“Cloud makes sense until compliance says otherwise.”
Gino Ferrand, writing today from Santa Fe, New Mexico 🌞
AI is no longer just a model you use. It's an infrastructure decision. As more teams go from testing to deploying production AI, engineering leaders are facing a strategic choice: run AI on-premise, in the cloud, or both?
I pulled together recent research and practical insights to map the key tradeoffs. Here’s what matters in 2025…
On-Prem AI: Control, Compliance, and Complexity
For teams in highly regulated environments or working with sensitive data, on-prem still matters. You get:
Full control over data (key for industries like healthcare and finance).
Reduced latency (helpful for real-time inference).
Customizability across hardware and software stacks.
Predictable long-term costs (after the initial investment).
But there’s a catch: upfront CapEx is high, scaling is manual, and maintenance demands real infrastructure talent.
As one DevOps lead put it, “On-prem is like owning a racecar. Powerful, but you better have a pit crew.”
Cloud AI: Speed, Scale, and Simplicity
For most engineering teams, cloud still wins:
You can scale up or down on demand.
Managed services reduce setup and maintenance overhead.
You move faster from prototype to production.
The cost model aligns better with experimentation and variable workloads.
Still, there are risks: vendor lock-in, data egress costs, and questions about security for highly sensitive applications.
The big cloud vendors are pushing back with better compliance guarantees, VPC isolation, and observability features… but it’s still a trust and control tradeoff.
AI-Enabled Nearshore Engineers: The Ultimate Competitive Edge
The future of software engineering isn’t just AI... it’s AI-powered teams. By combining AI-driven productivity with top-tier remote nearshore engineers, companies unlock exponential efficiency at a 40-60% lower cost, all while collaborating in the same time zone.
✅ AI supercharges senior engineers—faster development, fewer hires needed
✅ Nearshore talent = same time zones—real-time collaboration, no delays
✅ Elite engineering at significant savings—scale smarter, faster, better
Hybrid: A Realistic Middle Ground
Many teams are landing somewhere in between:
Sensitive workloads stay on-prem.
Cloud handles burst compute or non-critical jobs.
Teams experiment and fine-tune in the cloud, then deploy guarded endpoints on-prem.
It’s not simple...hybrid requires orchestration, monitoring, and discipline. But in 2025, it’s increasingly common for organizations to mix models based on data sensitivity and performance needs.
The Engineering Call
This isn’t just about where your AI runs. It’s about:
Compliance posture.
Team capabilities.
Ops and monitoring expectations.
If your org handles sensitive data and has a mature platform team, on-prem might make sense. If your priority is speed and flexibility, cloud’s still the easier play. And if you’re navigating both, hybrid is no longer an outlier. It’s the emerging default.
One thing is clear: choosing where your AI lives is no longer a purely technical decision. It’s strategic.
Where will your models run in 2026?
Recommended Reads
✔️ AI Infrastructure Evolution: From Cloud-First to Control-First in 2025 (Omniscien Technologies)
✔️ Cloud vs. On-Premise GPU Solutions in 2025 (Novita)
– Gino Ferrand, Founder @ TECLA