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+ Can run on-premise

+ Vendor is publicly listed — audited financial disclosure

+ Largest vendor (270,000 employees)

+ Longest track record — vendor founded 1911

+ Broadest feature coverage (9)

+ Only one here with Workflow automation

+ Can run on-premise

+ Vendor is publicly listed — audited financial disclosure

− Lacks SOC 2, ISO 27001, HIPAA — offered by others here

− Narrowest feature scope (6)

− Cloud only

− Smallest vendor (4,000 employees) — check support capacity

− Lacks SOC 2, ISO 27001, HIPAA — offered by others here

− Lacks Dashboards — offered by others here

− Cloud only

+ Combines AI studio, data store and governance

+ Strong focus on AI governance and compliance

+ Flexible cloud, on-premise and hybrid deployment

+ Unified lakehouse for data engineering and AI

+ Built on open formats like Delta Lake and Spark

+ Strong collaborative notebooks and ML tooling

+ Visual and code-based workflows in one platform

+ Enables collaboration between analysts and data scientists

+ Flexible deployment across clouds and on-premise

+ End-to-end ML and generative AI tooling

+ Access to Gemini and third-party models

+ Integrates with BigQuery and Google Cloud services

− Complex product portfolio to navigate

− Requires specialist skills to implement

− Costs can be hard to estimate

− Usage-based costs can be hard to predict

− Requires data engineering expertise

− Cluster tuning and governance need attention

− Licensing is expensive for smaller teams

− Platform breadth brings a learning curve

− Performance depends on underlying infrastructure

− Complex for teams new to Google Cloud

− Usage-based costs can escalate

− Rapidly changing features and documentation