I run local models on my own hardware for some tasks and cloud APIs for others. People ask which one is better as if it is a philosophical question. It is not. It is a cost and latency question, and the answer depends on what you are doing.
When local makes sense
If you have a task that runs thousands of times a day with short inputs and predictable outputs - classification, extraction, simple Q&A - a local model is cheaper. You pay for the hardware once (or rent a dedicated GPU) and run as many requests as you want. No per-token billing, no rate limits, no dependency on someone else’s uptime.
We run a 7B model locally for content classification. It sorts incoming items into categories, tags them, and moves on. The quality is indistinguishable from a cloud API for this specific task, and the marginal cost per request is effectively zero after the GPU cost.
Local also makes sense when data cannot leave your infrastructure. Healthcare, finance, government - some clients simply cannot send data to a third-party API, regardless of the provider’s privacy policy. A local model running on your own hardware solves that constraint entirely.
And latency. A local model on your own network responds in 20-50ms. A cloud API adds network round-trip, queue time, and whatever the provider’s load looks like that minute. For interactive applications where every millisecond matters, local is faster.
When cloud makes sense
For complex reasoning, long-form generation, code review, anything that needs a large model - cloud APIs are the practical choice. Running a 70B+ model locally requires serious hardware. An H100 costs $25,000+ to buy, and the operational overhead of keeping it running, updated, and healthy is real work.
Cloud APIs also make sense when your volume is low or unpredictable. If you make 500 requests a day, the per-token cost of a cloud API is cheaper than keeping a GPU warm 24/7. The crossover point depends on the model and the task, but for most teams it is somewhere around 50,000-100,000 requests per day. Below that, cloud is cheaper. Above it, the math starts to favour local.
And model variety. If you need to switch between different models for different tasks - a small model for classification, a large one for generation, a code-specialised one for review - cloud APIs let you do this without managing multiple deployments. This is what Smart Inference does: route each request to the right model on the right provider at the right price.
The mistake most teams make
They pick one approach and use it for everything. All cloud, or all local. The right answer is almost always a mix: local for the high-volume, low-complexity tasks where you control the hardware cost, cloud for the complex, variable tasks where flexibility and model access matter more than per-request cost.
We run both. Local 7B for classification and tagging. Cloud APIs through Smart Inference for everything else, routed dynamically to the cheapest provider that meets latency requirements. The combination costs less than either approach alone.
The question is not “local or cloud.” The question is “which tasks go where.” Start with your request volume and your latency requirements. The rest follows.