Building a successful AI cloud infrastructure demands far more than simply provisioning GPU resources. It requires the careful orchestration of multiple software and hardware layers, strategic optimization of network and storage technologies, and a relentless focus on ensuring every GPU minute delivers maximum computational value.
In this article, we'll explore the critical layers of an AI cloud stack. We’ll examine why each component is essential for empowering AI/ML engineers to efficiently train models and deploy inference workloads that meet demanding performance requirements and tight cost constraints. We'll reveal how DataCrunch innovates across every layer of the AI stack – from low-level hardware optimizations to high-level developer tools – to deliver exceptional performance through advanced network architectures, storage efficiency, and intelligent provisioning systems, ultimately achieving industry-leading cost efficiency.
Additionally, we'll demonstrate how to architect an AI cloud platform that champions workload portability and open standards, ensuring customers maintain complete freedom over their infrastructure choices without vendor lock-in.
TL;DR:
DataCrunch has built a comprehensive AI cloud infrastructure stack that goes beyond GPU provisioning. Our platform integrates and optimizes every layer – from low-level hardware and networking to high-level developer tools – to maximize GPU performance and utilization, and minimize costs. Key differentiators include instant provisioning, advanced network optimizations that minimize GPU idle time, flexible storage solutions that reduce startup times (by up to 50% compared to hyperscalers), and a commitment to workload portability through open standards (preventing vendor lock-in). Our stack offers everything from bare-metal clusters to fully managed inference endpoints, with optimizations like custom network fabric layers, dedicated hardware isolation to eliminate "noisy neighbor" problems, and dynamic pricing that adjusts twice a day based on demand. Built with a developer-first approach, DataCrunch enables AI teams to deploy workloads quickly through simple APIs and UIs while maintaining ISO 27001 certification, GDPR compliance, and sustainable data center practices.
The DataCrunch AI Cloud Stack
The diagram below introduces the DataCrunch stack and architecture for building an AI cloud. It should be mentioned that this diagram is a logical abstraction of the technology layers rather than a technical diagram of interconnections. It is constantly changing as we add additional software tools and managed software services, but generally communicates our approach to building an efficient AI cloud with broad capabilities for different AI workloads.
Key aspects of the stack are highlighted in the diagram above. Developer tools are in the top layers and are what users experience when interacting with the DataCrunch Cloud Platform. DataCrunch products are highlighted in Managed Software Services and GPU-based Products. And compute resources, including GPUs, network, and storage technologies, are at the foundational layer. Not to be missed is the provisioning layer, where many DataCrunch innovations are implemented to ensure instant access to products and services while optimizing utilization of core resources.
In the development of the DataCrunch Stack, our main principles included:
The developer-first approach: Engineers can’t waste time in meetings, talking to sales, submitting forms, or waiting for access. Instant, self-service access through a simple but powerful interface and API might seem easy on paper, but hard to deliver at scale.
Performance: Whether for training or inference, performance is not just a function of the latest GPU technology but optimizations all through the stack that include networking, storage, and software layers.
Efficiency: Delivering the best value GPU cloud is more than just the price per GPU hour. It requires optimal resource utilization so that the GPUs are not waiting for storage or the network, thereby minimizing GPU startup, model loading, and reloading times.
Portability: Interoperability between clouds, data centers, and clusters means more power and flexibility to AI builders, enabling them to choose the best options for each application and workload. Support for and use of popular open-source software such as PyTorch, TensorFlow, OpenStack, Kubernetes, and Docker containers gives DataCrunch customers the greatest flexibility.
"What makes DataCrunch different is our commitment to interoperability. Our goal is seamless onboarding: whether you’re deploying a Triton server, a custom Flask app, or a container originally built for another provider, it should just work. We build around emerging standards like vLLM, OpenAI-compatible APIs, and common deployment schemas to future-proof our stack and simplify migration paths. It’s freedom by design, not lock-in." – Nikolai Syrjälä, Head of Managed Services
Taken together, all the integrations, innovations, and optimizations in each layer of the DataCrunch AI Stack enable a world-class developer experience at affordable prices.
