Sisense

Name: Sisense
Brand: Sisense

Sisense is an API-first analytics platform that modernizes businesses by seamlessly embedding in-context analytics within applications, leading to action-oriented insights that enhance decision intelligence

Est 2004|Valuation $1B|Raised $391.2M|19 Ref Customers

LinkedIn 70K|Web traffic 128K|FTEs 553|HQ New York, New York, US

MarketsBusiness Intelligence (BI) and Analytics Data Visualization Embedded Analytics

01Competitive Landscape

Narrative analysis

Sisense vs Looker: - Looker emphasizes a modeled semantic layer (LookML) and metric governance. Sisense may be more appealing where theming and embedding customization are prioritized, but procurement should validate the depth of theming and whether embedded UX meets product UX requirements. Sisense vs Power BI Embedded: - Power BI Embedded provides a published capacity pricing model and a broad ecosystem. For SaaS embedding use cases, Power BI Embedded and QuickSight are often considered where predictable unit economics are a procurement priority. Sisense vs Tableau (embedded/OEM): - Tableau’s internal BI pricing is publicly documented for per-user scenarios, while embedded/OEM agreements typically require negotiation. Community commentary suggests Tableau has a large user community and ecosystem, though embedded licensing can be complex.

Adoption/market perception

G2 rankings and vendor announcements position Sisense in the Embedded BI category; these signals are directional and should be considered alongside independent market-share data and customer references.

	Sisense	Looker (Google Cloud)	Microsoft Power BI Embedded
Starting Price	Custom quote (not publicly standardized)	Published structure by edition on Google Cloud pricing page (exact totals depend on contract)	Published capacity-based pricing on Azure; viewer users typically do not require per-user licenses; creators require Pro
Best For	SaaS/product teams embedding analytics; teams needing theming and API-based extensibility	Organizations with semantic modeling (LookML) and Google Cloud alignment; governed metrics layer	Applications needing capacity-based embedded BI and Microsoft ecosystem integration
Key Strength	Focus on embedded analytics and extensibility via APIs/add-ons; data modeling options	Governed modeling and metric consistency; integration with Google Cloud	Clearer capacity model, broad ecosystem, and Microsoft platform familiarity
Key Weakness	Pricing opacity; customization can require development effort; performance and upgrade issues reported in reviews	LookML learning curve; licensing complexity; embedding may require adaptation	Capacity planning required; bias toward Microsoft stack; some embedded scenarios need expertise

01Competitive Landscape

Narrative analysis

Sisense vs Looker:

Looker emphasizes a modeled semantic layer (LookML) and metric governance. Sisense may be more appealing where theming and embedding customization are prioritized, but procurement should validate the depth of theming and whether embedded UX meets product UX requirements.

Sisense vs Power BI Embedded:

Power BI Embedded provides a published capacity pricing model and a broad ecosystem. For SaaS embedding use cases, Power BI Embedded and QuickSight are often considered where predictable unit economics are a procurement priority.

Sisense vs Tableau (embedded/OEM):

Tableau’s internal BI pricing is publicly documented for per-user scenarios, while embedded/OEM agreements typically require negotiation. Community commentary suggests Tableau has a large user community and ecosystem, though embedded licensing can be complex.

Adoption/market perception

G2 rankings and vendor announcements position Sisense in the Embedded BI category; these signals are directional and should be considered alongside independent market-share data and customer references.

	Sisense	Looker (Google Cloud)	Microsoft Power BI Embedded
Starting Price	Custom quote (not publicly standardized)	Published structure by edition on Google Cloud pricing page (exact totals depend on contract)	Published capacity-based pricing on Azure; viewer users typically do not require per-user licenses; creators require Pro
Best For	SaaS/product teams embedding analytics; teams needing theming and API-based extensibility	Organizations with semantic modeling (LookML) and Google Cloud alignment; governed metrics layer	Applications needing capacity-based embedded BI and Microsoft ecosystem integration
Key Strength	Focus on embedded analytics and extensibility via APIs/add-ons; data modeling options	Governed modeling and metric consistency; integration with Google Cloud	Clearer capacity model, broad ecosystem, and Microsoft platform familiarity
Key Weakness	Pricing opacity; customization can require development effort; performance and upgrade issues reported in reviews	LookML learning curve; licensing complexity; embedding may require adaptation	Capacity planning required; bias toward Microsoft stack; some embedded scenarios need expertise