Amazon Finspace
Current- Annual cost
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- Seats assigned
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This catalog view compares public market data and does not include organization spend or usage.

Sas Viya is the strongest catalog alternative to evaluate for Amazon Finspace.

This catalog view compares public market data and does not include organization spend or usage.
See how Amazon Finspace compares to 2 alternative apps you can switch to.
Sas ViyaBest FitSeat-basedAvg. switch cost$648/yr•$54/month
DatabricksWorth ConsideringPublic pricingAvg. switch cost--Databricks is a good fit if: your team processes large-scale data and needs unified analytics and AI capabilities.Sas Viya and Databricks selected for comparison.
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On-Demand
Public catalog pricing. Organization spend is not included.
$3,600
SAS Viya Workbench ($54/month)
Public catalog pricing estimate.
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Standard
Public catalog pricing estimate.
$0
No migration in the current app baseline.
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After $75,000 estimated migration cost.
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After $50,000 estimated migration cost.
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70/100
Built for regulated enterprises, excessive for ad-hoc analysis teams
75/100
Great for Data/ML teams, weak for simple reporting needs
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Public adoption signal for the current app.
high
Sas Viya is a high-complexity migration. Estimated 12 weeks and $75,000 one-time cost.
high
Databricks is a high-complexity migration. Estimated 12 weeks and $50,000 one-time cost.
4.5/5.0
65 reviews
4.3/5.0
187 reviews
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2450 reviews
No migration needed
12 weeks
High
Sas Viya is a high-complexity migration. Estimated 12 weeks and $75,000 one-time cost.
12 weeks
High
Databricks is a high-complexity migration. Estimated 12 weeks and $50,000 one-time cost.
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$75,000
Estimated one-time migration and setup effort.
$50,000
Estimated one-time migration and setup effort.
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Supports 250 native integrations.
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Supports 500 native integrations.
100%
85%
70%
You already standardize on this app.
You require enterprise-grade model governance and operate in heavily regulated industries
Your team processes large-scale data and needs unified analytics and AI capabilities
Public pricing or fit signals no longer match your needs.
Your data scientists prefer open-source flexibility and rapid prototyping without vendor lock-in
Your data volumes are small or your team lacks data engineering and Spark expertise