Integrating and Optimizing AI Stack Layers
Each layer of the DataCrunch stack provides an opportunity to develop, integrate, and optimize software and hardware components. We highlight below why it’s important and how DataCrunch applies its expertise in that area.
Developer Tools
With our developer-first approach, DataCrunch ensures every experience with the DataCrunch Cloud Platform is streamlined to get workloads running.
Cloud Interfaces - Dashboard and APIs: Engineers require a streamlined UI or an API for deploying and managing infrastructure and endpoints. The DataCrunch API and UI are designed for exposing and managing critical configuration details without human intervention.
Management & Observability: Effective control over infrastructure and clear visibility into operational status and performance are essential for maintaining high performance, availability, and cost efficiency. The Cloud Platform offers simple yet powerful tools and industry-standard monitoring solutions, such as Prometheus and Grafana, to track usage, costs, and system status. Internally, DataCrunch has implemented resource monitoring capabilities to minimize disruptions and speed recovery of any hardware failures.
Orchestration, Scheduling and Other Software Tools: Engineers need access to powerful tools for managing instances and clusters and a way to schedule their workloads. With built-in support for frameworks such as PyTorch, TensorFlow, Hivemind and OpenDiLoCo, and scheduling tools such as SLURM and Kubernetes, DataCrunch makes it easy to run AI workloads. In addition, the DataCrunch AI research team participates in and/or utilizes open source projects such as SGLang and TorchTitan to demonstrate enterprise scalability and to be able to advise and support DataCrunch customers. The entire DataCrunch infrastructure is Torch-native, either built on or integrated with the latest infrastructure.
GPU-based Products
DataCrunch offers products and services across a broad spectrum of AI infrastructure needs, from bare-metal clusters to fully managed and optimized inference endpoints, such as the FLUX model family by Black Forest Labs.
Instances: Fast access to a wide range of GPU price/performance points allows developers to move quickly without breaking the budget. DataCrunch streamlines the process by rapidly starting, stopping, and hibernating resources via the API or UI.
Instant Clusters: The setup and configuration processes of GPU clusters can be time-consuming, with many AI Neoclouds missing the essentials. DataCrunch, however, gives instant access to multi-node setups of GPU clusters without quotas and without the need to talk to sales.
Bare-metal Clusters: For specialized computing requirements such as custom software stacks or specific network and storage integration, DataCrunch customizes GPU clusters for customers. We rely on our team of AI and infrastructure experts to work with customers as a strategic partner to design and deploy compute resources tuned to their unique workload requirements.
Provisioning: Inefficient or slow provisioning can delay instance or cluster initiation times and hinder fast scaling up or scaling down, ultimately costing time and money. Efficient GPU provisioning requires a combination of people – a diverse infrastructure team – and software. The provisioning software of DataCrunch works behind the scenes to create and make available resources, such as Instant Clusters, and to optimize compute resources across instances, clusters, and customers. For example, internal tools for partitioning GPUs and the provisioning system are constantly being improved and expanded. The infrastructure team brings new GPUs into production, monitors and replaces failed hardware, and assists customers with troubleshooting issues with core resources.
Managed Software Services
Why spend time managing infrastructure when you can easily deploy a container or call an API for inference? DataCrunch goes beyond integration and software management by optimizing these layers for maximum performance.
Serverless Containers: Containers are an easy way to deploy AI workloads, but they need to be cost-effective and scale with demand. The DataCrunch container serving infrastructure has been pre-configured for performance and scaling. Storage management tools enable developers to quickly attach, detach, re-attach, and hibernate storage, resulting in faster GPU startup times and lower costs.
Managed Inference Endpoints: Optimized inference endpoints deliver the optimal performance and cost for teams that don’t have resources to manage model serving and scaling. DataCrunch has an AI team that continuously tests and deploys state-of-the-art models. Our AI team also engages in co-research with trusted industry partners to deliver deep, model-specific optimizations.
"At DataCrunch, our team operates like a mini AI startup within a GPU infrastructure company. We primarily focus on scalable, efficient inference while transferring that knowledge to the training regime, from pre-training to post-training. Everything we build is designed to be production-grade, transferable, and aligned with maximizing real-world application." – Antonio J. Dominguez, Head of AI
Beyond the aforementioned, we are actively developing a wide range of managed services that meet the needs of diverse AI teams across the model lifecycle. Stay tuned for forthcoming announcements and releases.
