Cloud & AI Platform Consulting Malta

The right choice depends on your specific requirements. AWS offers the broadest service catalogue, Azure integrates tightly with Microsoft ecosystems, and Google Cloud excels in ML research tools and TPU access. Our consulting evaluates each against your use cases, existing infrastructure, team skills, and compliance needs to provide a clear recommendation for your Malta business.

Our consultants maintain active certifications and hands-on experience across all major cloud platforms. Recommendations reflect the current service landscape at engagement time. We design architectures with abstraction that accommodates service evolution, and optional ongoing advisory keeps your strategy current with quarterly platform reviews.

Cloud AI cost optimisation is one of our most impactful consulting engagements. We analyse GPU utilisation, inference endpoint efficiency, storage costs, and data transfer patterns. Typical optimisation engagements identify 30 to 50 percent savings through right-sizing, spot instance adoption, reserved capacity planning, and architectural improvements.

Multi-cloud AI strategies can leverage best-of-breed services from each provider but add operational complexity. Our consulting evaluates whether multi-cloud benefits justify the overhead for your specific situation. When multi-cloud is appropriate, we design governance and abstraction layers that maintain operational coherence.

We evaluate data residency options for each cloud provider within the EU and specifically within Malta-relevant regions. Our consulting addresses GDPR compliance, industry-specific regulations, and data sovereignty requirements. Recommendations include architecture patterns that keep sensitive data within approved jurisdictions while leveraging cloud AI capabilities.

We evaluate this decision for each workload type. Managed services like SageMaker and Vertex AI accelerate development but may constrain flexibility. Custom infrastructure using Kubernetes and open-source tools offers more control but requires more operational investment. The right balance depends on your team's capabilities and workload requirements.

GPU costs are often the largest component of cloud AI spending. We design provisioning strategies that mix on-demand, spot, and reserved instances based on workload patterns. Training workloads use spot instances with checkpointing, while inference uses right-sized reserved capacity. These strategies typically reduce GPU costs by 40 to 60 percent.

Platform evaluation and architecture design typically requires six to ten weeks. Cost optimisation assessments take four to six weeks. Comprehensive consulting covering evaluation, design, and implementation roadmap takes ten to fourteen weeks for Malta organisations with complex requirements.