The decision between running AI models on your own infrastructure or using cloud APIs (Claude, GPT-4,Gemini) is one of the most important architectural decisions when building on AI. Each option has clear advantages and cases where it is the right choice.
When to choose local LLMs
Very high volumes with significant cost: beyond a certain volume of API calls, the token cost exceeds the cost of a server running your own model. The typical threshold is between 10.000and 50.000monthly calls, depending on the model.
Particularly sensitive data: medical information, financial data, commercial secrets you don’t want leaving your infrastructure. Although the major APIs (Anthropic, OpenAI) have robust privacy agreements, there are sectors where regulation or internal policy prohibits sending data to third parties.
Critical latency: a cloud API call has a minimum of 200-500ms network latency. For real-time applications (voice agents, interactive assistants), a local model on the same server as the application eliminates that latency.
Vendor independence: you don’t want to depend on Anthropic or OpenAI raising prices, changing policies, or restricting access. With a local model, you control the full stack.
When to choose cloud APIs
Most cases: for companies starting out with AI, cloud APIs are the right choice 90% of the time. No infrastructure to maintain, quality superior to open-source models, automatic scaling.
Maximum quality: the top models (Claude Opus, GPT-4) deliver consistently superior quality to the best open-source models. For tasks where quality matters most (editorial writing, complex analysis), the difference is significant.
Small teams without a technical profile: maintaining a server with Ollama requires technical knowledge. Cloud APIs are a matter of calling an endpoint.
The hybrid model that works best
At BAI Marketing we use both. Critical tasks in the cloud (Claude or GPT-4): editorial content, commercial proposals, strategic analysis. High-volume tasks locally (LLaMA on our own server): prospecting emails, ticket classification, responses to FAQs. Distributing workloads according to importance and volume optimises both quality and cost.