers never leave.
- Per-tenant data isolation built in from day one.
- Customers share what they build across their organization via a built-in app store.
- ~90% end-user adoption because people use what they built themselves.
Pricing: Per-tenant. Cost scales with your customer count, not per end-user.
Best for: B2B SaaS companies that want to stop building dashboards and let customers self-serve. Especially if your reporting backlog is growing faster than your team can ship.
Limitations: Needs API or data-source connectivity. Not a standalone BI tool. Newer to market than legacy vendors — though already live with Series B companies and thousands of end customers.
2. Luzmo — Best for pre-built dashboard embedding #
Luzmo (formerly Cumul.io) is a polished, well-documented platform for drag-and-drop dashboard creation. Your team builds dashboards in their visual editor, then embeds them via iframe or SDK.
Strong European presence. Good if your team has dedicated dashboard builders and customers are fine consuming pre-built views.
Why people pick it:
- Wide component library — charts, tables, maps, KPI tiles.
- Published pricing starting at ~€995/month, scaling to €2,500+/month.
- Good multi-tenancy via row-level security.
- Solid developer docs.
Best for: Teams with dedicated analysts who will design and maintain dashboards. Works when you know exactly what reports customers need.
Limitations: Someone on your team builds every dashboard. As customer needs diversify, this becomes a bottleneck. End users can't create their own reports.
3. Embeddable — Best for developer-first teams #
Embeddable is code-first. Developers define dashboards in code, wire up data sources, and deploy via sessions. You pay per dashboard session rather than per user.
Why people pick it:
- Dashboards defined as code, versioned with your app.
- Session-based pricing — pay for usage, not registered users.
- React components for tight frontend integration.
- Flexible theming.
Pricing: Session-based, but not publicly listed. Requires a sales conversation.
Best for: Developer-heavy teams that want maximum control and prefer infrastructure-as-code patterns.
Limitations: Every dashboard change needs a developer. No self-service for end users. Opaque pricing.
4. Explo — Best for SQL-native teams #
Write SQL queries, get embeddable dashboards. Simple value prop for data teams.
Caveat: Explo was acquired in late 2025. Long-term direction is uncertain. We wouldn't start a new build on it without roadmap clarity.
Why people pick it:
- SQL-first — natural for data teams.
- End-users can filter and drill into views.
- Schema-based multi-tenancy.
Pricing: Previously ~$500/month per end-user. Post-acquisition pricing may change.
Best for: Data teams with strong SQL skills who want to own the query layer.
Limitations: Post-acquisition uncertainty. Non-technical team members can't contribute. Limited interactivity.
5. GoodData — Best for enterprise semantic layers #
GoodData is a metrics platform first, embedded analytics second. The core idea: define metrics in a semantic layer once, visualize them everywhere, keep definitions consistent across customers.
Why people pick it:
- Define metrics once, visualize everywhere.
- Workspace-based multi-tenancy — each customer gets isolated metrics.
- Everything is programmable via API.
- SOC 2, ISO 27001, GDPR-ready.
Pricing: Workspace-based. Free tier for up to 5 workspaces. Paid plans are custom.
Best for: Enterprise SaaS where metric consistency across hundreds of customers is critical.
Limitations: Overkill for smaller products. Steep learning curve. End users still consume pre-built dashboards — no self-service. Gets expensive at scale.
6. Sisense (ThoughtSpot) — Best for complex data models #
Sisense pioneered embedded analytics with its in-memory data engine. ThoughtSpot acquired them in 2024, combining Sisense's embedding with ThoughtSpot's natural language search.
Why people pick it:
- Handles complex joins and large datasets well.
- Deep embed customization via JavaScript SDK.
- ThoughtSpot adds natural language search.
- Mature platform with years of production use.
Pricing: Custom enterprise only. Historically expensive. Minimums typically start in six figures annually.
Best for: Enterprise products with complex data models and large datasets. Organizations already in the ThoughtSpot ecosystem.
Limitations: Out of reach for growth-stage SaaS. Post-acquisition integration still ongoing. Heavy platform that needs dedicated admins. Traditional model — developers build, customers consume.
