Architect.io

In plain words

Architect.io is a continuous delivery platform that enables developers to create preview, staging, and production environments for any application. It uses GitOps principles and dependency awareness to ensure deployments include all necessary components like APIs, databases, and events. The platform is designed for software engineers and development teams who need production-grade deployment capabilities across multiple environment types.

written from the facts on this page · September 2026

From the sources

Architect’s continuous delivery platform allows any dev to create preview, staging, and even prod environments. We're dependency-aware and powered by GitOps so every deployment is production-grade, including the APIs, databases, and events it needs to run.

Architect live-migrates running Kubernetes workloads before spot instances disappear. Stateful apps on spot: up to 90% cheaper than on-demand, with zero downtime and no code changes.

Architect moves running pods before a spot instance is reclaimed. No dropped connections, no code changes. Check your cloud bill. The biggest on-demand line items are almost always stateful: session stores, message brokers, in-memory databases, AI agent memory. They run on full-price instances because the state can't disappear, roughly 2–3× the cost of the same workload on spot. Lose the instance and the in-memory store goes with it, taking the rest of your system down: reconnect storms, cold caches, a slammed database. Stability required on-demand or reserved instances. Not anymore. Architect checkpoints the full running state (memory, open sockets, in-flight work) before the node is…from architect.io

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  Hey HN — I’m Adil from Katanemo (with Salman, Shuguang, and Meiyu) We previously shared an early version of this project as ArchGW. Based on customer feedback, the scope expanded from “LLM routing and model access” into something broader: delivery infrastructure for agentic applications. We renamed it to Plano and reworked the architecture accordingly. The problem On-the-ground AI practitioners will tell you that calling an LLM is not the hard part. The really hard part is delivering agentic applications to production quickly and reliably, then iterating without rewriting system code every…

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