Every Inference & Gateways comparison and buyer's guide for building AI agents — 11 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
Three startups built custom silicon to outrun the GPU on token generation. The speed is real, the SRAM is tiny, and that tradeoff decides everything.
A single tokens-per-second number hides two workloads pulling in opposite directions — and the whole arc of serving optimization is the field admitting they should never share a GPU.
Every major provider sells inference at roughly half price if you can wait up to 24 hours. The discount isn't the point — the contract is, and it tells you which agent work was never realtime to begin with.
Three engines, one job: turn a model into a high-throughput endpoint. The feature gaps are closing — what's left is portability, vendor lock-in, and which project is still being built.
Buyers shop for these cards by peak FLOPS. Token generation barely uses them. The spec that actually moves inference throughput is the one most spec sheets bury — and a single NVIDIA card proves it.
Three ways to put a model behind an endpoint — and they increasingly run the same engine underneath, so the thing you are actually choosing is not speed.
They all wrap roughly the same inference engine, so they all run the same model at roughly the same speed. The thing that actually separates them is what shape they want to be — a daemon, a polished app, or an open one.
Three ways to rent open-weight inference without owning a GPU — and why the fastest of them just licensed its speed to Nvidia instead of competing with it.
Per-prompt model routing promises GPT-quality answers at a fraction of the bill. The honest 2026 answer is that it's a cost lever with a threshold, not a free one — and a neutral benchmark disagrees with the marketing.
Every agent ends up talking to more than one model provider. The library you put in the middle decides whether that seam stays a proxy or quietly becomes your control plane.
The benchmark everyone argues over is the wrong one. The engine you should run is decided by how much context your requests share — not by whose tokens-per-second screenshot is biggest.