Embedded analytics refers to integrating interactive dashboards, reviews, and AI-driven information insights immediately into functions or workflows. This strategy lets customers entry analytics in context with out switching to a separate BI instrument. It’s a quickly rising market – valued round $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 info. These traits are fueled by demand for self-service information entry and AI options like pure language queries and automatic insights, which make analytics extra accessible.
Beneath we overview high instruments that present AI-powered embedded analytics and reporting. Every instrument consists of an outline, key execs and cons, and a breakdown of pricing tiers.
AI Instruments for Embedded Analytics and Reporting (Comparability Desk)
AI Instrument | Greatest For | Worth | Options |
---|---|---|---|
Explo | Turnkey, white-label SaaS dashboards | Free inner · embed from $795/mo | No-code builder, Explo AI NLQ, SOC 2/HIPAA |
ThoughtSpot | Google-style NL seek for information in apps | Dev trial free · usage-based quote | SpotIQ AI insights, search & Liveboards embed |
Tableau Embedded | Pixel-perfect visuals & broad connectors | $12–70/person/mo | Pulse AI summaries, drag-drop viz, JS API |
Power BI Embedded | Azure-centric, cost-efficient scaling | A1 capability from ~$735/mo | NL Q&A, AutoML visuals, REST/JS SDK |
Looker | Ruled metrics & Google Cloud synergy | Customized (≈$120k+/yr) | LookML mannequin, safe embed SDK, BigQuery native |
Sisense | OEMs needing deep white-label management | Starter ≈$10k/yr · Cloud ≈$21k/yr | ElastiCube in-chip, NLQ, full REST/JS APIs |
Qlik | Associative, real-time information exploration | $200–2,750/mo (capacity-based) | Associative engine, Perception 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 | Information storytelling & versatile OEM pricing | Customized (≈$15k+/yr) | Tales, Alerts AI alerts, multi-tenant |
Mode Analytics | SQL/Python notebooks to embedded reviews | Free · Professional ≈$6k/yr | Notebooks, API embed, Visible Explorer |

(Supply: Explo)
Explo is an embedded analytics platform designed for product and engineering groups to rapidly add customer-facing dashboards and reviews to their apps. It provides a no-code interface for creating interactive charts and helps white-labeled embedding, so the analytics mix into your product’s UI.
Explo focuses on self-service: end-users can discover information and even construct advert hoc reviews without having developer intervention. A standout characteristic is Explo AI, a generative AI functionality that lets customers ask free-form questions and get again related charts robotically.
This makes information exploration as simple as typing a question in pure language. Explo integrates with many databases and is constructed to scale from startup use instances to enterprise deployments (it’s SOC II, GDPR, and HIPAA compliant for safety).
Professionals and Cons
- Drag-and-drop dashboards—embed in minutes
- Generative AI (Explo AI) for NLQ insights
- Full white-label + SOC 2 / HIPAA compliance
- Younger platform; smaller neighborhood
- Prices rise with giant end-user counts
- Cloud-only; no on-prem deployment
Pricing: (Month-to-month subscriptions – USD)
- Launch – Free: Inner BI use solely; limitless inner customers/dashboards.
- Progress – from $795/month: For embedding in apps; consists of 3 embedded dashboards, 25 buyer accounts.
- Professional – from $2,195/month: Superior embedding; limitless dashboards, full white-label, scales with utilization.
- Enterprise – Customized: Customized pricing for giant scale deployments; consists of precedence help, SSO, customized options.
Go to Explo →
ThoughtSpot is an AI-driven analytics platform famend for its search-based interface. With ThoughtSpot’s embedded analytics, customers can sort pure language queries (or use voice) to discover information and immediately get visible solutions.
This makes analytics accessible to non-technical customers – primarily a Google-like expertise for your enterprise information. ThoughtSpot’s in-memory engine handles giant information volumes, and its AI engine (SpotIQ) robotically finds insights and anomalies.
For embedding, ThoughtSpot supplies low-code elements and strong REST APIs/SDKs to combine interactive Liveboards (dashboards) and even simply the search bar into functions. It’s widespread for customer-facing analytics in apps the place end-users want ad-hoc querying capability.
