The FeatureOps Summit 2026 gathers engineers from Wayfair, Visa, Mintlify and Lloyds to tackle AI inference engineering. Organizers frame the event around “fearless delivery,” stressing safety nets for auto‑generated code, sub‑millisecond edge evaluation, and a move away from fixed‑release pipelines. Attendees will leave with concrete patterns for building fail‑safe production stacks.

Inference engineering hinges on a two‑phase GPU workflow. Prefill consumes raw compute to generate the first token and KV cache, measured by time‑to‑first‑token. Decode streams each subsequent token, limited by memory bandwidth and judged by tokens‑per‑second. Open‑model registries now host over two million models, letting firms self‑host and cut costs by roughly 80% at scale.

Practices such as batching multiple requests token‑by‑token boost throughput while raising per‑user latency, and prefix caching reuses KV data for shared prompts, shaving prefill time. Cursor’s Composer 2.0 demonstrates these tricks, delivering autocomplete latency that beats closed APIs. Mastering this split and its optimizations now defines production‑ready AI services.

The shift from frontier‑lab exclusivity to widespread inference stacks forces companies to invest in low‑level GPU code, model‑serving frameworks and cloud orchestration. Those that ignore the prefill‑decode trade‑off risk higher latency, lower uptime and inflated cloud bills. Effective inference engineering therefore translates directly into faster user experiences and measurable cost savings.