Vertex AI Overview
Vertex AI (Latest) by Google provides GPU-accelerated inference with features: Managed models, Gemini, Claude, Llama, Model Garden, endpoint deployment, model registry... It achieves N/A (managed), pay per hour or token throughput on H100 GPUs. License: Commercial, GCP integrated.
Performance Benchmarks
On H100 80GB with Llama 4 Scout (17B) at FP8: prefill throughput: 15,304 tokens/second; decode throughput: 896 tokens/second per user with 1863 max batch; TTFT (time to first token): 20ms; inter-token latency: 17ms. GPU utilization: N/A (managed).
Feature Comparison
Key features: Managed models, Gemini, Claude, Llama, Model Garden, endpoint deployment, model registry... Unique strengths: Managed models Gemini Claude. Production features include: multi-LoRA, adapter routing, model management.
Cost-Per-Token Analysis
Cost-per-token on H100 80GB at $2.50/hr: input tokens: $0.000030/1K tokens; output tokens: $0.000049/1K tokens with Vertex AI. At 50% utilization, cost-per-million tokens: $91-$229 for output tokens, depending on batch size and model size. Reserved pricing reduces costs by 30-50%.
Production Deployment
Deploy Vertex AI in production: containerized deployment with Docker + NVIDIA Container Toolkit; Kubernetes with GPU node pools; monitoring with Prometheus + GPU metrics; horizontal scaling with Kubernetes HPA + VPA; and CI/CD integration for model updates. Recommended: 6x H100/B200 GPUs per node with NVLink.
When to Choose Vertex AI
Choose Vertex AI when: Managed models Gemini are critical for your workloads; Commercial, GCP integrated license model fits your budget; and your team has experience with Google's ecosystem. Consider alternatives when: specific hardware optimization is needed, team familiarity with other frameworks, or license costs are prohibitive for your scale.