Companies in retail, finance, and healthcare use ThoughtSpot to let frontline staff and clients ask information questions on the fly. The platform emphasizes ease-of-use and quick deployment, although it additionally provides enterprise options like row-level safety and scalability throughout cloud information warehouses.
Professionals and Cons
- Google-style NL seek for information
- SpotIQ AI auto-surfaces traits
- Embeds dashboards, charts, or simply the search bar
- Enterprise-grade pricing for SMBs
- Restricted superior information modeling
- Setup wants schema indexing experience
Pricing: (Tiered, with consumption-based licensing – USD)
- Necessities – $1,250/month (billed yearly): For bigger deployments; elevated information capability and options.
- ThoughtSpot Professional: Customized quote. Full embedding capabilities for customer-facing apps (as much as ~500 million information rows).
- ThoughtSpot Enterprise: Customized quote. Limitless information scale and enterprise SLA. Consists of multi-tenant help, superior safety, and so forth.
Tableau (a part of Salesforce) is a number one BI platform recognized for its highly effective visualization and dashboarding capabilities. Tableau Embedded Analytics permits organizations to combine Tableau’s interactive charts and reviews into their very own functions or web sites.
Builders can embed Tableau dashboards by way of iFrames or utilizing the JavaScript API, enabling wealthy information visuals and filtering in-app. Tableau’s energy lies in its breadth of out-of-the-box visuals, drag-and-drop ease for creating dashboards, and a big person neighborhood.
It additionally has launched AI options – for instance, in 2024 Salesforce introduced Tableau Pulse, which makes use of generative AI to ship automated insights and pure language summaries to customers. This augments embedded dashboards with proactive explanations.
Tableau works with a variety of knowledge sources and provides dwell or in-memory information connectivity, making certain that embedded content material can show up-to-date information. It’s well-suited for each inner embedded use (e.g. inside an enterprise portal) and exterior customer-facing analytics, although licensing value and infrastructure should be deliberate accordingly.
Professionals and Cons
- Market-leading visible library
- New “Pulse” AI summaries & NLQ
- Broad information connectors + huge neighborhood
- License value balloons at scale
- Requires Tableau Server/Cloud infrastructure
- Styling customization by way of JS API solely
Pricing: (Subscription per person, with role-based tiers – USD)
- Creator – $70 per person/month: Full authoring license (information prep, dashboard creation). Wanted for builders constructing embedded dashboards.
- Explorer – $35 per person/month: For customers who discover and edit restricted content material. Appropriate for inner energy customers interacting with embedded reviews.
- Viewer – $12 per person/month: Learn-only entry to view dashboards. For finish viewers of embedded analytics.
Microsoft Energy BI is a widely-used BI suite, and Energy BI Embedded refers back to the Azure service and APIs that allow you to embed Energy BI visuals into customized functions. That is enticing for builders constructing customer-facing analytics, because it combines Energy BI’s strong options (interactive reviews, AI visuals, pure language Q&A, and so forth.) with versatile embedding choices.
You’ll be able to embed full reviews or particular person tiles, management them by way of REST API, and apply row-level safety for multi-tenant situations. Energy BI’s strengths embrace tight integration with the Microsoft ecosystem (Azure, Workplace 365), robust information modeling (by way of Energy BI Desktop), and rising AI capabilities (e.g. the Q&A visible that enables customers to ask questions in plain English).
Professionals and Cons
- Wealthy BI + AI visuals (NL Q&A, AutoML)
- Azure capability pricing scales to any person base
- Deep Microsoft ecosystem integration
- Preliminary setup could be advanced (capacities, RLS)
- Devs want Energy BI Professional licenses
- Some portal options absent in embeds
Pricing: (Azure capacity-based or per-user – USD)
- Energy BI Professional – $14/person/month: Permits creating and sharing reviews. Required for builders and any inner customers of embedded content material.
- Energy BI Premium Per Person – $24/person/month: Enhanced options (AI, bigger datasets) on a per-user foundation. Helpful if a small variety of customers want premium capabilities as an alternative of a full capability.
