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+ Only one here with Custom reports

+ Can run on-premise

+ Can run on-premise

+ Vendor is publicly listed — audited financial disclosure

+ Largest vendor (270,000 employees)

+ Longest track record — vendor founded 1911

+ Only one here with SOC 2, ISO 27001, HIPAA

+ Broadest feature coverage (9)

+ Only one here with Workflow automation

+ Can run on-premise

− Narrowest feature scope (6)

− Cloud only

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

+ Comprehensive statistical and machine learning toolset

+ Supports both visual tools and code in SAS, Python and R

+ Strong model governance and decisioning capabilities

+ 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

− Licensing can be expensive compared with open-source tools

− Learning curve for SAS-specific concepts

− Platform administration can be complex

− 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