Independent comparison for enterprise buyers. Updated May 2026.
Quick verdict: Choose AWS SageMaker when the organisation is standardised on AWS and the priority is the broadest end-to-end ML platform with the largest distributed training fleet and deepest catalogue of foundation models via Bedrock. Choose Google Vertex AI when the organisation is on Google Cloud, when Gemini is part of the AI strategy, or when the workload benefits from BigQuery-native ML and Google's data tooling. The differentiator is cloud alignment combined with foundation model access: SageMaker leads Bedrock-distributed models, Vertex leads Gemini and Google-native data integration.
| Criteria | AWS SageMaker | Google Vertex AI |
|---|---|---|
| Editorial score | 4.4 / 5.0 | 4.4 / 5.0 |
| Hosting Model | AWS-managed PaaS | Google Cloud-managed PaaS |
| Training | Distributed training, SageMaker HyperPod | Distributed training, Vertex AI Training |
| Deployment | Real-time, batch, async, serverless endpoints | Online prediction, batch, private endpoints |
| Foundation Model Access | Bedrock (Claude, Llama, Mistral, Cohere, Titan) | Vertex Model Garden (Gemini, Claude, Llama) |
| Data Integration | S3, Glue, Athena, Redshift | BigQuery, Cloud Storage, Dataplex |
| Key Strength | Platform depth and breadth of Bedrock models | Gemini integration and BigQuery-native ML |
| Key Limitation | Complex pricing, steeper learning curve | Narrower regional footprint than AWS |
AWS SageMaker and Google Vertex AI are the two leading hyperscaler ML platforms outside Azure ML. Both offer end-to-end MLOps capability and integrated access to foundation models. The choice typically follows underlying cloud commitment and foundation model strategy.
SageMaker provides SageMaker Studio (IDE), Canvas (low-code), Ground Truth (labelling), Feature Store, Pipelines, Model Registry, JumpStart (pre-trained models), HyperPod for frontier-scale distributed training, and SageMaker Clarify for bias and explainability. Foundation model access through AWS Bedrock covers Claude, Llama, Mistral, Cohere, Stability, and AWS Titan models.
Vertex AI provides Vertex AI Workbench (IDE), AutoML, Vertex AI Pipelines, Feature Store, Model Registry, Model Garden, and tight integration with BigQuery ML for SQL-native machine learning over warehouse data. Foundation model access through Vertex Model Garden covers Gemini (Google's frontier family), Claude via partnership, Llama, Mistral, and Google's open-weight Gemma family.
On platform capability depth, SageMaker generally has a broader and deeper feature surface, particularly for large-scale distributed training and the variety of inference deployment patterns (real-time, batch, async, serverless, multi-model endpoints). Vertex generally has cleaner integration with the surrounding Google Cloud data stack, particularly BigQuery, which can materially simplify the data-to-model path for organisations whose data lives in BigQuery.
On foundation model strategy, SageMaker + Bedrock typically wins on catalogue breadth. Vertex typically wins where Gemini's long-context multimodal capability or BigQuery ML integration is strategic. On enterprise controls, both inherit hyperscaler compliance (SOC 2, ISO 27001, HIPAA, FedRAMP for AWS, ISO 27018 for Google, plus regional residency). Compliance differences are typically minor and workload-specific.
Both platforms price per resource consumed. SageMaker prices ml.m5.large training at approximately $0.115 per hour and ml.p4d.24xlarge GPU training at approximately $32.77 per hour on-demand. Real-time inference endpoints add a 20-40% SageMaker premium over the underlying EC2 rate. Vertex AI prices a2-highgpu-1g (A100) at approximately $3.67 per hour and prediction endpoints at compute-plus-management rates broadly comparable to SageMaker.
A buying-side caveat applies to both: total cost is dominated by sustained inference endpoint utilisation, training cluster hours, and data egress. Reserved capacity (AWS Savings Plans, Google Committed Use Discounts) can reduce sustained workload cost 30-60%. Foundation model spend through Bedrock or Vertex Model Garden is metered separately on a per-token basis and frequently becomes the largest line item once generative AI workloads scale. Enterprise buyers should model end-to-end cost across compute, storage, foundation model tokens, and data movement rather than picking by headline compute rates.
Choose AWS SageMaker when the organisation is standardised on AWS, when the workload requires the broadest distributed training capability (HyperPod, EFA networking, P5 instances), when Bedrock catalogue breadth (Claude, Llama, Mistral, Cohere, Titan) is strategic, or when the data estate is already in S3, Glue, Redshift, or Athena. SageMaker typically wins where AWS commitment is established and the AI strategy spans classical ML, deep learning, and a wide foundation model portfolio.
Choose Google Vertex AI when the organisation is on Google Cloud, when Gemini is part of the AI strategy (long context, native multimodal, video understanding), when the data estate is in BigQuery and BigQuery ML's SQL-native ML simplifies the path to production, or when Google's data tooling (Dataplex, Looker) is the BI layer. Vertex typically wins where Google Cloud commitment, Gemini dependency, or BigQuery-native ML outweigh SageMaker's broader platform surface.
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