- Energy BI Embedded (A SKUs) – From ~$735/month for A1 capability (3 GB RAM, 1 v-core). Scales as much as ~$23,500/month for A6 (100 GB, 32 cores) for high-end wants. Billed hourly by way of Azure, with scale-out choices.
Looker is a contemporary analytics platform now a part of Google Cloud. It’s recognized for its distinctive information modeling layer, LookML, which lets information groups outline enterprise metrics and logic centrally.
For embedded analytics, Looker supplies a sturdy resolution: you’ll be able to embed interactive dashboards or exploratory information tables in functions, leveraging the identical Looker backend. Considered one of Looker’s core strengths is consistency – due to LookML, all customers (and embedded views) use trusted information definitions, avoiding mismatched metrics.
Looker additionally excels at integrations: it connects natively to cloud databases (BigQuery, Snowflake, and so forth.), and since it’s within the Google ecosystem, it integrates with Google Cloud providers (permissions, AI/ML by way of BigQuery, and so forth.).
Professionals and Cons
- LookML enforces single supply of reality
- Safe embed SDK + full theming
- Tight BigQuery & Google AI integration
- Premium six-figure pricing widespread
- Steep LookML studying curve
- Visuals much less flashy than Tableau/Energy BI
Pricing: (Customized quotes by way of gross sales; instance figures)
Sisense is a full-stack BI and analytics platform with a powerful deal with embedded analytics use instances. It permits corporations to infuse analytics into their merchandise by way of versatile APIs or net elements, and even permits constructing customized analytic apps.
Sisense is thought for its ElastiCube in-chip reminiscence expertise, which may mash up information from a number of sources and ship quick efficiency for dashboards. In recent times, Sisense has included AI options (e.g. NLQ, automated insights) to remain aggressive.
A key benefit of Sisense is its capability to be absolutely white-labeled and its OEM-friendly licensing, which is why many SaaS suppliers select it to energy their in-app analytics. It provides each cloud and on-premises deployment choices, catering to totally different safety necessities.
Sisense additionally supplies a spread of customization choices: you’ll be able to embed whole dashboards or particular person widgets, and use their JavaScript library to deeply customise appear and feel. It’s fitted to organizations that want an end-to-end resolution – from information preparation to visualization – particularly tailor-made for embedding in exterior functions.
Professionals and Cons
- ElastiCube fuses information quick in-memory
- White-label OEM-friendly APIs
- AI alerts & NLQ for end-users
- UI studying curve for brand spanking new customers
- Quote-based pricing could be steep
- Superior setup typically wants dev assets
Pricing: (Annual license, quote-based – USD)
- Starter (Self-Hosted) – Begins round $10,000/yr for a small deployment (few customers, fundamental options). This may usually be an on-prem license for inner BI or restricted OEM use.
- Cloud (SaaS) Starter – ~$21,000/yr for ~5 customers on Sisense Cloud (cloud internet hosting carries ~2× premium over self-host).
- Progress/Enterprise OEM – Prices scale considerably with utilization; mid-range deployments typically vary $50K-$100K+ per yr. Massive enterprise offers can attain a number of hundred thousand or extra if there are very excessive numbers of end-users.
Qlik is a long-time chief in BI, providing Qlik Sense as its fashionable analytics platform. Qlik’s embedded analytics capabilities assist you to combine its associative information engine and wealthy visuals into different functions.
Qlik’s differentiator is its Associative Engine: customers can freely discover information associations (making picks throughout any fields) and the engine immediately updates all charts to replicate these picks, revealing hidden insights.
In an embedded state of affairs, this implies end-users can get highly effective interactive exploration, not simply static filtered views. Qlik supplies APIs (Functionality API, Nebula.js library, and so forth.) to embed charts and even construct absolutely customized analytics experiences on high of its engine. It additionally helps customary embed by way of iframes or mashups.
Qlik has included AI as effectively – the Perception Advisor can generate insights or chart ideas robotically. For builders, Qlik’s platform is kind of strong: you’ll be able to script information transformations in its load script, use its safety guidelines for multi-tenant setups, and even embed Qlik into cell apps.
