Watering AI makes enterprise agents safer, simpler, and more cost-efficient, helping companies scale AI productivity at a cost they can afford.
Product Capabilities
Watering AI reduces the total cost of deploying and operating AI agents through compute efficiency, runtime density, and an integrated development toolchain.
Compute
A heterogeneous compute token service unifies existing enterprise hardware and optimizes inference so every unit of compute produces more tokens.
Runtime
A containerized runtime designed for thousands to millions of enterprise agents improves density, scheduling efficiency, and operational control.
Studio
A one-stop agent model and application development environment helps enterprises connect agents to real business workflows faster.
The biggest barrier to enterprise AI is shifting from “models are not strong enough” to “cost and engineering are too heavy.” If token cost stays high, large-scale agent deployment becomes an economic disaster. Watering AI optimizes from the source of cost to make scale economically viable.
Platform Architecture
Kube AI Hub provides heterogeneous compute management, scheduling, observability, and application delivery. Watering AI builds agent-oriented token services, runtime, and development capabilities on top.
GPU clusters
4 resource typesCPU clusters
3 resource typesStorage
3 resource typesHow It Works
From heterogeneous compute onboarding to agent launch, shorten the path from setup to production.
Connect existing GPU/CPU/NPU clusters across bare metal, VMs, and containers, onboarding new hardware in as little as one week.
Pool resources automatically, enable virtualization, deploy lightweight agent containers, and schedule millions of nodes to cut cost.
Connect business workflows with mainstream frameworks, tune small models, and move stable flows to generated code execution.
Why Now
As models become stronger, enterprises need a practical way to convert compute into secure, low-cost, and scalable agents.
Lower cost from both inference efficiency and workflow execution, preventing large-scale agents from becoming an economic burden.
A unified platform covers deployment, scheduling, monitoring, and development tools, shortening the path from setup to production.
Container and multi-tenant foundations keep agent workloads isolated, governed, and observable.
Broad hardware and inference engine compatibility protects existing investments and preserves technology choice.
Product Docs
The online docs cover installation, upgrades, heterogeneous compute management, multi-cluster operations, and platform model capabilities.
About Watering AI
Not wasteful flood irrigation, but precise technical cost reduction, drop by drop.
Watering AI makes AI productivity affordable nourishment for every piece of business soil.
We believe AI should not be a luxury reserved for giants. It should become standard infrastructure for every organization, just as personal computers moved computing power from machine rooms into everyone's hands.
The answer is in our Chinese name, Di Zhi Rang: not wasteful flood irrigation, but precise and efficient drip irrigation. We push token output efficiency to the limit and make deployment light enough to reach every enterprise environment.
One day, when a small business owner is asked whether they use AI, we want the question to feel as natural as asking whether they have running water. Watering AI is not about irrigating AI technology itself. It is about using AI to irrigate the world.
Multi-tenant isolation and tiered access control, with deep support for domestic compute chips to build a secure and controllable foundation.
A one-stop platform spans compute management, model development, and agent applications with one-click deployment and ready-to-use tooling.
Pooled heterogeneous compute lifts utilization 3-10x, runs 5 agents on 1-core 2 GB, and cuts premium-model token usage by 80% via code execution.
