Embedded analytics refers to integrating interactive dashboards, reports, and AI-driven data insights directly into applications or workflows. This approach lets users access analytics in context without switching to a separate BI tool. It’s a rapidly growing market – valued around $20 billion in 2024 and projected to reach $75 billion by 2032 (18% CAGR).
Organizations are embracing embedded analytics to empower end-users with real-time information. These trends are fueled by demand for self-service data access and AI features like natural language queries and automated insights, which make analytics more accessible.
Below we review top tools that provide AI-powered embedded analytics and reporting. Each tool includes an overview, key pros and cons, and a breakdown of pricing tiers.
AI Tools for Embedded Analytics and Reporting (Comparison Table)
| AI Tool | Best For | Price | Features |
|---|---|---|---|
| Explo | Turnkey, white-label SaaS dashboards | Free internal · embed from $795/mo | No-code builder, Explo AI NLQ, SOC 2/HIPAA |
| ThoughtSpot | Google-style NL search for data in apps | Dev trial free · usage-based quote | SpotIQ AI insights, search & Liveboards embed |
| Tableau Embedded | Pixel-perfect visuals & broad connectors | $12–70/user/mo | Pulse AI summaries, drag-drop viz, JS API |
| Power BI Embedded | Azure-centric, cost-efficient scaling | A1 capacity from ~$735/mo | NL Q&A, AutoML visuals, REST/JS SDK |
| Looker | Governed metrics & Google Cloud synergy | Custom (≈$120k+/yr) | LookML model, secure embed SDK, BigQuery native |
| Sisense | OEMs needing deep white-label control | Starter ≈$10k/yr · Cloud ≈$21k/yr | ElastiCube in-chip, NLQ, full REST/JS APIs |
| Qlik | Associative, real-time data exploration | $200–2,750/mo (capacity-based) | Associative engine, Insight Advisor AI, Nebula.js |
| Domo Everywhere | Cloud BI with built-in ETL & sharing | From ~$3k/mo (quote) | 500+ connectors, alerts, credit-based scaling |
| Yellowfin BI | Data storytelling & flexible OEM pricing | Custom (≈$15k+/yr) | Stories, Signals AI alerts, multi-tenant |
| Mode Analytics | SQL/Python notebooks to embedded reports | Free · Pro ≈$6k/yr | Notebooks, API embed, Visual Explorer |
(Source: Explo)
Explo is an embedded analytics platform designed for product and engineering teams to quickly add customer-facing dashboards and reports to their apps. It offers a no-code interface for creating interactive charts and supports white-labeled embedding, so the analytics blend into your product’s UI.
Explo focuses on self-service: end-users can explore data and even build ad hoc reports without needing developer intervention. A standout feature is Explo AI, a generative AI capability that lets users ask free-form questions and get back relevant charts automatically.
This makes data exploration as easy as typing a query in natural language. Explo integrates with many databases and is built to scale from startup use cases to enterprise deployments (it’s SOC II, GDPR, and HIPAA compliant for security).
Pros and Cons
- Drag-and-drop dashboards—embed in minutes
- Generative AI (Explo AI) for NLQ insights
- Full white-label + SOC 2 / HIPAA compliance
- Young platform; smaller community
- Costs rise with large end-user counts
- Cloud-only; no on-prem deployment
Pricing: (Monthly subscriptions – USD)
- Launch – Free: Internal BI use only; unlimited internal users/dashboards.
- Growth – from $795/month: For embedding in apps; includes 3 embedded dashboards, 25 customer accounts.
- Pro – from $2,195/month: Advanced embedding; unlimited dashboards, full white-label, scales with usage.
- Enterprise – Custom: Custom pricing for large scale deployments; includes priority support, SSO, custom features.
Visit Explo →
ThoughtSpot’s Analyst Studio | Day in the life Demo
Mode is a platform geared towards advanced analysts and data scientists, combining BI with notebooks. It’s now part of ThoughtSpot (acquired in 2023) but still offered as a standalone solution.
