On-Premise AI vs Cloud Solutions: Which to Choose?

Introduction

Implementing AI in your business requires a serious infrastructure decision. The main question — use cloud services (AWS, Google Cloud, Azure) or build your own on-premise GPU infrastructure? This decision impacts not just costs, but also data security, performance, and your ability to scale AI solutions in the future.

The Devs.lv team has worked with both approaches — from installing NVIDIA A100 servers in client data centers to building large-scale AWS SageMaker pipelines. In this article, we share real experience, not theory.

On-Premise Advantages

Local GPU infrastructure is the best choice for businesses with consistent, predictable AI workloads and strict data security requirements.

• Complete data control — sensitive data never leaves your premises. In finance, healthcare, and defense, this is often mandatory.
• Predictable long-term costs — after the initial investment, monthly costs are just electricity and maintenance. After 18-24 months, on-premise usually costs less than cloud.
• Lower latency — local inference delivers <5ms latency vs. 50-200ms in the cloud. Critical for real-time applications.
• GDPR compliance — easier to ensure data residency in the EU when data is physically under your control.
• No third-party dependency — cloud provider price changes or outages don't affect you.

On-Premise Drawbacks

• High upfront investment — a single NVIDIA H100 server costs €30,000-50,000. Full infrastructure with cooling, UPS, and networking can reach €100,000+.
• Hardware depreciation — GPU generations change every 2-3 years. Your investment loses value.
• Maintenance burden — requires a DevOps specialist or outsourced service for server maintenance.
• Scaling limitations — if load spikes suddenly, you can't add GPUs in minutes.

Cloud Advantages

Cloud AI is ideal for prototyping, variable workloads, and businesses still experimenting with AI.

• Elasticity — scale resources up/down on demand. Pay only for what you use.
• No upfront investment — start with €100/month and scale gradually.
• Faster start — infrastructure ready in minutes, not months. Ideal for hackathons and POC projects.
• Latest hardware — cloud providers regularly refresh GPU fleets. You always have access to the newest generation.
• Built-in tools — AWS SageMaker, Google Vertex AI, Azure ML Studio offer ready-made pipeline tools.

Cloud Drawbacks

• Costs can grow exponentially — a 24/7 GPU instance can cost €2,000-8,000/month. Over a year, that exceeds on-premise investment.
• Data transfer risks — every dataset you upload to the cloud is a potential security risk.
• Vendor lock-in — switching between cloud providers is complex and expensive.
• Availability issues — popular GPU types (H100, A100) aren't always available on demand.

The Hybrid Approach — Our Recommendation

At Devs.lv, we recommend a hybrid approach that combines the best of both worlds:

1. Start in the cloud — prototype and validate your AI model with cloud resources. Lower risk, faster results.
2. Measure workload — once the model is production-ready, analyze actual GPU utilization and costs.
3. Migrate to on-premise — if workload is consistent and predictable, invest in local infrastructure. ROI typically 18-24 months.
4. Keep cloud burst capacity — use the cloud for peak loads while on-premise handles base workload.

Real-World Example

One of our clients — a Latvian manufacturing company — started with AWS SageMaker for a quality control AI model. After a 6-month validation period, workload was stable: ~200 inference requests per second, 24/7. The monthly AWS bill reached €4,200.

We helped install 2x NVIDIA A100 servers in their data center for €65,000. Result: €4,200 → €800/month (electricity + maintenance). The investment paid for itself in 19 months, and latency dropped from 85ms to 3ms.

Conclusion

There's no universally right answer. The choice depends on your workload characteristics, budget, data sensitivity, and scaling plans. But almost always, the hybrid approach is the smartest choice — start small, measure, then invest with confidence.

Need help planning your AI infrastructure? We offer a free 30-minute consultation to assess your needs.