AI isn’t going anywhere, obviously. As models get bigger and more power-hungry, demand for AI compute is only accelerating. That’s why we need neoclouds — cheaper, faster AI infrastructure.
Today, businesses across various industries are adopting AI to enhance their products, services, and everyday operations. So, organizations are actively seeking reliable infrastructure that can handle growing workloads efficiently while still maintaining cost control.
This growing demand is showing up in the fast rise of the neocloud market. New data from Synergy Research Group shows that neocloud revenues reached $9 billion in Q4 2025, up 223% year-over-year. For the full year, revenues passed $25 billion.
At PixelPlex, we help companies build practical and scalable AI solutions through our AI development services. We created this article to explain the latest changes in the neocloud market, why this growth matters, and how businesses can prepare for the future of AI.
What is a neocloud?
Simply put, a neocloud is a new generation of cloud infrastructure designed for high-performance AI and GPU-intensive workloads. Neoclouds typically offer GPU-as-a-service (GPUaaS), bare-metal AI infrastructure, and highly specialized networking environments that prioritize performance, flexibility, and faster deployment for AI companies.
Here are several factors that contributed to the rise of neoclouds and explain why more and more businesses are turning to them for their AI infrastructure needs:
- The AI boom dramatically increased demand for computing power as companies raced to build and deploy AI models, chatbots, and other machine learning applications.
- GPU shortages made it harder and more expensive to access the powerful hardware needed for AI workloads. This created an opportunity for providers focused on supplying these resources.
- Traditional cloud providers like AWS, Google Cloud, and Azure weren’t always the best fit for AI companies. While they offer a wide range of services, GPU capacity can be expensive, limited, or difficult to secure at scale.
- Growing demand for AI-focused infrastructure led companies to seek cloud platforms built specifically for AI, with features like dedicated GPUs, faster networking, lower latency, and better performance for training and running models.
So what exactly do neocloud companies have to offer? One of their biggest advantages is faster access to GPU resources. Because a neocloud is designed with AI and machine learning in mind, it can deliver better performance and efficiency while often reducing infrastructure costs.
These platforms also provide greater flexibility when configuring and scaling compute clusters. This makes it easier for startups and AI-native companies to grow quickly.
In addition, their high-speed networking infrastructure supports distributed AI training. This capability allows large models to be trained more efficiently across multiple GPUs.
Neocloud vs traditional clouds: what’s the difference?
Not all cloud infrastructure is built the same way. While traditional cloud providers, neoclouds, and private GPU clusters can all support AI workloads, they differ significantly in cost, flexibility, performance, and ease of use.
We’ve prepared a table that highlights the main differences:
| Feature | Neocloud | Hyperscalers | Private GPU clusters |
| Cost | Often more affordable for AI workloads | Can become expensive at scale | High upfront hardware investment |
| Scalability | Easy to scale up or down | Highly scalable | Limited by owned hardware |
| GPU access | Fast access to AI-focused GPU resources | GPU availability may be limited during periods of high demand | Full control over owned GPUs |
| Deployment speed | Fast setup and provisioning | Fast, but GPU allocation can take time | Slow due to hardware procurement and setup |
| Operational complexity | Low to moderate | Low | High as it requires in-house management |
| Vendor lock-in | Generally lower | Can be significant depending on services used | Minimal |
| AI performance | Optimized for AI training and inference | Good, but designed for many different workloads | Excellent when properly configured |
For organizations focused on AI, neoclouds provide a balance between the convenience of public cloud platforms and the performance of dedicated infrastructure. They offer quick access to powerful GPUs without the cost and complexity of building and maintaining a private cluster.
However, moving AI workloads to a new cloud environment isn’t always straightforward. Companies often need to migrate applications, data, and infrastructure while minimizing disruption to existing operations.
Our cloud migration services help organizations make this transition faster and more smoothly, whether they’re moving from a traditional cloud provider, consolidating environments, or adopting a neocloud platform for the first time.
With the right migration strategy, businesses can start benefiting from AI-optimized infrastructure sooner while also reducing risk and operational complexity.
What kind of businesses benefit from neoclouds?