Mode’s appeal in an embedded context is its flexibility: analysts can use SQL, Python, and R in one environment to craft analyses, then publish interactive visualizations or dashboards that can be embedded into web apps. This means if your application’s analytics require heavy custom analysis or statistical work, Mode is well-suited.
It has a modern HTML5 dashboarding system and recently introduced “Visual Explorer” for drag-and-drop charting, plus AI assist features for query suggestions. Companies often use Mode to build rich, bespoke analytics for their customers – for example, a software company might use Mode to develop a complex report, and then embed that report in their product for each customer with the data filtered appropriately.
Mode supports white-label embedding, and you can control it via their API (to provision users, run queries, etc.). It’s popular with data teams due to the seamless workflow from coding to sharing insights.
Pros and Cons
- Unified SQL, Python, R notebooks → dashboards
- Strong API for automated embedding
- Generous free tier for prototyping
- Analyst skills (SQL/Python) required
- Fewer NLQ/AI features for end-users
- Visualization options less extensive than Tableau
Pricing: (USD)
- Studio (Free) – $0 forever for up to 3 users. This includes core SQL/Python/R analytics, private data connections, 10MB query limit, etc. Good for initial development and testing of embedded ideas.
- Pro (Business) – Starts around ~$6,000/year (estimated). Mode doesn’t list fixed prices, but third-party sources indicate pro plans in the mid four-figure range annually for small teams.
- Enterprise – Custom pricing, typically five-figure annually up to ~$50k for large orgs. Includes all Pro features plus enterprise security (SSO, advanced permissions), custom compute for heavy workloads, and premium support.
Visit Mode →
How to Choose the Right Embedded Analytics Tool
Selecting an embedded analytics solution requires balancing your company’s needs with each tool’s strengths. Start with your use case and audience: Consider who will be using the analytics and their technical level. If you’re embedding dashboards for non-technical business users or customers, a tool with an easy UI could be important. Conversely, if your application demands highly custom analyses or you have a strong data science team, a more flexible code-first tool might be better.
Also evaluate whether you need a fully managed solution (more plug-and-play, e.g. Explo or Domo) or are willing to manage more infrastructure for a potentially more powerful platform (e.g. self-hosting Qlik or Sisense for complete control). The size of your company (and engineering resources) will influence this trade-off – startups often lean towards turnkey cloud services, while larger enterprises might integrate a platform into their existing tech stack.
Integration and scalability are critical factors. Look at how well the tool will integrate with your current systems and future architecture. Finally, weigh pricing and total cost of ownership against your budget and revenue model. Embedded analytics tools vary from per-user pricing to usage-based and fixed OEM licenses. Map out a rough projection of costs for 1 year and 3 years as your user count grows.
FAQs (Embedded Analytics and Reporting)
1. What are the main differences between Tableau and Power BI?
Tableau focuses on advanced visual design, cross-platform deployment (on-prem or any cloud), and a large viz library, but it costs more per user. Power BI is cheaper, tightly integrated with Microsoft 365/Azure, and great for Excel users, though some features require an Azure capacity and Windows-centric stack.
2. How does Sisense handle large datasets compared to other tools?
Sisense’s proprietary ElastiCube “in-chip” engine compresses data in memory, letting a single node serve millions of rows while maintaining fast query response; benchmarks show 500 GB cubes on 128 GB RAM. Competing BI tools often rely on external warehouses or slower in-memory engines for similar workloads.
3. Which embedded analytics tool offers the best customization options?
Sisense and Qlik are stand-outs: both expose full REST/JavaScript APIs, support deep white-labeling, and let dev teams build bespoke visual components or mashups—ideal when you need analytics to look and feel 100 % native in your app.
4. Are there any free alternatives to Tableau and Sisense?
Yes—open-source BI platforms like Apache Superset, Metabase, Redash, and Google’s free Looker Studio deliver dashboarding and basic embedded options at zero cost (self-hosted or SaaS tiers), making them good entry-level substitutes for smaller teams or tight budgets.
