Building a Neocloud

"First of all, what in the heck is a neocloud?" is a question I hear repeatedly when talking to businesses about the AI landscape. For those that are unaware, it's a new generation of specialized cloud providers that focus primarily on workloads that require GPUs or extremely dense CPU complexes (e.g. high-performance computing, artificial intelligence).

Breaking it down to its constituent parts, a neocloud is 1) a cloud offering that 2) provides systems with GPUs or dense CPU complexes to 3) help accelerate businesses with HPC and AI workloads.

The neocloud market has exploded from roughly a dozen providers in 2022 to hundreds today, with new entrants appearing weekly. CoreWeave raised $1.1B at a $19B valuation. Lambda Labs secured $320M. Together AI pulled in $450M. The pattern is clear - there's massive demand for GPU compute, and traditional cloud providers are having difficulty keeping up.

While many neoclouds are succeeding, enterprises are still curious how they can safely take advantage of such offerings with data that resides behind the firewall. Over 75% of enterprise data is still on-premises, and approximately 30% of that data falls under the umbrella of some form of regulation (HIPAA, CCPA, GDPR, et al). A single HIPAA violation can result in fines ranging from $100 to $2M per incident, with the average data breach costing enterprises $4.45M in 2023. Meaning, while an enterprise wants to be able to accelerate HPC and AI workloads, certain categories of data simply can't be used with external neocloud services due to privacy, security, or regulatory control. This presents a unique challenge because there are not only monetary fines but also damage to company image and potential incarceration of employees and executives (we'll be talking more about this in an upcoming blog post).

What are enterprises to do? Naturally, build your own internal neocloud.

Requirements for a Neocloud

Hardware: The Foundation

You guessed it, first on the list is hardware. Any cloud platform shares infrastructure across a plethora of users, and a neocloud is no different. It could be on-demand, burst, reserved, or otherwise, but the point of having hardware is to have someone pay you to use it. Meaning enterprises need to have ample CPU and GPU capacity on which to run workloads.

Let's talk numbers. A basic internal neocloud setup requires:

• Minimum 8-16 GPUs for meaningful multi-tenancy

• 4-8 high-density CPU nodes for preprocessing and orchestration

• 100Gbps+ networking infrastructure

• Storage systems capable of 10GB/s+ throughput

Total hardware investment for a minimal viable internal neocloud generally starts at about $1M. For a production-grade system serving 100+ data scientists and supporting enterprise workloads, you're looking at $3-10M.

Software: Where Complexity Lives

The second is software. The hardware systems need operating systems, management, remote lights-out capabilities, monitoring, telemetry, all of the things necessary to integrate a system into a broader management platform.

But here's where it gets interesting - and expensive. The software stack for a neocloud isn't just about keeping servers running. While it sounds simple on the surface, the items for which you need to account include:

• Bare metal infrastructure

• Virtualization and network virtualization

• Storage orchestration

• Container orchestration

• GPU virtualization

• Model serving including runners and serving frameworks

• Batching and request queueing

• Model registries and versioning

• Inference optimization

• Token-level usage tracking and metering

• Cost allocation and chargeback

• Performance monitoring and SLA management

• Predictive capacity planning

The software stack must also include model runners and orchestration layers that can efficiently distribute inference requests across available GPUs and CPU complexes, implement intelligent load-balancing to maximize throughput (we're talking about optimizing for both latency-sensitive and throughput-oriented workloads simultaneously), perform continuous health-checks to ensure model availability and performance SLAs are met, and capture granular telemetry reporting for usage tracking and optimization.

This means implementing a comprehensive chargeback system where you can track token consumption at the customer (business unit) level - for example, selling 1 million tokens to business unit A at a specific price per token, with real-time monitoring dashboards showing usage patterns, remaining allocation, and projected burn rates to enable proper capacity planning and budget management. Most enterprises discover they need to track at least 15 different metrics per workload: tokens/second, time-to-first-token, GPU memory utilization, queue depth, p50/p95/p99 latencies, and more.

The Reality Check: Time and Resources

Building an internal neocloud from scratch typically takes minimum 12 months, half a dozen engineers, millions of dollars, and then the ongoing management, maintenance, monitoring, care, and feeding.

And here's the kicker - by the time you've built it, the landscape has changed. New model architectures require different optimization strategies. That vLLM version you standardized on? It's now 3 versions behind and missing critical features like guided decoding or speculative sampling. Your carefully tuned batch sizes? Obsolete with the latest mixture-of-experts models.

Hidden Complexities Nobody Talks About

Multi-tenancy nightmares: Isolating workloads between business units isn't just about Kubernetes namespaces. You need memory isolation, cache partitioning, network QoS, and protection against noisy neighbor effects. One team running a poorly optimized model can tank performance for everyone else.

