Comparisons

How EkamFlow compares.

Honest, side-by-side comparisons with the platforms and approaches teams evaluate when they're shopping for customer decisioning. We'll tell you when the alternative is genuinely the right call.

EkamFlow vs

AWS SageMaker

AWS SageMaker is a full MLOps platform for teams building custom models from scratch. EkamFlow is a customer decisioning layer — churn, LTV, next best action, next best offer, recommendations, lead scoring and 8 more predictions — trained on your data and served through one API. No ML team required.

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EkamFlow vs

Google Vertex AI

Google Vertex AI is a full ML platform for teams building custom models on Google Cloud. EkamFlow is a customer decisioning layer — churn, LTV, next best action, recommendations, lead scoring and 9 more predictions — trained on your data and served through one API. No ML team required.

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EkamFlow vs

Build in-house

The classic build-vs-buy question for customer decisioning ML. Building in-house means 6-12 months to production, a $500k-$1M/year ML team, and ongoing maintenance burden. EkamFlow gives you 13 pre-built customer-decisioning use cases in weeks, for less than the cost of one ML engineer.

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