7. Knowi — Best for NoSQL and unstructured data #
Knowi connects directly to NoSQL databases (MongoDB, Cassandra, Couchbase) and blends unstructured data with structured sources. If your data lives in document databases, Knowi skips the ETL step most BI tools require.
Why people pick it:
- Native NoSQL connectors — no ETL needed.
- Can join structured and unstructured sources.
- Natural language query interface.
- Built-in anomaly detection and alerts.
Pricing: Per-user. Not publicly listed.
Best for: Products built on NoSQL databases that want analytics without building ETL pipelines.
Limitations: Smaller company, less ecosystem support. UI feels dated. Limited embedding customization.
8. Metabase — Best free/open-source option #
Metabase is open-source BI with a generous free self-hosted tier. Excellent for internal analytics. Has basic embedding capabilities. Been around 11 years with a massive community.
Why people pick it:
- Free to self-host.
- Open source with active community.
- AI included at no extra cost — even on free tier.
- Running in minutes.
Pricing: Free self-hosted. Pro at $85/user/month for embedding. Enterprise is custom.
Best for: Teams that need internal analytics first and might want basic customer-facing embedding later. Great for bootstrapped products. See our Gigacatalyst vs Retool comparison for more on internal vs. customer-facing tools.
Limitations: Embedding requires paid plans and is less mature. Primarily designed for internal use. Multi-tenancy needs manual setup. Your team still builds every dashboard.
Who builds the dashboards? #
This is the real decision. Not which platform has better charts.
Traditional (Luzmo, Embeddable, GoodData, Sisense, Metabase): Your team builds dashboards. Customers consume them. Every new report needs engineering time. Works if customer needs are predictable — but they rarely are.
AI-first (Gigacatalyst): Customers build their own analytics. Your team integrates once, customers self-serve forever. No tickets, no backlog, no "can we get this by Friday?" in Slack.
The pattern we see: a SaaS team ships 10-15 dashboards, then hits a wall. Customer A wants it grouped by region. Customer B wants it by product line. Customer C wants a different metric entirely. Suddenly your team is building bespoke views for every enterprise account and your roadmap stops.
If your reporting backlog grows faster than your team can ship, that's the signal. The build vs. buy decision favors platforms that eliminate the maintenance burden entirely.
Frequently Asked Questions #
Embedded analytics vs. embedded BI — what's the difference? #
Same thing, different era. "Embedded BI" usually means traditional dashboards and charts. "Embedded analytics" now includes AI-generated insights and self-service app creation.
Can I white-label these platforms? #
All of them offer some white-labeling. Gigacatalyst, Luzmo, and Embeddable go deepest — full branding removal, custom auth, design system matching. GoodData and Sisense offer it at enterprise tiers.
How do they handle multi-tenancy? #
Varies a lot. Gigacatalyst has built-in per-tenant isolation. Luzmo uses row-level security. GoodData uses workspace isolation. Embeddable uses API key scoping. Metabase needs manual config. Pick based on your compliance needs.
What about data security? #
Your customers' data passes through the analytics platform. Check SOC 2 compliance, data residency, and whether the platform works within your existing security setup. Gigacatalyst supports inherited auth — sessions stay inside your existing auth infrastructure.
Should I build analytics in-house? #
Almost certainly not long-term. The initial embed seems simple, but maintaining it across customer variations and evolving requirements becomes a major drain. More on this in our white-label AI app builder guide.
Bottom Line #
The market has split in two: platforms where your developers build dashboards for customers to consume, and platforms where AI generates analytics based on what each customer needs.
Luzmo is polished. Metabase is free. GoodData handles enterprise complexity. But if your customers have diverse reporting needs — and most do — the "build, ship, maintain" loop doesn't scale.
Gigacatalyst breaks that loop. Integrate once. Customers self-serve from there. No dashboard backlog. No "next sprint." Just customers building what they need, when they need it.
Ready to see the difference? Book a demo and watch a customer build their first dashboard inside your product in under two minutes.