MoBagel

Name: MoBagel
Brand: MoBagel

Build AI Together and Beyond

Est 2015|Valuation $48M|Raised $21.1M|40 Ref Customers

LinkedIn 2K|Web traffic 3K|FTEs 77|HQ San Jose, California, US

MarketsPersonalization Engines Ecommerce Marketing Measurement

01Competitive Landscape

Narrative analysis

- **MoBagel vs Dataiku:** Dataiku is broadly adopted and has established governance narratives and public documentation regarding features and enterprise plans. MoBagel may be of interest for organizations seeking more packaged delivery for specific use cases (marketing, supply chain) and potentially faster time‑to‑value, but it has less visible independent review validation. - **MoBagel vs H2O.ai:** H2O.ai is known for an AutoML focus and open-source foundations. MoBagel emphasizes no-code collaboration and packaging around business workflows. Organizations with strong engineering teams may prefer H2O for flexibility; those seeking packaged business workflows may consider MoBagel. - **MoBagel vs DataRobot (adjacent):** DataRobot emphasizes enterprise automation, deployment, and governance. MoBagel’s public pricing is clearer for the marketing audience product but not for the enterprise AI platform.

Market share/adoption signals (caveats)

Vendor materials include large customer and adoption claims that were not independently audited in the reviewed sources; treat these as directional indicators rather than verified market-share metrics.

	MoBagel (Decanter AI / AI Audience)	Dataiku	H2O.ai (H2O AI Cloud / Driverless AI)
Starting Price	AI Audience: tiered, visitor-based pricing in the low- to mid-thousands per month for common bands; enterprise platform pricing is not public.	Free edition available; paid tiers are enterprise and require sales engagement.	Free trial available; enterprise pricing varies and can be higher for GPU/enterprise bundles.
Best For	Organizations seeking packaged "agentic AI" features with no-code modeling and a separate marketing audience optimization product with published visitor-based pricing.	Enterprises needing end-to-end data science and ML collaboration, governance, and broad adoption across teams.	Organizations wanting strong AutoML and flexible deployments, often with technical teams to manage infrastructure.
Key Strength	Specific marketing-use-case packaging (AI Audience) and partner-backed narratives that emphasize speed and cost improvements.	Platform breadth, governance features, and an established adoption and training ecosystem.	Technical flexibility, AutoML capabilities, and an open-source heritage.
Key Weakness	Limited independent end-user review footprint; public security/compliance details are limited; product naming and positioning is fragmented.	Enterprise pricing can be significant; full-platform adoption has a learning curve.	Requires technical expertise for some deployments; enterprise bundles can be costly.

01Competitive Landscape

Narrative analysis

MoBagel vs Dataiku: Dataiku is broadly adopted and has established governance narratives and public documentation regarding features and enterprise plans. MoBagel may be of interest for organizations seeking more packaged delivery for specific use cases (marketing, supply chain) and potentially faster time‑to‑value, but it has less visible independent review validation.
MoBagel vs H2O.ai: H2O.ai is known for an AutoML focus and open-source foundations. MoBagel emphasizes no-code collaboration and packaging around business workflows. Organizations with strong engineering teams may prefer H2O for flexibility; those seeking packaged business workflows may consider MoBagel.
MoBagel vs DataRobot (adjacent): DataRobot emphasizes enterprise automation, deployment, and governance. MoBagel’s public pricing is clearer for the marketing audience product but not for the enterprise AI platform.

Market share/adoption signals (caveats)

	MoBagel (Decanter AI / AI Audience)	Dataiku	H2O.ai (H2O AI Cloud / Driverless AI)
Starting Price	AI Audience: tiered, visitor-based pricing in the low- to mid-thousands per month for common bands; enterprise platform pricing is not public.	Free edition available; paid tiers are enterprise and require sales engagement.	Free trial available; enterprise pricing varies and can be higher for GPU/enterprise bundles.
Best For	Organizations seeking packaged "agentic AI" features with no-code modeling and a separate marketing audience optimization product with published visitor-based pricing.	Enterprises needing end-to-end data science and ML collaboration, governance, and broad adoption across teams.	Organizations wanting strong AutoML and flexible deployments, often with technical teams to manage infrastructure.
Key Strength	Specific marketing-use-case packaging (AI Audience) and partner-backed narratives that emphasize speed and cost improvements.	Platform breadth, governance features, and an established adoption and training ecosystem.	Technical flexibility, AutoML capabilities, and an open-source heritage.
Key Weakness	Limited independent end-user review footprint; public security/compliance details are limited; product naming and positioning is fragmented.	Enterprise pricing can be significant; full-platform adoption has a learning curve.	Requires technical expertise for some deployments; enterprise bundles can be costly.