The core trade-off
Running an LLM locally means the model weights live on your own machine and every inference happens there — no network call, no third party. Running AI in the cloud means you send your prompt to a provider like OpenAI, Anthropic, or Google and get a response back over an API. Local gives you privacy, control, and zero per-token cost at the price of hardware and effort. Cloud gives you frontier quality and zero setup at the price of recurring fees and sending your data to someone else. Almost every real decision comes down to which of those you value more for a given workload.
Privacy and control
This is where local AI wins decisively. With a local model, your prompts and documents never leave your device, so there is nothing to leak, nothing logged on a vendor’s servers, and no question of whether your data trains someone else’s model. That is why regulated industries, security-sensitive teams, and offline or air-gapped deployments reach for local inference. Cloud providers do offer enterprise privacy terms, data- processing agreements, and zero-retention options on their APIs — but you are still trusting a contract rather than a network boundary.
Cost
The cost shapes are opposite. Cloud is pay-per-token: cheap to start, no upfront spend, but the bill scales linearly forever with usage. Local is the reverse — a real upfront cost for capable hardware, then near-zero marginal cost per request plus electricity. The break-even depends on volume. For occasional or bursty use, cloud is almost always cheaper. For high, sustained throughput — a service answering millions of requests, or a developer hammering a model all day — owned hardware can pay for itself within months.
Quality, latency, and hardware
On raw quality, the best cloud models (GPT-4-class, Claude, Gemini) still lead on the hardest reasoning, long-context, and coding tasks. Strong open models — Llama 3.1, Qwen, Mistral — are excellent for everyday work and closing the gap quickly, but the absolute frontier remains cloud-only today.
On latency, local can be faster because there is no network round-trip, if your hardware is fast enough; on weak hardware a quantised model may be slower than a cloud call. Hardware is the gatekeeper for local: 7B-8B models run on a 16GB laptop, mid- size models want 16-24GB of VRAM, and 70B+ models need serious GPUs or aggressive quantisation. Tools like Ollama, LM Studio, and llama.cpp make local setup genuinely approachable by handling model downloads and quantisation for you.
How to choose
- Choose local when data privacy is non-negotiable, when you have steady high volume, when you need offline operation, or when you want fixed, predictable costs and control over the model.
- Choose cloud when you need the best possible quality, when usage is low or unpredictable, when you want zero setup and instant scaling, or when you lack the hardware budget.
- Use both — the common production pattern. Route private or high-volume work to a local model and escalate the hardest queries to a frontier cloud API, getting most of the privacy and cost benefits while keeping a quality safety net.