Neoclouds are often associated with AI startups, but they can benefit organizations across many industries. The industries below are among those seeing the greatest value from this AI-focused cloud infrastructure.
| Industry | Typical AI workload | How a neocloud can help |
| Healthcare | Medical image analysis, diagnostics, drug discovery, patient data analysis | Provides the computing power needed to process large datasets and train complex AI models quickly |
| Fintech | Fraud detection, risk assessment, algorithmic trading, customer analytics | Supports real-time processing and large-scale model training while helping control infrastructure costs |
| Retail | Product recommendations, demand forecasting, inventory optimization, customer support chatbots | Enables fast AI model deployment and scaling during peak shopping periods |
| Media & entertainment | Content generation, video processing, recommendation engines, audience analytics | Delivers high-performance infrastructure for compute-intensive AI workloads and large media datasets |
| Manufacturing | Predictive maintenance, quality control, computer vision, supply chain optimization | Helps process sensor and production data efficiently while supporting AI-driven automation |
| SaaS companies | AI assistants, generative AI features, personalization engines, customer support automation | Provides flexible access to GPUs and scalable infrastructure needed to launch and grow AI-powered products |
As AI adoption continues to grow, more organizations are turning to neocloud providers to build, deploy, and scale their AI initiatives efficiently.
Neocloud architecture: what’s inside and how it works
A typical neocloud environment consists of several key components such as GPUs, compute clusters, high-speed networking, storage systems, orchestration tools, and monitoring systems. Below, we’ll take a closer look at each of these components.
| Component | What it is | What it’s used for |
| GPUs | High-performance accelerators (often NVIDIA H100, A100, or similar) | Run and train large AI models efficiently at scale |
| Compute clusters | Densely packed groups of GPU-enabled servers | Provide elastic, AI-ready compute capacity on demand |
| High-speed networking | Low-latency interconnects like InfiniBand or high-throughput Ethernet | Enable fast data transfer between GPUs for distributed training |
| Storage systems | High-throughput distributed storage (e.g., object or parallel file systems) | Store massive datasets and model checkpoints used in training |
| Orchestration tools | Cluster and workload managers (often Kubernetes-based) | Automate provisioning, scaling, and scheduling of AI workloads |
| Monitoring and management tools | Observability and resource tracking systems | Help optimize GPU usage, performance, and cost efficiency |
On top of this infrastructure sits the AI/ML software layer — the neocloud tech stack. It connects raw compute resources with the tools developers use to build and deploy models. This typically includes frameworks like PyTorch, TensorFlow, and JAX, containerization with Docker, orchestration with Kubernetes, and ML lifecycle tools such as Kubeflow and MLflow for training, tracking, and deployment workflows.
What makes neoclouds different is that the entire stack is designed with one main goal: making high-performance AI compute easy to access, scale, and use without the complexity of traditional cloud infrastructure.
Leading neocloud providers and how to choose one
Over the past few years, several neocloud providers have scaled from niche startups into major infrastructure businesses generating hundreds of millions — and in some cases billions — of dollars in annual revenue.
Today, the market is led by companies such as CoreWeave, Crusoe, Lambda, Nebius, and Core Scientific. While they all provide AI computing infrastructure, each focuses on different customer segments and use cases.
CoreWeave
CoreWeave is currently the largest public neocloud company.
In 2025, the company generated approximately $5 billion in revenue, with 168% year-over-year growth. During the same period, its infrastructure footprint expanded to more than 850 MW of active power across 43 data centers worldwide.
CoreWeave has become a preferred infrastructure partner for large AI companies and enterprises that require access to massive GPU clusters.
Crusoe
Crusoe has emerged as one of the fastest-growing AI infrastructure providers. In 2025, it achieved several major milestones. It delivered the first phase of its 1.2 GW Abilene Stargate campus in less than a year. It also doubled its workforce for the second consecutive year. In addition, it raised $1.375 billion in a Series E funding round at a valuation above $10 billion.
The company is heavily focused on building large-scale AI infrastructure and data center capacity.
Core Scientific
Core Scientific has expanded beyond its original cryptocurrency infrastructure business and is increasingly focused on AI hosting.
The company reported approximately $319 million in total revenue in 2025.
Its AI hosting segment grew rapidly:
- Quarterly AI colocation revenue increased from $8.5 million to $31.3 million
- Annual AI hosting revenue reached $65.4 million, compared to $24.4 million in 2024
Lambda
Lambda has built a strong reputation among startups, researchers, and machine learning teams.
In 2025, the company reached approximately $760 million in annualized revenue, representing 79% year-over-year growth.
Lambda’s focus on simplicity, developer experience, and easy access to GPU resources has made it particularly popular among smaller and mid-sized AI companies.
Nebius
Nebius has become one of the fastest-growing players in the neocloud market.
The company reported $529.8 million in revenue in 2025, representing 350% year-over-year growth. Much of this growth came from the expansion of its AI cloud infrastructure business.
Nebius continues to invest heavily in modern GPU capacity while targeting both startups and enterprise customers.
