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Should I choose cloud or local AI deployment?

Quick Answer

Choose cloud AI for faster deployment, lower upfront cost, and automatic scaling. Choose local deployment for maximum data control, regulatory compliance in sensitive sectors, and predictable long-term costs at scale. Many organisations use a hybrid approach, running sensitive workloads locally while leveraging cloud for less sensitive tasks and experimentation.

Summary

Key takeaways

  • Cloud AI offers faster time-to-value with lower upfront investment
  • Local deployment provides maximum data sovereignty and privacy control
  • Hybrid approaches let you balance security needs with cloud flexibility
  • Consider total cost of ownership over 3 to 5 years, not just initial setup

When Cloud AI Is the Right Choice

Cloud AI deployment is the right choice for most organisations starting their AI journey. It offers dramatically lower upfront costs since you pay only for what you use rather than investing in hardware. Cloud providers offer access to the latest models and capabilities without needing to manage updates yourself. Scaling is automatic, handling spikes in demand without capacity planning. Cloud deployment is also faster: you can go from concept to production in weeks rather than months. For non-sensitive data and general business applications, cloud AI provides an excellent balance of capability, cost, and convenience. Major providers like AWS, Azure, and Google Cloud also offer robust security and compliance certifications that satisfy most regulatory requirements.

When Local AI Deployment Makes Sense

Local or on-premise AI deployment is essential when data must never leave your organisation's infrastructure. This is common in defence, certain healthcare applications, and financial services with strict data residency requirements. Local deployment gives you complete control over data flow, eliminating concerns about third-party data processing. It can also be more cost-effective at scale: once you reach a certain volume of AI inference, the predictable cost of owned hardware beats per-query cloud pricing. Local deployment also eliminates internet dependency, which matters for real-time applications in manufacturing or environments with limited connectivity. Open-source models like Llama and Mistral have made local deployment increasingly viable, offering strong performance without licensing fees.

The Hybrid Approach

Many organisations adopt a hybrid strategy that combines the strengths of both approaches. Sensitive data processing, such as handling personal data or proprietary information, runs on local infrastructure where data sovereignty is guaranteed. Less sensitive workloads, prototyping, and tasks requiring the latest large language models run in the cloud. This approach optimises cost while maintaining compliance. Implementing a hybrid architecture requires careful planning around data classification, network security, and orchestration between environments, but it provides the flexibility to use the right deployment model for each use case.

Related

Related questions

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FAQ

Frequently asked questions

Major cloud providers offer enterprise-grade security including encryption, access controls, and compliance certifications such as ISO 27001 and SOC 2. For most business applications, cloud security is more robust than what organisations can achieve on-premise.

A basic GPU server for AI inference starts at around £5,000 to £15,000. Enterprise-grade setups with redundancy and proper cooling can cost £50,000 to £200,000+. Factor in ongoing maintenance, power, and technical staff costs.

Yes, but plan for it from the start. Design your AI pipeline with abstraction layers that allow you to swap the underlying infrastructure. Migration typically takes 4 to 8 weeks for well-architected systems.

The break-even typically occurs when monthly cloud API costs consistently exceed the amortised cost of equivalent local hardware. For most organisations, this happens at around 10,000 to 50,000 daily AI requests, though the exact point depends on model size and task complexity.

Yes, with proper planning. Design your AI pipeline with abstraction layers that decouple your application logic from the specific model serving infrastructure. This makes migration straightforward when volume justifies the switch.

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