The workload changed. The infrastructure should too.
For most of the last decade, the default answer to “where do we run our model” was a general-purpose cloud region: the same multi-tenant fabric that runs web servers, batch jobs, and analytics. That worked when models were small and serving them was an afterthought. It does not hold up now.
Production AI inference is a distinct workload with its own physics. It is latency-sensitive at the millisecond level, sensitive to where every GPU sits relative to every other GPU, and unforgiving of the “noisy neighbor” effects that general-purpose clouds tolerate by design. When you serve a model to real users, tail latency is the product. A p99 spike caused by another tenant saturating a shared NIC is not an abstraction; it is a slow response a customer feels.
This is the argument for the inference neocloud: infrastructure purpose-built for AI workloads rather than retrofitted from general compute. Evergrid is built on that thesis.
Why single-tenant is the foundation, not a feature
The single most consequential design choice in inference infrastructure is tenancy. On a multi-tenant platform, your GPUs share host CPUs, memory bandwidth, storage paths, and — most painfully — network fabric with workloads you cannot see and cannot control. You inherit their congestion. You inherit their security surface. You inherit their scheduling.
A single-tenant GPU cluster removes that uncertainty. The fabric is yours. The topology is known. The performance you measure on Tuesday is the performance you get on Saturday during a traffic spike, because no one else can step on it. For teams running production inference, that determinism is worth more than a marginally cheaper hourly rate on shared hardware.
Predictability is an engineering requirement
Inference economics live and die on consistency. If your time to first token wanders because the underlying fabric is contended, you cannot make hard promises to your users, and you cannot reason about cost per request. Single tenancy turns performance from a probability distribution into a specification.
Purpose-built means more than fast GPUs
Anyone can rack accelerators. The difference between a GPU rental and an inference platform is everything around the silicon.
- Low-latency networking. Evergrid clusters run on RoCE or InfiniBand fabrics so GPUs talk to each other at the bandwidth and latency that distributed serving and large-model sharding actually require. Ethernet built for web traffic is not the same thing.
- GPU-native redundancy and self-healing. Automated provisioning, continuous health checks, and automated healing mean a failing accelerator is detected and routed around, not discovered hours later in a latency graph.
- Advanced cooling. Dense modern accelerators throttle or fail without serious thermal engineering. Cooling is not back-office plumbing; it is what lets a cluster sustain rated performance under continuous load.
- 24/7 monitoring with a 12-minute response SLA. Production inference does not keep business hours, and neither does the operations posture behind it.
These are not add-ons. They are the reason a purpose-built cluster behaves like infrastructure and a general-purpose region behaves like a lottery.
Neocloud versus hyperscaler: a real decision
The honest comparison is not “neocloud good, hyperscaler bad.” It is a tradeoff, and it deserves to be made on the merits. We lay it out in detail in our neocloud vs hyperscaler breakdown, but the short version: hyperscalers win on breadth of adjacent services and global default presence; a neocloud wins on price-to-performance for the specific job of running GPUs hard, on tenancy isolation, and on a cost profile you can forecast.
If your workload is genuinely diverse — databases, queues, web tier, and a little inference on the side — a general cloud may be the right center of gravity. If your workload is GPUs running models at scale, the general cloud is paying for flexibility you are not using and charging you in the currency that matters most: performance variance.
Predictable cost, not just predictable performance
Inference at scale is a margin business. Whether you price your product per token or per seat, your unit economics are downstream of what compute costs and how efficiently it runs. General-purpose clouds optimize for elasticity and bill accordingly, which is excellent for spiky, unpredictable demand and expensive for steady, heavy, always-on serving.
Evergrid offers GPU-based or token-based pricing against a predictable cost profile, with capacity reserved to your workload. That is the difference between guessing at next quarter’s infrastructure bill and knowing it. For dedicated, production-grade serving capacity, our AI inference solution is built around that reserved, single-tenant model.
The bigger picture
Evergrid is building AI infrastructure to power the 4th Industrial Revolution, with more than 1GW of capacity under development. The teams shaping that era — labs, hyperscalers, enterprises, institutions — are not looking for somewhere to park a GPU for an afternoon. They are looking for a foundation: dedicated, isolated, instrumented, and operated by people whose entire job is keeping accelerators healthy and fast.
The inference neocloud is not a rebranding of cloud. It is the recognition that production inference earned its own infrastructure. The labs that treat it that way will ship faster, serve cheaper, and sleep better than the ones still renting time on someone else’s congestion.
