Karine DilanyanDiscover what AI developers need from the cloud in 2025—from GPU performance to open-source flexibility and seamless Kubernetes integration.
In 2025, AI developers are more dependent on cloud infrastructure than ever before. The pace of innovation in machine learning, deep learning, and large-scale model training is accelerating, and developers need scalable, secure, and high-performance environments to build, test, and deploy their solutions. By 2026, organizations are projected to spend over $300 billion on AI systems, yet 83% of containerized resources remain underutilized, and vendor lock-in remains a top concern.
Clearly, something has to change in how we build cloud environments for AI. For AI teams, the cloud isn't just a tool. Let’s explore what developers expect from their cloud environments today.
High-Performance Infrastructure: Built for Modern AI Workloads
One of the most critical needs for AI developers is access to GPU-powered infrastructure that can handle compute-intensive workloads like model training, inferencing, and real-time data analysis.
Atmosphere delivers on this need with fully integrated GPU instances across all editions: Cloud, Hosted, and On-Premise. These GPU instances are managed through OpenStack Nova, offering robust scheduling and orchestration to ensure optimal performance. Combined with features like SR-IOV, DPDK, and ASAP2 for near-bare-metal networking, and high-performance block storage via Ceph or third-party backends, Atmosphere empowers developers with a unified high-performance computing environment.
Kubernetes + GPU: Containerized AI at Scale
AI developers rely on Kubernetes for orchestration of containerized workloads. In Atmosphere, Kubernetes clusters powered by OpenStack Magnum with a custom Cluster API driver support GPU-accelerated applications natively. These clusters expose GPU resources via device plugins, enable autoscaling and auto-healing, and ensure GPU workloads can be deployed securely within isolated networks.
Storage integration is also seamless: Kubernetes GPU workloads can access persistent data through native CSI drivers, making it easier to handle large datasets in production environments.
Developer Flexibility & MLOps Readiness
AI development environments are becoming increasingly complex, involving not only model training but full pipelines for data ingestion, experimentation, deployment, and monitoring. Atmosphere supports this flexibility by providing:
- Support for the latest GPU hardware and deep learning features (e.g., CUDA, Tensor Cores, FP16/INT8 precision)
- Compatibility with frameworks like TensorFlow, PyTorch, MXNet
- Multi-GPU configurations for distributed training
- Integration with tools that teams can deploy for MLOps workflows
With the ability to define custom instance flavors and integrate with diverse networking and storage setups, developers can tailor infrastructure to their exact workload requirements.
For a deeper look at how open-source technologies like OpenStack and Kubernetes shape modern AI infrastructure, check out our blog on Open-Source AI: How it Impacts Your Cloud Strategy.
Real-World Use Cases: From Research to Production
GPU instances in Atmosphere are actively powering transformative AI applications across a wide range of industries—bridging the gap between research and production environments.
Real-world use cases powered by Atmosphere’s GPU instances include:
- Deep learning and NLP model training
- Autonomous systems and real-time inferencing
- Healthcare imaging and molecular simulation
- Financial risk modeling and fraud detection
- 3D animation rendering and media transcoding
- Big data analytics and graph processing
Organizations are leveraging Atmosphere’s high-performance computing capabilities to accelerate the development and deployment of machine learning models. From deep neural networks processing massive datasets to computer vision systems analyzing real-time video feeds, these GPU-backed workloads are achieving new levels of speed and accuracy.
In financial services, Atmosphere supports fraud detection algorithms capable of processing millions of transactions per second in real time, while healthcare providers can use AI-driven medical imaging solutions to detect conditions with unprecedented precision and efficiency. Research institutions can use it to conduct groundbreaking work in climate modeling, drug discovery, and genomics, harnessing the platform’s scalable GPU infrastructure to move seamlessly from small-scale experiments to massive parallel workloads.
The autonomous vehicle industry is another major beneficiary—training and validating self-driving algorithms using GPU clusters that can process petabytes of sensor data, dramatically accelerating development cycles from months to weeks. Meanwhile, media and entertainment companies can render 3D animations and transcoding video at scale, reducing time-to-delivery while maintaining high fidelity.
Built on OpenStack, Atmosphere provides the flexibility to run everything from individual GPU instances for development and testing to full-scale multi-node clusters for production, all within a secure, compliant, and cost-optimized environment. This seamless scalability enables organizations to move from prototype to production without the usual infrastructure constraints, accelerating innovation and time-to-market for AI-powered solutions.
Whether it’s fueling recommendation engines for e-commerce, powering real-time language translation services, or enabling advanced robotics and automation, GPU instances in Atmosphere are delivering measurable business value and a competitive edge.
These applications are made possible by Atmosphere’s consistent performance, enterprise-grade security, and seamless scalability—available across Cloud, Hosted, and On-Premise deployment models.
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Enterprise-Grade Features AI Teams Need
Modern AI developers need more than just raw compute—they need a platform that supports operational excellence. Atmosphere offers:
- Live migration and high availability
- Private networking for data isolation
- Encryption at rest and in transit
- Per-minute billing in the Cloud edition
- Global data center deployment options
- Multi-tenancy for team and workload separation
Combined with professional services such as architecture planning, optimization, deployment, and training, Atmosphere helps organizations maximize their investment in AI infrastructure.
Conclusion
As AI continues to evolve, so do the demands of the developers behind it. In 2025, cloud platforms need to deliver performance, flexibility, security, and ease of use. Atmosphere stands out by blending open-source innovation with enterprise-grade capabilities, providing AI teams with everything they need to push boundaries and accelerate results.
From seamless GPU support and Kubernetes integration to scalable storage and professional services, Atmosphere is the AI developer’s cloud of choice.
Want to run your AI workloads without hitting GPU walls or vendor roadblocks? Atmosphere gives you the tools to build, train, and scale—on your terms. Get started with Atmosphere and deploy GPU-powered infrastructure that grows with you.