Model heterogeneity: Your finance team wants to run Llama-3, engineering insists on Claude, and the research team needs custom fine-tuned models. Each has different serving requirements, optimization profiles, and hardware preferences. Your unified platform suddenly needs to be three platforms.

Compliance and governance: Every model inference needs an audit trail. Who ran it? What data was processed? Which model version? What were the outputs? For regulated industries, you're building a distributed ledger on top of your ML platform. Add another 6 months and 2 FTEs.

The upgrade treadmill: NVIDIA releases new drivers monthly. CUDA versions break compatibility. PyTorch and TensorFlow have breaking changes. Kubernetes deprecates APIs. You need a dedicated team just to keep the lights on, let alone add new capabilities.

How View Helps Internal Neoclouds

As shown above, a lot goes into building out a neocloud. We at View have capabilities to help accelerate deployments of internal neoclouds, allowing you to go from deployment to outcomes in the fastest time possible.

Accelerated Deployment of Inference and Embedding Infrastructure

View delivers a turnkey, on-prem AI platform — a pre-integrated, enterprise-grade inference and embedding layer you can layer on top of your GPU/CPU infrastructure rather than developing from scratch. Not only does View allow you to rapidly ingest and process internal data to quickly create conversational assistants and agents, it also expose API endpoints (completions/embeddings) that your business units can consume for managing, load-balancing, scaling, and monitoring your inference infrastructure.

This means your internal neocloud doesn’t need to start with “we’ll build orchestration, load-balancing, multi-tenant APIs, monitoring” — View provides those foundational elements out of the box.

Unified API Compatibility and Orchestration Layer

View incorporates a flexible orchestration model inspired by next-generation inference systems. Multiple backend inference engines, virtual front-end endpoints, automatic model distribution and synchronization, health-checks, and load-balancing are all included.

Do you need security controls to ensure someone doesn't mistakenly adjust context window size on a completion request? Parameter pinning allows you to lock down model usage to a specific configuration.

Can't afford to mistakenly send a completion request related to or containing GDPR-sensitive data to an inference node that is in the wrong country? Tag-based load-balancing and node selection ensure the right inference endpoint is used to keep you compliance.

This enables your internal neocloud to:

• Support multiple model frameworks transparently (e.g., Llama-3, Claude, fine-tuned custom models)

• Maintain multi-tenant isolation (different teams get different endpoints and policies)

• Handle health, failover, routing, load-balancing, and utilization automatically

• Ensure proper use of models and the hardware from which they are being served

• Keep jobs running only on machines appropriate for the task at hand.

You get cross-model flexibility, scalability, failover, and integrated security controls without fragmenting your stack.

Cost Allocation, Monitoring, and Chargeback Readiness

While most neocloud builders must design telemetry and chargeback systems from scratch, View includes usage tracking and analytics out of the box. Because model inference is abstracted as internal API endpoints, your teams can monitor:

• Token throughput per conversation, business unit, and tenant

• Remaining allocations

• Cost and usage forecasts

• SLA and latency metrics

This dramatically shortens the 12-month software build cycle and allows your finance and operations teams to manage cost predictability early in deployment.

Future-Proofing and Rapid Model Iteration

The AI landscape changes monthly — new model architectures, inference engines, and optimization strategies. View’s architecture allows pluggable model backends, so you can introduce or retire model types without breaking front-end APIs.

That means you’re not forced to rebuild every time CUDA, PyTorch, or a serving framework changes — the abstraction layer absorbs most of that churn.

Enterprise-Ready High Availability and Operability

High-density neoclouds demand uptime, failover, model synchronization, and monitoring. View’s platform supports high-availability inference clusters through:

• Continuous health-checks

• Automatic failover and recovery

• Load-balanced distribution of requests across healthy nodes

This gives you the operator toolkit normally requiring months of engineering.

Simplified Multi-Tenant Isolation and Workload Heterogeneity

Supporting multiple tenant teams with distinct needs is one of the toughest challenges in internal neoclouds. View solves this via:

• Separate front-end endpoints per tenant/business unit

• Parameter enforcement to prevent resource abuse

• Label-based backend routing (assign GPUs by tenant or workload type)

• Logical workload isolation on shared infrastructure

Result: predictable performance, controlled governance, and minimal noisy-neighbor interference.

Comparison: Building from Scratch vs. Using View

Conclusion

Building an internal neocloud is one of the most strategic yet complex initiatives an enterprise can undertake — but with View, it becomes achievable in weeks rather than years.

By abstracting away the orchestration, compliance, chargeback, and infrastructure complexity, View allows you to focus on business outcomes — not backend plumbing.

In short: View helps accelerate your GPU cluster or dense CPU cluster into an enterprise-ready neocloud.