Zoho’s Vertical AI Integration Strategy: From Silicon to Intelligence
At ZohoDay25, Ram Prakash Ramamoorthy, AI Research at Zoho & ManageEngine, provided a "kitchen tour" of the underlying architecture powering the Zoho ecosystem. While the industry standard for SaaS involves operating as a tenant on third-party public clouds, Zoho is executing a strategy of vertical integration that extends from custom-designed hardware up to specialized AI models. It is important to clarify that this is not an initiative asking customers to move their data or change their deployment models; rather, Zoho is using its own infrastructure to insulate users from the rising costs and complexities of the modern cloud.
In my calls with CIOs, there is a recurring confusion regarding the total cost of ownership for AI. Many vendors currently employ a pricing structure that combines a fixed seat cost with an unpredictable variable for tokens, creating significant friction. Zoho's strategy aims to remove this uncertainty. CIOs frequently ask me how to manage compute and token costs during implementation; Zoho is answering by owning the layers where those costs originate.
The Economics of Vertical Compounding
The core of the strategy centers on the concept of "foundations that compound." While many SaaS providers innovate at the surface level—focusing on UI or pricing—Zoho is moving deeper into the stack. Ramamoorthy noted that in a "deep red ocean" market, the foundation becomes the ultimate differentiator. By owning the layers beneath the application, Zoho avoids the "supply chain shocks" inherent in relying on third-party hyperscalers.
A multi-layered architectural approach where hardware, software, and AI models are integrated to ensure cost control, technical sovereignty, and predictable performance across the entire business suite.
Designing custom servers allows for direct optimization of compute efficiency, critical for high-load AI workloads.
12-18% Energy GainRemoving the dependency on third-party public cloud margins translates to fixed, predictable pricing for enterprise customers.
Decoupled from Token FluctuationLocalized data centers allow AI to run closer to the customer, improving speed and adhering to data sovereignty laws.
Privacy-First ArchitectureIn-house design of motherboards, chassis, and firmware tailored specifically for SaaS and AI workloads.
10,000 Units by 2026Hardware Innovation & Tiered AI Strategy
One of the most significant reveals was Zoho's move into custom server hardware. These proprietary servers are designed in-house to balance high-intensity AI tasks with standard virtual machine operations. This is paired with a "right-sizing" model approach that favors contextual intelligence over the pursuit of massive, generalized models for every task.
Tiered AI Ecosystem
Zoho’s AI approach, embodied in Zia, utilizes a hierarchy of models to prioritize efficiency, speed, and data privacy.
3B parameter models for specialized tasks like receipt parsing and data extraction.
Models ranging from 1.3B to 7B, with MoE architectures reaching up to 100B.
The Shashi Take: Physical AI and Edge Processing
During our discussions on the future of Physical AI, Tony Thomas, Chief Information Officer at Zoho, answered my question if all businesses will have a Datacenter in their premises. He thinks that the next phase of this strategy extends beyond the data center. He indicated an expectation that a significant amount of processing will eventually occur on-premises, utilizing the compute power of laptops or other devices located directly within the business facility.
How Zoho's App OS could manage workloads between the cloud and local hardware to reduce latency and cost.
The potential for government and highly regulated sectors to run full-stack Zoho on-premise without cloud leaks.
What Does This Mean for the Next Five Years?
Zoho’s move into hardware and custom models is a defensive and offensive play. It provides a moat against "AI inflation" while ensuring that the infrastructure can support the next decade of growth. For the customer, this means a more responsive system and a predictable cost structure, regardless of how the broader cloud market fluctuates. The focus on edge processing further reinforces that the future of enterprise technology is not just in the cloud, but wherever the work is actually happening. CIOs should plan for a hybrid future where the distinction between rented cloud power and owned local compute becomes increasingly blurred.
