Focus on ML, not infrastructure
Inference and training anywhere – on-prem, edge and public cloud.
Get personalized strategies from our cloud native AI/ML experts
Trusted by leading companies
We delight IT operations, edge infrastructure, developers, and DevOps teams at these companies.
Six reasons
Why your AI/ML initiatives are slow to launch
Challenges faced by our customers before they transitioned to Platform9.
A lot of time is spent on infrastructure by data scientists and MLOps
Slow roll out of AI apps increases costs and decreases adoption
Operational tasks are consuming the time of scarce skilled resources
Initially, the cloud appeared perfect, but now its costs have skyrocketed
POC rollouts don’t meet executive’s expectations or timelines
Despite equipment shortage, infrastructure flexibility is unrealistic
Explore alternatives to public cloud for your Machine Learning workloads
Buy your own GPU
GPUs and accelerators run in commodity x86 hardware and can be bought today to run anywhere for a fraction of the cost of public cloud.
Lease or purchase your own servers
You can now rent or buy hardware suiting your financial models (OpEx/ CapEx), with amortization over 3, 5, or 7 years.
Consume managed data centers, private clouds, and colos
Data centers have modernized alongside public cloud, eliminating traditional rack, stack, and provision cycles while enabling consuming them as a service.
Watch the webinar to learn how to maximize infrastructure ROI for AI/ML workloads
Platform9 offloads the burden of running infrastructure anywhere
Focus on your data science and machine learning modeling and execution
Data pipeline, training & inference
Platform9’s agnostic approach allows you to choose your tools and run anywhere.
Platform9 Managed Kubernetes
Offload operational complexity while gaining portability and efficient execution of AI/ML workloads on Kubernetes.
Cloud, on-prem or edge
Run your workloads on any infrastructure, from bare metal in edge locations to the public cloud.
MLOps for streamlined app deployment
Deploy anywhere instantly
Create a cluster and instantly deploy your ML tools and models across public, private, and edge clouds.
Choose your own infrastructure
Choose the right cloud for your team, regardless of need or location, and leverage any workflow tool.
Minimize infrastructure burden
Focus on building and running ML workloads instead of managing infrastructure.
Get automated operations
Simplify operations with monitoring, upgrades, security patching, and more.
Resources for AI/ML infrastructure management
Why and how to run ML workloads on Kubernetes
Enhance Data Workflows with MetaFlow on Platform9 Managed Kubernetes
