Quick Verdict
Yellowfin is an enterprise BI and embedded-analytics platform, not a separate product called “Yellowfin BI AI.” Assisted Insights, Natural Language Query, and Tell Me About My Data belong to the same Yellowfin product. NLQ turns plain-language questions and follow-ups into charts; Assisted Insights automates analysis; Tell Me About My Data is the interface action that generates AI-written explanations and recommended charts from dashboards, report toolbars, or Report Builder. Dashboards, Signals, Stories, and Present complete the broader workflow.
It is aimed at established analytics programs and OEM/ISV teams embedding white-label analytics into software. Enterprise BI and Embedded Analytics are quote based. The website offers demos, a 30-day trial, and an embedded proof-of-concept path. Selection should focus on semantic models, authorization, tenant isolation, external-LLM data flow, deployment, capacity, support, and total cost rather than treating individual AI labels as independent tools.
Best For
- Enterprise teams combining dashboards, governed self-service, automated insights, NLQ, and storytelling.
- Business users who need guided access to curated metrics rather than raw database access.
- OEMs and ISVs embedding white-label analytics into a SaaS or industry application.
- Technical teams able to maintain semantic models, row and column permissions, identity, data quality, audit, and publishing controls.
- Not ideal for individuals seeking a permanently free spreadsheet assistant or organizations expecting an LLM to replace financial and operational accountability.
Key Features
- Dashboards and reports: interactive reports, KPIs, filters, drilldowns, alerts, and governed business views.
- Natural Language Query: asks questions and generates charts while retaining conversational context for follow-ups.
- Assisted Insights: automated analysis of drivers, anomalies, and explanations, subject to business validation.
- Tell Me About My Data: generates explanations and recommended charts inside existing analysis surfaces.
- Signals: monitors changes, anomalies, and thresholds and directs users toward issues worth investigation.
- Stories and Present: combines visualizations, explanations, and context into collaborative narratives.
- Embedded Analytics: white labeling, JavaScript API or secure iframe integration, multi-tenancy, and in-product analytics.
- Deployment choices: self-managed on premises or in cloud infrastructure, plus a vendor-managed service subject to contract.
- Connectors and extensions: enterprise data-source integration with component-specific licensing boundaries.
Use Cases
Start with one governed subject area such as sales or service operations. Build an approved semantic model, then test a fixed NLQ suite for metric definitions, date grain, currency, filters, tenant permissions, and conversational follow-ups. A metric owner should review Tell Me About My Data explanations. Signals also need thresholds, noise controls, owners, and response procedures; detection alone does not create an operational process.
An embedded pilot should cover white labeling, SSO, tenant isolation, JavaScript or iframe integration, mobile behavior, export, peak concurrency, upgrade compatibility, and customer-level audit. OpenBB is a closer fit for financial research and developer data workflows. n8n can automate actions after an insight, while Dify is an AI application and RAG layer. Neither replaces Yellowfin’s complete BI semantic, authorization, and embedded-analytics stack.
Pricing
Yellowfin does not publish one universal seat price. Enterprise BI and Embedded Analytics require a sales quote. The official site offers a 30-day free trial, product demonstrations, and an embedded POC. A trial is an evaluation right, not a free production license.
| Path | Price | Intended use |
|---|---|---|
| 30-day trial | Free trial | Evaluate sample or customer data and technical fit |
| Enterprise BI | Quote | Reporting, governed self-service, and collaboration |
| Embedded Analytics | Quote | OEM/ISV white-label in-product analytics |
| Aligned Utility | Quote | Pricing aligned to a site, app, bus, device, or similar business unit |
| Revenue Share | Quote | Share revenue from an add-on analytics module |
| Server Core | Quote | Scale price with deployment server cores |
| Self-managed or vendor-managed | Contract | Different infrastructure, service, and responsibility boundaries |
Require the quote to specify production and nonproduction environments, users or tenants, cores, modules, AI features, external-model fees, connectors, storage, support, upgrades, disaster recovery, training, taxes, minimum commitments, overages, and exit exports. Flexible embedded pricing must be modeled against the product’s own growth curve.
Pros
- One enterprise product combines dashboards, NLQ, automated insight, Signals, and storytelling.
- Assisted Insights and Tell Me About My Data work in existing report context.
- Strong OEM/ISV white-label, embedding, and multi-tenant positioning.
- Self-managed and vendor-managed deployment choices.
- A 30-day trial, demos, and POC path support evaluation with representative data.
Cons
- Enterprise and embedded editions require quotes, increasing procurement effort.
- Licensing, deployment, and embedded pricing need careful growth modeling.
- NLQ and generated explanations depend on semantic design, field names, permissions, and data quality.
- External LLMs may receive context needed to generate an insight; payload and provider terms require verification.
- The proprietary core is not made auditable or portable by a few open connectors or examples.
- Mainland China access, hosting region, support, and cross-border dependencies need separate testing.
Alternatives
| Tool | Best fit | Main difference |
|---|---|---|
| Yellowfin | Enterprise BI and OEM/ISV embedding | Integrated NLQ, automated insights, Signals, Stories, and white labeling; quote based |
| OpenBB | Financial research and developer data workflows | More open platform components, with separate data licensing questions |
| Dify | Custom RAG and AI applications | Application orchestration rather than a full enterprise BI platform |
| n8n | Cross-system automation | Strong connectors and execution, not a governed analytics semantic layer |
| iFlytek Astron Agent | Chinese agents and domestic channels | Agent building rather than enterprise BI and embedded analytics |
FAQ
Is Yellowfin BI AI a separate product?
No. The legacy directory entry has been consolidated into canonical Yellowfin. Assisted Insights, NLQ, and Tell Me About My Data are related capabilities inside the platform.
Does Yellowfin have a free edition?
The vendor advertises a 30-day trial and demo or POC paths. Production Enterprise BI and Embedded Analytics are paid and quote based.
How is Yellowfin priced?
Enterprise and embedded deployments require a quote. The embedded pricing page describes Aligned Utility, Revenue Share, and Server Core models, with final cost determined by deployment and contract.
Does row-level data stay out of external LLMs?
The vendor states that detailed row-level data is analyzed locally and does not leave the instance. Buyers should still verify whether prompts, schemas, field names, aggregates, summaries, questions, or metadata are sent to the configured external model. “No row-level data” does not mean the entire AI request is data free.
Is Yellowfin open source?
The core platform is proprietary commercial software. Individual connectors, SDKs, or examples may have open licenses, but those licenses do not apply automatically to the server, web UI, AI features, or complete distribution.
How do Assisted Insights, NLQ, and Tell Me About My Data relate?
They are complementary labels inside one product. NLQ creates and refines charts from questions, Assisted Insights automates analysis, and Tell Me About My Data triggers AI explanations and chart suggestions in the user interface.
Is it suitable for embedding in SaaS?
That is a primary Yellowfin use case. Validate white labeling, identity, tenant isolation, performance, upgrade behavior, audit, mobile use, and the cost curve through a POC before signing a production contract.
Bottom Line
The canonical Yellowfin entry should represent the full enterprise and embedded BI platform rather than inventing a standalone “Yellowfin BI AI.” NLQ, Assisted Insights, and Tell Me About My Data serve one ask, visualize, understand, and share workflow. The vendor offers a 30-day trial, while production enterprise and embedded deployments require quotes. Pilot with a representative semantic model and real permissions, inspect exactly what an external LLM receives, and model support and growth costs. Keep the licensing statement equally precise: the core is proprietary, while any open connector or example is governed only by its own license.