How to choose a neocloud provider
Although all major neocloud companies offer GPU infrastructure, their strengths differ significantly. The table below compares the leading providers across several key criteria.
| Provider | Main specialization | GPU availability | Enterprise focus | Geographic reach | Pricing positioning |
| CoreWeave | Large-scale AI training and enterprise GPU clusters | Extremely high | Very strong | Global | Premium |
| Crusoe | Hyperscale AI infrastructure and large AI campuses | Very high | Strong | International | Premium |
| Core Scientific | AI hosting and colocation services | Moderate to high | Strong | Primarily North America | Competitive |
| Lambda | AI model building, testing, and training | High | Medium | International | Cost-effective |
| Nebius | AI cloud infrastructure and scalable enterprise deployments | High | Strong | International | Mid-range |
So, the right choice depends on your company’s size, workload requirements, and budget.
CoreWeave is best suited for large enterprises and AI companies that need massive GPU clusters and global-scale infrastructure.
Crusoe is a strong option for organizations planning large AI deployments and long-term infrastructure investments.
Core Scientific may be attractive for businesses seeking AI hosting or colocation services rather than a full cloud platform.
Lambda is often a good fit for startups, research teams, and growing AI companies that prioritize ease of use and affordability.
Nebius offers a balanced combination of enterprise-grade infrastructure, scalability, and competitive pricing.
Neocloud and its implementation scenarios: where to start?
Neocloud adoption can start in different ways depending on business goals, existing infrastructure, and AI maturity. The most common implementation scenarios include:
- Launching new AI initiatives without investing in on-premises hardware. Teams can quickly access GPU resources and start building, training, and deploying AI models.
- Expanding existing AI environments when internal infrastructure can no longer meet growing compute demands.
- Supporting AI inference and production workloads with scalable resources that can adjust to changing demand.
- Creating dedicated environments for data science, experimentation, and model development while maintaining security and governance requirements.
- Building hybrid architectures that combine existing infrastructure with neocloud resources for greater flexibility and cost efficiency.
Like any technology transition, adoption may come with challenges. Organizations often face questions around migration planning, resource optimization, security, compliance, and ongoing operations. These challenges can be addressed through a phased implementation approach, clear governance policies, workload assessment, and expert operational support.
To maximize the value of neocloud, many organizations choose end-to-end management of their AI cloud infrastructure. Cloud managed services, for example, help reduce operational complexity while ensuring performance, security, and cost efficiency through:
- Infrastructure operations and administration
- Continuous monitoring and optimization
- Resource scaling and capacity management
- Incident response and issue resolution
- Governance and compliance support
- Security management and risk mitigation
- Cost control and usage optimization
With cloud managed services, teams can focus on AI innovation and business outcomes while cloud experts manage the underlying infrastructure.
What you can build with neocloud and how
With Neocloud, organizations can build a wide range of AI products and services, including:
- Generative AI applications and AI assistants
- Enterprise chatbots and customer support tools
- AI-powered search and knowledge management systems
- Recommendation engines
- Computer vision applications for image and video analysis
- Speech recognition and voice AI solutions
- Predictive analytics and forecasting tools
- Large language model (LLM) training and fine-tuning environments
Neocloud is also well suited for AI research, model experimentation, and production deployments. Teams can scale resources up or down as project requirements change, making it easier to support both development and real-world workloads.
Nowadays, many companies look beyond infrastructure and seek support with building complete AI solutions. This is where AI application development services play an important role. Experienced AI development teams help organizations design, develop, integrate, and deploy AI applications that align with business goals.
By combining neocloud infrastructure with AI development expertise, companies can accelerate time to market, reduce implementation risks, and turn AI ideas into practical business solutions.
The future of neocloud: what to expect?
The future of neocloud looks very promising as demand for AI infrastructure continues to grow. According to Synergy Research Group, neocloud providers are currently growing by more than 200% per year and are expected to generate nearly $400 billion in revenue by 2031, with an average annual growth rate of 58%.
While hyperscale cloud providers remain major players, neoclouds are gaining traction by focusing specifically on AI workloads. Companies such as CoreWeave have established themselves as key infrastructure providers, while organizations like OpenAI continue to shape the market through large-scale initiatives and investments in AI infrastructure.
For businesses, this means greater access to powerful AI computing resources, more deployment options, and improved cost efficiency.
All in all, businesses want AI capabilities, but managing GPU infrastructure can be complex, costly, and resource-intensive. PixelPlex helps organizations design, deploy, optimize, and operate AI infrastructure on neocloud platforms efficiently and securely.
Our expertise includes:
- Infrastructure consulting
- Cloud and workload migration
- AI platform engineering
- MLOps implementation
- Multi-cloud AI architecture
- Cloud security services
Whether you are launching a new AI initiative or scaling existing workloads, our team helps build a reliable, secure, and cost-effective foundation for AI growth.
