AWS SageMaker vs Azure Machine Learning vs Vertex AI: Enterprise Cost Comparison 2026

Why the AI Platform Decision Is Now Primarily Commercial

Two years ago, choosing between AWS SageMaker, Azure Machine Learning, and Google Vertex AI was mainly a technical decision — MLOps tooling maturity, native data integrations, model library depth. In 2025–2026, the three platforms have converged sufficiently on capability that the differentiating variables for enterprise buyers are increasingly commercial: compute pricing for GPU training and inference at production scale, foundation model API economics at billion-token monthly volumes, managed endpoint cost structures, data transfer charges in hybrid architectures, and the flexibility of each vendor's enterprise commitment programmes.

This guide provides the commercial comparison enterprise technology leaders need before committing material AI infrastructure spend to any single hyperscaler. The analysis is calibrated to production-scale enterprise AI — teams running multiple models across business use cases — not experimental workloads. For AWS-specific commercial structure, see our EDP benchmark guide. For Microsoft AI licensing context, see our Microsoft AI licensing guide. Our AWS advisory team can model the full multi-platform economics for your specific AI programme.

GPU Compute Pricing: Training and Inference at Scale

NVIDIA A100 and H100 GPU instances are available on all three platforms, but on-demand availability, pricing, and reserved/committed discount structures differ. AWS p4d (A100, 8-GPU) on-demand runs approximately $32/hour. Comparable Azure NDv4 instances are approximately $29/hour. Google A2 (A100) is approximately $28/hour on-demand. At on-demand rates the compute differential is modest. The economics diverge at committed pricing: AWS Reserved Instances for GPU families deliver 30–40% off on-demand on 1-year terms. Azure Reserved VM Instances achieve similar discounts. Google Committed Use Discounts for A2/A3 instances are negotiated differently and often achieve 40–50% for 1-year commitments.

H100 GPU availability — critical for large LLM training — has been constrained across all three platforms since 2023. Microsoft Azure has benefited from priority H100 access through its OpenAI partnership infrastructure, giving it a practical availability advantage for the largest training workloads through 2025. For inference-only workloads not requiring GPU, AWS Graviton3 instances offer compelling cost-per-inference-token economics for transformer model serving versus x86 alternatives — a AWS-specific advantage with no direct equivalent on Azure or GCP for CPU-based inference.

Foundation Model Access: Bedrock vs Azure OpenAI vs Vertex AI

The most commercially significant differentiator in 2025–2026 is foundation model access economics. AWS Bedrock provides access to Anthropic Claude (3.5 Sonnet, Opus, Haiku), Meta Llama, Mistral, Cohere, and Amazon Nova models. Azure OpenAI provides access to GPT-4o, o1, and GPT-4 Turbo. Vertex AI provides access to Gemini Ultra, Pro, and Flash, plus a model garden including Llama and Mistral. Pricing is consumption-based across all three — per input and output token.

At enterprise scale — hundreds of millions to billions of tokens per month — per-model pricing differences create 2–3x cost variation between platforms for workloads at similar capability levels. Claude 3.5 Sonnet on Bedrock, GPT-4o on Azure OpenAI, and Gemini 1.5 Pro on Vertex AI are broadly comparable in capability, but their per-million-token prices differ in ways that are material at production scale. Enterprise provisioned throughput (reserved capacity for foundation model inference) is available on all three platforms at 30–50% discounts versus on-demand token pricing for 1-month commitments. For organisations with a Microsoft Enterprise Agreement, Azure OpenAI provisioned throughput can sometimes be incorporated into EA negotiations — a structural advantage that AWS and Google cannot match for enterprises already deep in the Microsoft commercial relationship.

MLOps Tooling, Endpoint Economics, and Data Transfer Costs

At the MLOps layer, SageMaker, Azure ML, and Vertex AI have each invested heavily and now offer comparable capabilities for model training orchestration, experiment tracking, feature stores, and model registries. The commercial differences are in the billing architecture. SageMaker managed online endpoints bill per inference instance-hour — a minimum of one instance per endpoint even at zero traffic, creating a baseline idle cost for each production model. SageMaker Serverless Inference resolves this for low-traffic endpoints but carries higher per-request latency unsuitable for user-facing workloads. Azure ML managed online endpoints have a similar instance-based billing model. Vertex AI Prediction has historically been more aggressive on idle cost management for managed endpoints, though AWS improvements in 2024 have narrowed the gap for most workload profiles.

Data transfer costs are the most consistently underestimated component of AI platform economics in hybrid architectures. Training workloads pulling petabytes of data from on-premise storage, or inference workloads serving results to applications on a different cloud, incur egress charges at $0.08–$0.09/GB on all three platforms. For enterprises with data in AWS S3 running Azure ML training, or BigQuery data running SageMaker inference, the cross-platform transfer cost can exceed the compute cost on large dataset workloads. Any multi-cloud AI architecture must explicitly model data gravity economics — the platform closest to the majority of your training data is usually the right cost choice regardless of other factors. For procurement of third-party MLOps tools (Weights and Biases, Comet, MLflow), see our AWS Marketplace vs direct procurement guide for guidance on private offer structuring. Book a call with our cloud advisory team to model the full economics for your platform decision.