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Cloud & AI Platform Consulting Malta

Cloud and AI platform consulting in Malta. Expert guidance on AWS, Azure, and GCP AI services, cloud ML platforms, and enterprise AI infrastructure.

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/ the solution /

Cloud & AI Platform Consulting built around your business.

Every solution we deliver is built on three pillars: your data, your context, and continuous improvement. Each capability is traceable and measurable.

  • Cloud AI Platform Selection & Evaluation

    Compare AWS SageMaker, Azure AI, Google Vertex AI, Databricks, and emerging platforms agai…

  • Cloud Architecture Design for AI Workloads

    Design cloud architectures optimised for AI workloads including GPU provisioning strategie…

  • Multi-Cloud AI Strategy & Governance

    For organisations spanning multiple cloud providers, we design AI strategies that leverage…

  • Cloud AI Cost Optimisation & FinOps

    AI workloads generate substantial cloud bills through GPU compute, model storage, inferenc…

/ how it works /

Live in weeks, not months.

We evaluate your existing cloud infrastructure, AI workloads, cost patterns, and team capabilities. This assessment establishes the baseline for platform recommendations and identifies immediate optimisation opportunities.

Detailed analysis of your AI use cases, training requirements, inference patterns, data integration needs, compliance constraints, and growth projections. Requirements drive platform evaluation rather than technology preferences.

Structured evaluation of cloud AI platforms against your requirements using weighted scoring criteria. Where appropriate, we conduct proof-of-concept evaluations on candidate platforms to validate performance and usability claims.

Detailed architecture design covering compute infrastructure, storage, networking, security, monitoring, and CI/CD for AI workloads. Cost models project spending across different usage scenarios and growth patterns.

A phased roadmap for platform adoption or migration that minimises risk and maintains business continuity. The roadmap sequences infrastructure setup, workload migration, and team enablement for progressive value delivery.

Optional ongoing advisory during implementation provides architecture reviews, cost monitoring, and performance benchmarking. Regular optimisation reviews ensure your Malta cloud AI platform stays efficient as usage patterns evolve.

/ what you get /

Everything you need. Nothing you don't.

Cloud AI Platform
Selection & Evaluation
Cloud Architecture Design
for AI Workloads
Multi-Cloud AI Strategy
& Governance
Cloud AI Cost
Optimisation & FinOps
/ built for your situation /

Sounds familiar?

Infrastructure Manager, financial services company
"We're moving our data workloads to Azure but we don't know whether to use Azure ML, Azure OpenAI, or third-party AI services — we need an expert to guide the decision"

We assess your workload requirements, data volumes, and compliance constraints, then provide a vendor-neutral recommendation with a migration plan and cost model for each option.

How Neural AI helps

We assess your workload requirements, data volumes, and compliance constraints, then provide a vendor-neutral recommendation with a migration plan and cost model for each option.

/ proof /

Real deployments. Real results.

Data Engineering & AI

GPT Cloud Migration Architecture

Complete migration of Malta Tourism Authority legacy licensing data to cloud using GPT-powered NLP for error detection, achieving over 90% reduction in migration errors.

Seamless platform transition

Read case study →
AI Development

VelaCloud Portal Infrastructure

A comprehensive AI-powered internal and client portal for Veracloud, managing all their IT services with intelligent document processing, CRM integration, and automated summarisation.

Enterprise cloud AI platform

Read case study →
/ questions /

Cloud & AI Platform Consulting FAQ

Should we use AWS, Azure, or Google Cloud for AI workloads?
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.
How do you keep platform recommendations current as cloud services evolve?
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.
Can you help optimise our existing cloud AI spending?
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.
What about using multiple cloud providers for AI?
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.
How do you address data residency and compliance for cloud AI?
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.
Should we use managed AI services or build custom infrastructure?
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.
How do you handle GPU provisioning strategy?
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.
What is the typical consulting engagement timeline for cloud AI?
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.
/ get started /

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