Professionals and Cons
- Associative engine permits free exploration
- Quick in-memory efficiency for giant information
- Strong APIs + Perception Advisor AI
- Distinctive scripting → greater studying curve
- Enterprise-level pricing
- UI can really feel dated with out theming
Pricing: (USD)
- Starter – $200 / month (billed yearly): Consists of 10 customers + 25 GB “information for evaluation.” No additional information add-ons accessible.
- Commonplace – $825 / month: Begins with 25 GB; purchase extra capability in 25 GB blocks. Limitless person entry.
- Premium – $2,750 / month: Begins with 50 GB, provides AI/ML, public/nameless entry, bigger app sizes (10 GB).
- Enterprise – Customized quote: Begins at 250 GB; helps bigger app sizes (as much as 40 GB), multi-region tenants, expanded AI/automation quotas.
Domo is a cloud-first enterprise intelligence platform, and Domo All over the place is its embedded analytics resolution aimed toward sharing Domo’s dashboards outdoors the core Domo surroundings. With Domo All over the place, corporations can distribute interactive dashboards to clients or companions by way of embed codes or public hyperlinks, whereas nonetheless managing all the things from the central Domo occasion.
Domo is thought for its end-to-end capabilities within the cloud – from information integration (500+ connectors, built-in ETL known as Magic ETL) to information visualization and even a built-in information science layer.
For embedding, Domo emphasizes ease of use: non-technical customers can create dashboards in Domo’s drag-and-drop interface, then merely embed them with minimal coding. It additionally provides strong governance so you’ll be able to management what exterior viewers see.
Professionals and Cons
- Finish-to-end cloud BI with 500+ connectors
- Easy drag-and-embed workflow
- Actual-time alerts & collaboration instruments
- Credit score-based pricing difficult to funds
- Cloud-only; no on-prem choice
- Deeper customized UI wants dev work
Pricing: (Subscription, contact Domo for quote – USD)
- Fundamental Embedded Package deal – roughly $3,000 per 30 days for a limited-user, limited-data state of affairs. This may embrace a handful of dashboards and a reasonable variety of exterior viewers.
- Mid-size Deployment – roughly $20k–$50k per yr for mid-sized companies. This may cowl extra customers and information; e.g., just a few hundred exterior customers with common utilization.
- Enterprise – $100k+/yr for large-scale deployments. Enterprises with 1000’s of exterior customers or very excessive information volumes can count on prices in six figures. (Domo typically buildings enterprise offers as unlimited-user however metered by information/question credit.)
Yellowfin is a BI platform that has carved a distinct segment in embedded analytics and information storytelling. It provides a cohesive resolution with modules for dashboards, information discovery, automated alerts (alerts on modifications), and even a singular Story characteristic for narrative reporting.
For embedding, Yellowfin Embedded Analytics supplies OEM companions a versatile licensing mannequin and technical capabilities to combine Yellowfin content material into their functions. Yellowfin’s energy lies in its balanced focus: it’s highly effective sufficient for enterprise BI but in addition streamlined for embedding, with options like multi-tenant help and white-labeling.
It additionally has NLP question (pure language querying) and AI-driven insights, aligning with fashionable traits. A notable characteristic is Yellowfin’s information storytelling – you’ll be able to create slide-show fashion narratives with charts and textual content, which could be embedded to offer end-users contextual evaluation, not simply uncooked dashboards.
Yellowfin is commonly praised for its collaborative options (annotations, dialogue threads on charts) which could be helpful in an embedded context the place you need customers to interact with the analytics.
Professionals and Cons
- Constructed-in Tales & Alerts for narratives
- OEM pricing adaptable (fastened or revenue-share)
- Multi-tenant + full white-label help
- Decrease model recognition vs. “huge three”
- Some UI components really feel legacy
- Superior options require coaching
Pricing: (Customized – Yellowfin provides versatile fashions)
Mode is a platform geared in direction of superior analysts and information scientists, combining BI with notebooks. It’s now a part of ThoughtSpot (acquired in 2023) however nonetheless supplied as a standalone resolution.
Mode’s enchantment in an embedded context is its flexibility: analysts can use SQL, Python, and R in a single surroundings to craft analyses, then publish interactive visualizations or dashboards that may be embedded into net apps. This implies in case your software’s analytics require heavy customized evaluation or statistical work, Mode is well-suited.