Data Centers
Transparency, trust, and sustainability are among our top priorities at DataCrunch. We’re open in communication, security practices, privacy protections, and responsible energy usage.
The Network: Slow networking can cause GPUs to wait for data on large model training or high-volume inference jobs. The DataCrunch team consists of experts on InfiniBand, RDMA, NCCL, and other networking technologies, continuously improving the network performance within and between GPU nodes, GPU clusters, and even data centers.
"One of our most impactful innovations has been developing a custom layer between our cloud and network fabric. This 'glue' allows us to build incredibly scalable networks supporting millions of tenants, utilizing technologies like BGP EVPN, which ultimately gives our customers immense flexibility. We’re constantly pushing the boundaries of what's possible, especially with cutting-edge technologies like Ultra Ethernet. We're actively exploring and building proofs of concept for advanced RDMA over Converged Ethernet (RoCE) and new network fabrics, always with the goal of ensuring our GPUs are utilized to their absolute maximum." – Marek Svensson, Principal Architect
Storage: Poor storage choices or misconfigurations can cause slow load times for model training, retraining, and restoring of training runs, wasting GPU cycles. Storage experts at DataCrunch help with storage selection, configuration, and optimization. We go beyond just adding storage to help you evaluate storage against your application design to optimize storage performance.
"A major focus for us is solving the 'noisy neighbor' problem. We recognized that sharing file systems on slower mediums could bottleneck performance, so we innovated by creating dedicated hardware isolation for tenants. This means each customer essentially gets their own slice of the system, completely eliminating performance interference and ensuring consistent, optimal speeds." – Marek Svensson, Principal Architect
Simli, a DataCrunch customer, experienced 30-50% faster GPU startup times – reduced from 5 minutes to under 2 minutes. They attributed this improvement largely to how DataCrunch handles disk objects, allowing their team to pre-load a lot of data and attach it efficiently to GPUs upon commission. In addition, DataCrunch makes it easy to detach and reattach disks and avoid preemption issues with the on-demand resources.
Compute and GPU nodes: Access to the latest GPU and server technology and expertise in configuration is a never-ending challenge. As an NVIDIA partner, DataCrunch gets the latest GPU models and quickly delivers a tuned production-grade product.
Summary
The AI adoption demands more than raw computational power – it requires a thoughtfully architected cloud infrastructure that maximizes every GPU hour while empowering engineers to move at the speed of innovation.
The DataCrunch Stack represents our commitment to solving the real challenges AI builders face: eliminating barriers to access, optimizing performance across every layer, and ensuring workload portability without compromise.
"We promote co-research initiatives with the most active and popular open-source projects like SGLang, tackling large-scale MoE model serving and PyTorch ecosystem: TorchTitan and torch.compiler. Everything we build is designed to be production-grade, transferable, and aligned with maximizing real-world application." – Antonio J. Dominguez, Head of AI
By integrating cutting-edge technologies, developing proprietary optimizations, and maintaining a relentless focus on developer experience, we've built an AI cloud that delivers on the promise of accessible, high-performance computing. From our instant provisioning systems that eliminate waiting times to our network innovations that ensure GPUs never sit idle, every component of the DataCrunch Stack works in concert to deliver exceptional value.
"We're actively exploring and building proofs of concept for advanced RDMA over Converged Ethernet (RoCE) and new network fabrics, always with the goal of ensuring our GPUs are utilized to their absolute maximum. It's about optimizing every layer, from custom cloud software integrations to innovative storage solutions, to deliver unprecedented performance for our customers." – Marek Svensson, Principal Architect
As AI workloads continue to evolve – from massive distributed training runs to latency-sensitive inference endpoints – the flexibility and efficiency of the underlying infrastructure becomes increasingly critical. The DataCrunch Stack isn't just built for today's AI challenges; it's designed to adapt and scale with the rapidly changing landscape of AI technology. Whether you're a startup training your first model or an enterprise deploying production inference at scale, our platform provides the foundation for AI innovation without the complexity, delays, or vendor lock-in that plague traditional hyperscalers.