Google Cloud AI Platform
Integrations
- BigQuery (Zero-copy)
- Apigee API Registry
- Salesforce Agentforce (A2A)
- ServiceNow (A2A)
- NVIDIA NeMo
- Ray on Vertex AI
Pricing Details
- Billing is based on token consumption (Gemini API), compute node uptime (vCPU/GPU/TPU), and Flex-start VM usage.
- Grounding with Google Search is billed as a separate feature as of January 2025.
Features
- Gemini 3 and 2.5 Stable Models
- Vertex AI Agent Builder (A2A & MCP)
- Model Garden (200+ Foundation Models)
- Dynamic Workload Scheduler (Flex-start VMs)
- Agent Engine Memory Bank & Code Execution
- Enterprise Security (Model Armor & Private VPC)
Description
Vertex AI & Agentic Orchestration Infrastructure Review
The 2026 iteration of Vertex AI serves as an Agentic Orchestration Layer, centered on the Vertex AI Agent Builder and the open Agent-to-Agent (A2A) protocol. This standard allows Vertex agents to collaborate securely with agents from external ecosystems (Salesforce, ServiceNow, UiPath) regardless of the underlying framework 📑.
Model Orchestration & Agentic AI
The Model Garden provides a curated library of 200+ foundation models, including the latest Gemini 3 and Gemini 2.5 stable releases.
- Multimodal Live Ingestion: Input: Real-time bidirectional audio/video streams → Process: Low-latency inference via Gemini Live 2.5 Flash API → Output: Context-aware multimodal responses with sub-second latency 📑.
- A2A Orchestration: Input: High-level goal requiring cross-platform data → Process: Supervisor Agent negotiates with external agents via A2A protocol and ApiRegistry tools → Output: Autonomous task completion across heterogeneous ecosystems 📑.
- Model Garden Fine-tuning: Supports managed LoRA and full-domain specialization for Gemini and open-source models like Llama 4; however, hardware-level scheduling priorities within the AI Hypercomputer remain undisclosed 🌑.
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Infrastructure & Trust Layer
The 2026 architecture leverages TPU v5p hardware and the Dynamic Workload Scheduler (DWS) for resource efficiency.
- DWS Flex-Start VMs: Provides cost-optimized inference for short-duration workloads by scheduling capacity on reserved accelerator clusters during idle cycles 📑.
- Agent Engine & Memory Bank: Offers a managed runtime with a persistent 'Memory Bank' for agentic long-term context retention and code execution in isolated sandboxes 📑.
- Security Guardrails: Integrates Model Armor for prompt injection protection and Private Service Connect for VPC-isolated agent deployments 📑.
Evaluation Guidance
Technical evaluators should verify the following architectural characteristics:
- A2A Negotiation Latency: Benchmark the handshake and capability-negotiation overhead between Vertex AI agents and third-party A2A-compliant frameworks 🌑.
- Flex-Start VM Availability: Validate the typical wait times for Flex-start VM allocation across different geographical zones to ensure alignment with batch inference SLAs 🧠.
- Tool Governance: Audit the ApiRegistry configuration to ensure that agent-accessible tools comply with enterprise-wide security and data access policies 📑.
Release History
Year-end update: Release of the Autonomous Model Hub. Features 500+ open and proprietary models with automatic fine-tuning for specific industry tasks.
GA release of Gemini 1.5 Pro with 2-million-token context window. Enhanced multimodal reasoning and audio analysis support.
Launch of Agent Builder. A low-code environment to build and deploy generative AI agents grounded in enterprise data (RAG).
Integration of the Gemini family. Added multimodal capabilities (text, image, video, code) to Vertex AI with enterprise-grade safety.
Introduction of GenAI support. Launched Model Garden with PaLM 2, Imagen, and Codey models. Released Generative AI Studio.
Major shift: Launch of Vertex AI. Unified AI Platform and AutoML into a single UI and API. Introduced Pipelines and Feature Store.
Rebranded to AI Platform. Introduced AI Platform Notebooks and Data Labeling Service to support the full ML lifecycle.
Initial release of Google Cloud Machine Learning Engine. Provided managed TensorFlow training and prediction at scale.
Tool Pros and Cons
Pros
- Scalable ML infrastructure
- Integrated ML tools
- Multi-framework support
- Easy Python integration
- Automated model development
- Real-time deployment
- Robust data processing
- Simplified training
Cons
- Complex setup
- Potential cost
- Vendor lock-in
