Slow AI API? The problem isn't the model — it's the route

Every Brazil ↔ US round trip costs ~150ms. Add model processing time and the "slowness" your users feel has a name — and a fix.

"The AI API is slow" — the #1 complaint when integrating models. Let's separate your code, the network, and the model.

Where latency comes from:

1. Provider server location — most providers run in the US/EU; each round trip adds ~120-180ms of pure network;
2. Time to First Token (TTFT) — big models can take 2-3s+ before the first token;
3. Synchronous responses — waiting for the full response before showing anything;
4. Your own code — no keep-alive, no timeouts, unnecessary serial work.

Fixes, in order of impact:

1. Streaming (stream=True) — show tokens as they arrive. Users perceive the answer in hundreds of ms even if the total is slow.

2. Smaller models for simple tasks — TTFT grows with model size. A mini solves most cases.

3. Trim context — context tokens take time to process. Send only what's needed.

4. Pick a provider with a good route to your region — some gateways have South America endpoints.

5. Measure first — time.perf_counter() around the call, compare TTFT vs total.

Golden rule: stream first, measure second, switch providers third. Big models have higher TTFT, but network and streaming are what to attack first — in most cases streaming alone fixes most of the perceived slowness.

Want to try these models in your project? **ModelKiwi* gives you access to GPT, Claude and Gemini with PIX payment (no international credit card needed) and free credits to start: https://www.modelkiwi.com. WhatsApp: +5521999500402 — and join our channel: https://t.me/ModelkiwiOfficial.*