Why dedicated inference beats shared APIs at scale

Every serious LLM product starts on a shared API, and most should. You get a token and a curl command and you are live before lunch. The trouble is that the very things that make shared APIs easy — pooled capacity, opaque routing, per-token billing — are the same things that bite you once you have real traffic.

Three walls you eventually hit

1. 01Rate limits you did not know existed. Traffic spikes on launch day, and a limit you never configured throttles the exact moment you most need throughput.
2. 02Noisy neighbors. Your p99 latency drifts because someone else on the shared pool just kicked off a giant batch job. You cannot see it, and you cannot fix it.
3. 03Costs that swing 5x. Per-token billing is great when traffic is flat and brutal when it is bursty. A viral week can blow past a month of budget.

None of these are bugs. They are the price of sharing. Pooling is what makes a shared API cheap at low volume — and it is precisely what you want to stop paying for once volume is steady.

What dedicated fixes

A dedicated GPU is yours. The rate limit is your hardware, not a policy. There is no neighbor. And the bill is a flat monthly number tied to the GPU and the hours, not to a token counter you cannot predict.

The crossover is simpler than people expect: once your traffic is steady, dedicated is usually both faster and cheaper.

Seattle Compute pricing model

The catch — and how we remove it

Dedicated inference has one real cost: someone has to build and babysit the GPU infrastructure. Picking the card, packing the model, configuring the engine, watching for OOMs, handling restarts. That is the half nobody wants. It is the half we run, so you get the steady latency and the predictable bill without the pager.

If your traffic has graduated from spiky experiments to a steady baseline, it is worth doing the math. The pricing page has example configs for an 8B chatbot, a 70B assistant, and an MoE frontier deployment.