AWS SageMaker vs Google Vertex AI

Cons

Cons

• ✗Pricing complexity is high — different billing models for predictions, training, storage, and per-token API calls make cost forecasting difficult without dedicated FinOps monitoring
• ✗Ecosystem lock-in to Google Cloud; migrating trained models, pipelines, and Feature Store data to another cloud provider requires significant re-engineering
• ✗Documentation can be fragmented and inconsistent across the many sub-products (AI Studio, Agent Builder, Pipelines, AutoML), creating a steep learning curve for new users
• ✗Cold-start latency for online prediction endpoints can be significant (minutes) when scaling from zero, which is problematic for latency-sensitive applications without provisioned capacity
• ✗Some advanced features like provisioned throughput and certain Gemini model variants are restricted to specific regions, limiting availability for global deployments
• ✗Third-party model availability in Model Garden can lag behind direct provider APIs — new model releases from Anthropic, Meta, or Mistral may not be immediately available on Vertex