Cloud & AI infrastructure
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Moving systems to the cloud or preparing a company to use AI are infrastructure decisions before they are technology decisions. You need to know what you gain, what it costs over several years, what connectivity and security it requires, and what should stay on-premises. I help make these decisions and run the migration, from assessment to delivery.
The problem
The company hears it must “go to the cloud” and “use AI”, but the cloud bill grows unchecked, the links cannot cope and nobody can say what should leave the data center and what should stay. AI projects run into scattered data and infrastructure that was not ready for them.
Signs you need to review your strategy
- The cloud bill grows every month and nobody can explain why.
- Migrated systems got slower than before.
- There are no criteria for deciding what goes to the cloud.
- Site links were not sized for the cloud.
- AI initiatives start without knowing where the data is and who can access it.
How I work
1. Assessment
I map workloads, current costs, dependencies, connectivity and data requirements.
You get: a list of what should move to the cloud, what should stay and what should be reviewed.
2. Design
I compare scenarios (public cloud, in-house data center and hybrid) on cost, risk and performance.
You get: the recommended architecture and a multi-year cost comparison.
3. Migration
I run the migration in stages, with testing, without stopping operations.
You get: follow-up at every stage and a planned rollback.
4. Delivery
I document, set cost-control rules and hand over to operations.
You get: a working environment and cost indicators to track.
Proof
- Century: 474 B2B clients served in connectivity, cloud and security.
- IBM: over BRL 20 million in mission-critical solutions.
- Education: Digital Transformation at MIT (2021) and Fundação Dom Cabral (2017).
Frequently asked questions
In-house data center or cloud: how to decide?
Compare the total cost over several years, the criticality and latency needs of each system, data requirements and the team’s ability to run each model. Many companies end up with a hybrid model, with each workload where it costs less and carries less risk.
What do we need before adopting AI?
Organized data with controlled access, adequate connectivity and processing capacity, security and LGPD rules, and a use case with clear value. Without that foundation, AI becomes a pilot that never scales.
Is the cloud always cheaper?
No. For some workloads it is, for others it is not. The math depends on the usage profile, the volume of data transferred and discipline in managing resources.
Let’s talk about your case
Tell me in a few lines what is happening or what you are planning. I reply personally.
Prefer email? james.eustachio@gmail.com · Contact form