SageMaker vs Vertex AI : 2026

AWS SageMaker et Google Vertex AI sont les plateformes ML enterprise cloud-native 2026. Convergence features : training distribué, serving managed, AutoML, feature store, LLM hosting. Choix dépend cloud strategy entreprise.

TL;DR

- SageMaker AWS et Vertex AI GCP : leaders ML enterprise.

- Convergence : training, serving, AutoML, feature store, LLMs.

- Pricing complexes : compute + storage + inference.

- Africa : SageMaker domine AWS-heavy, Vertex AI gagne GCP-native.

AWS SageMaker

Composants

• Studio : IDE notebooks managed
• Training Jobs : distributed training
• Endpoints : real-time serving + batch transform
• AutoPilot : AutoML
• Feature Store : online + offline
• Model Monitor : drift detection
• Pipelines : MLOps orchestration
• JumpStart : pre-trained models + foundation models
• Bedrock integration : LLM access

Pricing typique

• Training instances : ml.p4d.24xlarge (8× A100) = $32/h
• Inference : ml.g5.xlarge = $1.4/h
• Studio notebooks : ml.t3.medium = $0.05/h
• Storage : $0.14/GB/mois
• Africa typical : $500-5K/mois petites équipes, $50K-500K/mois enterprise

Forces

• Mature, écosystème AWS
• 100+ instance types (CPU, GPU, Inf, Trn)
• Inferentia / Trainium custom chips (économies)
• Integration S3, IAM, KMS native
• Africa : adoption forte (Cape Town région)

Google Vertex AI

Composants

• Workbench : notebooks JupyterLab
• Training : custom jobs, AutoML
• Endpoints : managed serving
• Feature Store : online + offline
• Pipelines : Kubeflow-based
• Model Garden : foundation models (Gemini, Llama, Claude via partners)
• Evaluation : LLM evals
• Agent Builder : conversational AI

Pricing typique

• Training compute : a2-highgpu-1g (1× A100) = $3.7/h
• TPU v5p : $5/h-hour (les + puissantes Google chips)
• Endpoints : $1.5-3/h instance + $0.0001/request
• AutoML : $20-100/training job
• Africa typical : $300-3K/mois petites équipes, $30K-300K/mois enterprise

Forces

• TPUs (training LLM massif moins cher)
• Gemini natif (LLM Google)
• BigQuery ML integration (warehouse-native ML)
• Vertex AI Agent Builder pour conversational
• DeepMind research transfer

Comparaison structurée 2026

Use cases Africa

SageMaker gagne

• AWS-heavy infrastructures (banques)
• Edge ML (SageMaker Edge Manager)
• ML existant à migrer

Vertex AI gagne

• GCP-native startups
• BigQuery analytics → ML natif
• LLMs Gemini desired
• Multi-modal (Imagen, Veo)

Alternatives 2026

• Azure ML : Microsoft cloud ML (Azure-native)
• Databricks ML : data + ML unified (cf U1/3)
• HuggingFace : open-source friendly (cf U2/5)
• Modal Labs : serverless ML/AI compute
• Anyscale (Ray) : distributed compute
• RunPod, Vast.ai : GPU cloud cheap

Pattern training 2026

SageMaker

`python

from sagemaker.huggingface import HuggingFace

estimator = HuggingFace(

entry_point='train.py',

instance_type='ml.p3.2xlarge',

transformers_version='4.36',

py_version='py310',

)

estimator.fit('s3://bucket/data/')

`

Vertex AI

`python

from google.cloud import aiplatform

job = aiplatform.CustomTrainingJob(

display_name='fraud-detection',

script_path='train.py',

container_uri='gcr.io/cloud-aiplatform/training/pytorch-gpu.1-13:latest',

)

job.run(machine_type='n1-highmem-8', accelerator_type='NVIDIA_TESLA_A100')

`

FAQ

Q : Startup Africa choisir ?

R : Vertex AI free tier + Gemini. SageMaker si AWS déjà en place. Sinon Modal Labs / RunPod plus économiques pour POCs.

Q : Coût LLM serving Africa ?

R : 1B token/mois inference Llama 70B : SageMaker ~$5K, Vertex AI ~$4K, Modal Labs ~$3K, self-host GPU (RunPod) ~$1.5K + ops.

Q : Compliance Africa ?

R : SageMaker (Cape Town région) et Vertex AI (Johannesburg) ont data residency Africa. POPIA / NDPR compatibles via configs.

Conclusion

SageMaker vs Vertex AI 2026 : convergence features, choix par cloud strategy. SageMaker AWS-heavy (banques), Vertex AI GCP-native (e-commerce, media). Free tier Vertex AI gagne startups Africa. Pour POCs économiques, Modal Labs / RunPod / HuggingFace Spaces. LLMs hosting devient feature commune.