Local AI vs. hosted APIs: how to choose
"Should I run this locally or just call an API?" is one of the most consequential — and most reflexively answered — decisions in an AI stack. The honest answer is that it depends on four things you can actually measure: cost at your real volume, how sensitive your data is, how much latency you can tolerate, and how much maintenance you're willing to own. Here's how to think it through instead of defaulting to whatever's trendy.
Cost flips with volume — know where your line is
Hosted APIs are priced per token, which is wonderful at low volume and brutal at high volume. Local inference inverts that: a real fixed cost up front (GPU, or the opportunity cost of hardware you own) and near-zero marginal cost per request after. At a few thousand calls a month, an API almost always wins on total cost. At sustained high throughput, the per-token meter is what kills budgets. Estimate your monthly token volume, multiply it out at current API prices, and compare against the amortized cost of hardware that can serve your model. The crossover point is usually clearer than people expect.
Data sensitivity can decide it before cost does
If you're sending regulated, confidential, or customer-identifying data, where the inference happens stops being a performance question and becomes a compliance one. A hosted API means your data leaves your boundary and lands under someone else's data-handling, retention, and training terms — read them, because they vary widely. Local or self-hosted inference keeps the data inside your perimeter, which can be the entire reason to accept a worse cost or quality trade-off. Decide this axis first: if the answer is "this data can't leave," it narrows everything downstream.
Latency: round-trips vs. cold starts
Hosted frontier models are fast and consistent because someone else keeps the hardware warm. Local models avoid network round-trips entirely, which can win for short, frequent calls — but only if your hardware keeps the model loaded; a cold start on a large local model can dwarf any API round-trip. Match the choice to your access pattern: steady high-frequency traffic rewards a warm local model, while bursty or occasional use often runs cheaper and simpler against an API you don't have to keep alive.
Capability gap: be honest about "good enough"
The largest hosted models still lead on the hardest reasoning, long-context, and tool-use tasks. But a great deal of production work — classification, extraction, summarization, routing, structured generation — is comfortably handled by smaller open models you can run yourself. The mistake is benchmarking your local option against the frontier on tasks you don't actually do. Benchmark it against your tasks. If a 7B–14B open model clears your bar, the local economics and privacy benefits are yours to take.
Maintenance is the cost nobody quotes
An API is someone else's on-call rotation: updates, scaling, and uptime are their problem. Self-hosting means you own model updates, GPU drivers, serving infrastructure, and the 2 a.m. page when throughput spikes. That's a real, recurring tax that rarely shows up in the initial cost comparison. For a small team without infra depth, an API's simplicity can be worth paying for even when local looks cheaper on a spreadsheet.
A practical default — then revisit
For most teams: start with a hosted API to validate the use case without committing capital, and move toward local when volume makes the meter painful, data sensitivity demands it, or a smaller open model provably clears your quality bar. Many mature stacks end up hybrid — local for high-volume, non-sensitive, "good enough" work; hosted for the hardest queries. Re-run the comparison when your volume changes by an order of magnitude; the right answer at 10k calls a month is often the wrong one at 10M.