Graas

Name: Graas
Brand: Graas

A Graas Company - Check out our new LinkedIn page @graas

Est 2012|Raised $49M|2 Ref Customers

LinkedIn 11K|Web traffic 18K|FTEs 269|HQ Outram, Central, SG

MarketsEcommerce Marketing Measurement NL & Conversational Analytics

01Competitive Landscape

Narrative analysis

- Graas and Triple Whale serve different primary use cases: Triple Whale is oriented toward Shopify-centric DTC analytics and attribution, while Graas emphasizes unified commerce data and agent-based workflows with explicit marketplace coverage for Shopee, Lazada, Tokopedia, and Flipkart. - Daasity aligns more closely with a modern data stack approach (ETL + warehouse + BI templates). Organizations that prioritize disciplined data modeling and governance may favor a warehouse-centric approach, though this often involves steeper setup and engineering effort. Graas aims to reduce the dashboard and ETL lift by providing commerce-specific connectors and prebuilt metrics, but buyers should validate data model transparency and connector robustness. - General BI platforms like Power BI or search-driven analytics like ThoughtSpot provide broad enterprise analytics capabilities but typically require a separate investment in data pipelines, modeling, and governance. Sources: Competitor public documentation and third-party reviews; Graas product pages.

	Graas (Turbo + agents)	Triple Whale	Daasity
Starting Price	Turbo: free Starter tier; mid-tier paid plans priced in the low hundreds per month; Premium is custom	Free tier available; paid entry tiers start in the low hundreds per month	Pricing not consistently public; third-party sources cite mid-hundreds per month for typical entry tiers
Best For	Multi-channel eCommerce teams (especially India/SEA) seeking unified data and agent-based workflows with some ops automation	Shopify-first DTC brands prioritizing attribution, marketing analytics, and quick setup	Omnichannel brands seeking a structured data warehouse, data modeling, and reporting layer (often Looker-based)
Key Strength	Unified data plus multiple components for analytics, ETL, and automation, with explicit marketplace focus in SEA/India	Strong Shopify ecosystem presence and packaged attribution analytics	Strong data modeling and warehouse-based governance; many integrations
Key Weakness	Limited third-party reviews; limited public security artifacts; some ops features region-limited	Pricing can scale with revenue or GMV; Shopify-centric approach may not cover marketplace complexities	Steeper learning curve; some reviews note data sync and refresh constraints

01Competitive Landscape

Narrative analysis

Graas and Triple Whale serve different primary use cases: Triple Whale is oriented toward Shopify-centric DTC analytics and attribution, while Graas emphasizes unified commerce data and agent-based workflows with explicit marketplace coverage for Shopee, Lazada, Tokopedia, and Flipkart.
Daasity aligns more closely with a modern data stack approach (ETL + warehouse + BI templates). Organizations that prioritize disciplined data modeling and governance may favor a warehouse-centric approach, though this often involves steeper setup and engineering effort. Graas aims to reduce the dashboard and ETL lift by providing commerce-specific connectors and prebuilt metrics, but buyers should validate data model transparency and connector robustness.
General BI platforms like Power BI or search-driven analytics like ThoughtSpot provide broad enterprise analytics capabilities but typically require a separate investment in data pipelines, modeling, and governance.

Sources: Competitor public documentation and third-party reviews; Graas product pages.

	Graas (Turbo + agents)	Triple Whale	Daasity
Starting Price	Turbo: free Starter tier; mid-tier paid plans priced in the low hundreds per month; Premium is custom	Free tier available; paid entry tiers start in the low hundreds per month	Pricing not consistently public; third-party sources cite mid-hundreds per month for typical entry tiers
Best For	Multi-channel eCommerce teams (especially India/SEA) seeking unified data and agent-based workflows with some ops automation	Shopify-first DTC brands prioritizing attribution, marketing analytics, and quick setup	Omnichannel brands seeking a structured data warehouse, data modeling, and reporting layer (often Looker-based)
Key Strength	Unified data plus multiple components for analytics, ETL, and automation, with explicit marketplace focus in SEA/India	Strong Shopify ecosystem presence and packaged attribution analytics	Strong data modeling and warehouse-based governance; many integrations
Key Weakness	Limited third-party reviews; limited public security artifacts; some ops features region-limited	Pricing can scale with revenue or GMV; Shopify-centric approach may not cover marketplace complexities	Steeper learning curve; some reviews note data sync and refresh constraints