There is a practical question I keep hearing from customers and partners.
A customer completes a successful AI pilot. As the organization begins moving that pilot into production, two questions come up: How do we manage token costs, and how do we protect our intellectual property and data?
This is when customers begin considering an on-prem solution such as Cisco Secure AI Factory. They understand the concept and why enterprises need AI infrastructure that is secure, secure, and under their control.
Then they ask the questions that determine whether the initiative moves forward:
How do we design Secure AI Factory to meet our business requirements, and how do we implement it in our environment?
The architecture is clear. Implementation is the challenge.
NVIDIA gave the industry the AI factory concept. Tokens drive inference from large language models, and if enterprises are going to use AI broadly, they need a factory capable of generating those tokens.
Cisco offers a secure, on-prem version of that factory for enterprises.
Secure AI Factory brings together the infrastructure enterprises need: NVIDIA GPUs, Cisco networking and security, storage, observability, and the controls required to run AI in a production environment.
That matters because AI is not simply another application placed on the network and monitored from the outside. It changes the network itself.
You have a front-end network that gives users access to AI applications. You have a back-end network that supports GPU-to-GPU communication. You also have models, agents, data pipelines, inference traffic, identities, and token consumption. All of these components must work together.
The architecture must be designed correctly from the beginning. Once AI enters production, weaknesses in the underlying infrastructure become visible very quickly.
Production AI fails in the places people do not plan for
Most organizations do not fail because they lack interest in AI. They fail because a pilot and a production environment are fundamentally different problems.
In a pilot, you might process 100 invoices a day. In production, the question becomes whether you can process 10,000 reliably, securely, and efficiently.
Moving to that scale requires organizations to consider several layers of the AI architecture:
- Server infrastructure: GPUs, memory, storage, and networking
- Supporting infrastructure: Power and cooling
- AI applications: Models, data access, access controls, model validation, and model integrity
- Security infrastructure: Network security and operating system security
- Observability: GPU performance, token consumption, network performance, and security monitoring
Technology alone is not enough. You also need experts who understand how to design and operate the environment so that it runs efficiently and delivers measurable AI value.
This is where many customers get stuck. They do not have the full set of experts required to design, implement, and operate an AI factory.
That is the problem we set out to solve.
What PulseAI changes
Gruve recently launched PulseAI platform, based on Cisco’s Secure AI Factory design. Customers can consume it through either a CapEx or OpEx model, with financing available.
Gruve brings the architectural expertise, design, deployment, and managed infrastructure services behind the solution.
Infrastructure alone does not create an outcome. Expertise does. Customers need confidence that the team designing their environment understands Cisco security, data center networking, compute, observability, and the operational realities of AI. Gruve has worked with Cisco for more than a decade and has several hundred experts focused on AI. That depth matters when a customer moves from pilot to production.
When a Cisco account team brings Gruve into the conversation, the customer is not simply hearing about a reference architecture. They are working with people who can design it, implement it, and operate it.
That is what completes the solution.
The three signals I would manage first
When I look at AI infrastructure in production, three signals come to mind:
Tokenomics. Performance. Security.
Signal 1: Tokenomics
Tokenomics is a signal many enterprises are still learning to manage.
In cloud environments, customers can consume tokens quickly without understanding the cost until the bill arrives. In an on-prem environment, you know what you have invested, what the infrastructure is generating, and how the economics are changing over time.
That visibility makes token consumption manageable.
Signal 2: Performance
Performance is simple. If AI becomes part of your reasoning infrastructure or application infrastructure, the response has to come back in a useful amount of time. The network path, the GPU design, the placement of workloads, and the observability all matter.
Signal 3: Security
Security is not something you add later. There are two dimensions to consider: AI model and application security, and infrastructure security.
For models and applications, you need to know whether your models are safe, whether applications are behaving correctly, whether identities are being used appropriately, and whether traffic is moving in unexpected ways.
AI agents make identity especially important. An agent does not have a face, but it may act on behalf of a person or process. Its identity, access, and authority must be controlled.
Infrastructure security is equally important. AI servers must be protected against malicious activity, and firewalls and other controls must defend the environment against threats such as denial-of-service attacks.
These three signals are connected. If performance is poor, the business will not adopt the system. If security is weak, the risk is too high. If token consumption is invisible, the economics will create surprises. All three must be actively managed.
This is also a cyber resilience issue
Cyber resilience is not only about what happens after a security attack. It is about whether the infrastructure your organization depends on can continue operating under pressure.
If an AI environment is built as a one-off pilot with unclear ownership, weak security, limited observability, broad access, and no operating model, it becomes another fragile system.
If it is built as a secure factory with appropriate fault tolerance, monitoring, security, and managed operations, it becomes part of the organization’s resilience strategy.
AI also introduces a new set of security questions:
Which AI models are safe? What good or bad data has the model learned? What identity, access, and authority does an agent have? How do we detect abnormal inference traffic or token usage? These are not future questions. They are production related questions.
The answer is not to slow down AI adoption. The answer is to build the foundation correctly.
Start small, but build it for scale
My advice to CIOs is not to boil the ocean.
Start with one to five pilots. Make them successful. Learn what the environment requires. But do not design the foundation as if it will always remain small.
The moment a pilot moves into production, the requirements change.
Capacity matters. Security matters. Governance matters. Operations matter.
This is why Secure AI Factory matters and why PulseAI exists. Cisco gives enterprises a validated architecture. Gruve makes it deployable, consumable, and operable.
The vision was always clear. The hard part was making it practical. This is what Gruve’s Pulse AI Platform makes possible.
See how we packaged it: PulseAI by Gruve
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