Local LLMs vs Cloud AI: A Practical Decision Framework
Every few weeks, someone asks us whether they should run AI models locally or use cloud services. The honest answer is: it depends. But that's not very helpful, so here's a framework for actually making the decision.
What you're actually trading off
Cloud AI is convenient. You sign up, get an API key, and start making requests. The provider handles infrastructure, scaling, and model updates. You pay per use. For experimentation and low-volume applications, this is hard to beat.
The cost is control. Every query you send leaves your infrastructure. For a customer service chatbot discussing product features, this is probably fine. For an AI processing employee contracts, medical records, or financial documents, it might not be.
Local deployment flips this trade-off. Your data never leaves your servers. You control exactly which model runs and how it's configured. But you're responsible for hardware, maintenance, and keeping up with model improvements. The upfront investment is higher. So is the ongoing operational burden.
Neither option is inherently better. The right choice depends on what you're processing and what risks you can tolerate.
When local deployment makes sense
Regulatory requirements sometimes force the decision. If you're subject to GDPR, HIPAA, or financial services regulations, sending certain data to third-party APIs may create compliance headaches. Check with your legal team, but local deployment often simplifies the regulatory picture.
Competitive sensitivity matters too. If your AI is processing proprietary research, M&A documents, or strategic plans, you might reasonably not want that data flowing through someone else's servers, regardless of their privacy policy.
Volume economics can also tip the scales. Cloud AI charges per token. At low volumes, this is cheap. At high volumes, it adds up. One company we worked with was spending €40,000 per month on API calls. A local deployment with equivalent capability cost €15,000 upfront plus €2,000 monthly in compute. The payback period was under three months.
Finally, latency and availability matter for some applications. Local models respond faster because there's no network round trip. They also keep working if your internet connection drops or the provider has an outage.
Making the decision
Start by classifying your data. What's public? What's sensitive? What's regulated? If everything you're processing is already public information, cloud AI is probably fine.
Next, estimate your volume. How many queries per day? How long are they? Get pricing from cloud providers and compare against local infrastructure costs. Don't forget to include the cost of your team's time managing local systems.
Then consider your team's capabilities. Local deployment requires someone who can manage servers, troubleshoot model issues, and handle updates. If you don't have that expertise in-house, factor in the cost of acquiring it or outsourcing.
For most companies, the answer isn't purely one or the other. Use cloud AI for non-sensitive applications and experimentation. Deploy locally for anything involving confidential data. The extra complexity is worth the appropriate level of control.
The local-vs-cloud decision isn't about which technology is better. It's about matching your infrastructure to your actual data sensitivity, volume, and operational capabilities. Get specific about what you're processing and the numbers usually make the choice clear.