It has a contemporary HTML5 dashboarding system and lately launched “Visible Explorer” for drag-and-drop charting, plus AI help options for question ideas. Corporations typically use Mode to construct wealthy, bespoke analytics for his or her clients – for instance, a software program firm may use Mode to develop a posh report, after which embed that report of their product for every buyer with the info filtered appropriately.
Mode helps white-label embedding, and you may management it by way of their API (to provision customers, run queries, and so forth.). It’s widespread with information groups as a result of seamless workflow from coding to sharing insights.
Professionals and Cons
- Unified SQL, Python, R notebooks → dashboards
- Sturdy API for automated embedding
- Beneficiant free tier for prototyping
- Analyst abilities (SQL/Python) required
- Fewer NLQ/AI options for end-users
- Visualization choices much less intensive than Tableau
Pricing: (USD)
- Studio (Free) – $0 perpetually for as much as 3 customers. This consists of core SQL/Python/R analytics, personal information connections, 10MB question restrict, and so forth. Good for preliminary growth and testing of embedded concepts.
- Professional (Enterprise) – Begins round ~$6,000/yr (estimated). Mode doesn’t checklist fastened costs, however third-party sources point out professional plans within the mid four-figure vary yearly for small groups.
- Enterprise – Customized pricing, usually five-figure yearly as much as ~$50k for giant orgs. Consists of all Professional options plus enterprise safety (SSO, superior permissions), customized compute for heavy workloads, and premium help.
The best way to Select the Proper Embedded Analytics Instrument
Choosing an embedded analytics resolution requires balancing your organization’s wants with every instrument’s strengths. Begin along with your use case and viewers: Contemplate who can be utilizing the analytics and their technical stage. For those who’re embedding dashboards for non-technical enterprise customers or clients, a instrument with a straightforward UI may very well be vital. Conversely, in case your software calls for extremely customized analyses or you’ve got a powerful information science workforce, a extra versatile code-first instrument is likely to be higher.
Additionally consider whether or not you want a totally managed resolution (extra plug-and-play, e.g. Explo or Domo) or are prepared to handle extra infrastructure for a probably extra highly effective platform (e.g. self-hosting Qlik or Sisense for full management). The scale of your organization (and engineering assets) will affect this trade-off – startups typically lean in direction of turnkey cloud providers, whereas bigger enterprises may combine a platform into their present tech stack.
Integration and scalability are important elements. Have a look at how effectively the instrument will combine along with your present programs and future structure. Lastly, weigh pricing and whole value of possession towards your funds and income mannequin. Embedded analytics instruments range from per-user pricing to usage-based and stuck OEM licenses. Map out a tough projection of prices for 1 yr and three years as your person rely grows.
FAQs (Embedded Analytics and Reporting)
1. What are the principle variations between Tableau and Energy BI?
Tableau focuses on superior visible design, cross-platform deployment (on-prem or any cloud), and a big viz library, nevertheless it prices extra per person. Energy BI is cheaper, tightly built-in with Microsoft 365/Azure, and nice for Excel customers, although some options require an Azure capability and Home windows-centric stack.
2. How does Sisense deal with giant datasets in comparison with different instruments?
Sisense’s proprietary ElastiCube “in-chip” engine compresses information in reminiscence, letting a single node serve tens of millions of rows whereas sustaining quick question response; benchmarks present 500 GB cubes on 128 GB RAM. Competing BI instruments typically depend on exterior warehouses or slower in-memory engines for related workloads.
3. Which embedded analytics instrument provides the very best customization choices?
Sisense and Qlik are stand-outs: each expose full REST/JavaScript APIs, help deep white-labeling, and let dev groups construct bespoke visible elements or mashups—preferrred whenever you want analytics to appear and feel 100 % native in your app.
4. Are there any free alternate options to Tableau and Sisense?
Sure—open-source BI platforms like Apache Superset, Metabase, Redash, and Google’s free Looker Studio ship dashboarding and fundamental embedded choices at zero value (self-hosted or SaaS tiers), making them good entry-level substitutes for smaller groups or tight budgets.