How VMware Tanzu and vSAN Combine to Keep Agentic AI Close to Enterprise Data

# Overview Enterprises deploying autonomous, multi‑step AI agents need immediate, accurate access to proprietary data across formats and ages. VMware proposes a unified on‑premises stack—VMware Tanzu Data Intelligence (including Greenplum) on VMware Cloud Foundation with vSAN—to bring AI compute to the storage layer rather than moving massive datasets to the public cloud.

# The problem this architecture addresses Agentic AI requires low‑latency, high‑fidelity access to both structured and unstructured data. Moving petabyte‑scale enterprise data to public clouds creates latency, high egress costs, and compliance risks. Leaving data siloed on legacy systems introduces the same problems in reverse. The VMware approach eliminates that tradeoff by colocating compute and storage on a single platform.

# How the architecture is assembled The architecture relies on vSAN as a unified storage layer and Tanzu Greenplum for data processing and vectorization. Key storage roles are separated by protocol:

• File storage: holds model weights and binaries.
• Block storage: hosts vector databases and indexes that demand high IOPS for similarity searches.
• S3‑compatible object storage: stores large, unstructured data lakes (documents, logs, media) used as context for models.

vSAN's S3 object support is mentioned as a tech preview with VMware Cloud Foundation 9.1.1.

# Data lifecycle and runtime flow

1. 1Raw enterprise data—contracts, support logs, records—lands in vSAN S3 object storage.
2. 2Tanzu Greenplum processes and vectorizes that raw text into embeddings.
3. 3Embeddings are stored on vSAN block storage to support high‑performance similarity searches.
4. 4Agentic AI models stored on vSAN file shares can query raw content and the vector index simultaneously.

This creates a closed‑loop AI engine that runs inside the corporate perimeter, minimizing latency and avoiding external data transfers.

# Hot and warm tiering strategy The architecture separates "hot" and "warm" data by access patterns and age:

• Hot data (typically recent) resides on NVMe/TLC devices backing vSAN block storage, optimized for high write loads and frequent, demanding queries.
• Warm data (older) migrates to QLC‑based, read‑optimized object tiers using S3‑compatible stores and open table formats like Apache Iceberg, with deduplication to reduce cost while keeping data queryable.

This tiering allows teams to run analytics that cross‑query current operational datasets and years of historical records without moving data to the cloud.

# Practical outcomes for enterprises

• Lower latency for agentic AI inference and real‑time workflows by executing compute adjacent to storage.
• Reduced cloud egress costs because large datasets remain on‑premises instead of being transferred to public cloud AI services.
• Better compliance and data sovereignty since proprietary and regulated data can be kept behind the enterprise firewall.

# Implementation notes The brief highlights Tanzu Greenplum integrated with vSAN to handle vectorization and storage placement decisions. vSAN's unified support for file, block, and S3 protocols enables consistent operational management across the AI data lifecycle.

# Bottom line For organizations that must keep sensitive, high‑volume data on‑premises, this unified Tanzu-plus‑vSAN pattern offers a way to run agentic AI with lower latency, controlled costs, and retained sovereignty by bringing compute to data rather than moving data to remote AI services.